LAND USE CLASSIFICATION WITH BACK PROPAGATION
NEURAL NETWORK AND MAXIMUM LIKELIHOOD METHOD
(Case Study of Ciliwung Watershed, West Java Province)
YOSS ANDREAS ARMAN
GRADUATE SCHOOL
STATEMENT
I, Yoss Andreas Arman, here by declare that the thesis title:
Landuse Classification with Back Propagation Neural Network and Maximum Likelihood Method: A Case Study of Ciliwung Watershed, West
Java Province, Indonesia
contains correct results from my own work and that it has not been published ever
before. All data sources and information used factual and clear methods in this
project, and has been examined by the advising committee and the external
examiner.
Bogor, August, 2006
ACKNOWLEDGEMENT
There are many people I should thank in regard to this work no doubt I will not be able to name them one by one. To these I can but beg forgiveness. I wish to thank the following:
1. My supervisor Dr. Ir. I.Wayan Astika, MSi and my co-supervisor Dr. Ir. Lilik Budi Prasetyo, MSc for their guidance, technical comment and constructive criticism through all months of my research.
2. Dr. Ir. Tania June MSc, Chairman of study program of MIT for her kindness and providing academic assistance. And also to MIT Staff Miss. Uma, Miss. Devy, Mr. Bambang, Pak Zen-Zen, Pak Zein, Mas Mulyadiana, Mas Hasan, and Mas Mulyadi.
3. Dr. Ir. Antonius Bambang Widjanarko, MSc as the external examiner of this thesis for the positive ideas and inputs
4. MIT lectures, Prof. Jacub Rais, Dr. Handoko, Dr. Lukman Azis, Dr. Bambang Sapto, Dr. Iwan Gunawan, Dr. Kaswadji, Dr. Azis, Dr. Kudang Seminar, Dr. Hartisari, Mr. Iwan Setyawan, Mr. Eddi Nugroho and all other IPB lectures who thought me very important knowledge for my future.
5. PPLH-IPB through Mr. Yudi Setiawan that has given me image data support. 6. My uncle Ir Fery Mayhendra for all support, especially for “The Spirit to
Fight”
7. I deeply appreciate the effort of MIT staff. I specially appreciate to MIT colleagues for giving me encouragement and supporting, Andes Jayarsa, MSc, Iksal Yanuasyah MSc and Mr Upank.
8. MIT students year 2002 and 2003 Mr. Deny, Mr. Asep, Mr. Bonie, Mr. Yahya, Mrs. Vevin, Mrs. Evie, Mr Epho, Mr. Albert, Mr. Putu, Mrs. Brilie and also Linda friend’s (Ona, Indah, Yuni, Adit, Teti and Fita) I really appreciate our togetherness and how we support each other to finish our study. And also to the All MIT student, who gave me support prepare this research. 9. My special gratitude is also extended to my lovely Mom “Femarlyn”, Dad
“Arman Yazid”, and Sisters “Reny Arlinda” and “Arsepta Andreani”, for their prayers, understanding, moral support, patience, encouragement, and everything.
10.My pink jasmine Linda Setyoningrum, thanks for your patience, support, love and understand me.
CURRICULUM VITAE
Yoss Andreas Arman was born in Pekanbaru, Riau Province, Indonesia at Maret 14rd, 1979. He finished elementary school from SD 1 PIUS Payakumbuh in 1991, afterwards he finished
junior high school from SMP Fidelis Payakumbuh in 1994 and
in 1997 he finished senior high school from SMU Negeri 2 Payakumbuh. He
received his undergraduate diploma from Forest Management Department,
Faculty of Forestry, and Bogor Agricultural University in 2002.
In the year of 2003, Yoss Andreas Arman is received as Graduate School
Student in Information Technology for Natural Resources Management, Bogor
Agricultural University. In 2006, he received his Master of Science degree in
Information Technology for Natural Resources Management from Bogor
Agricultural University. His thesis title was on “Landuse Classification with Back
ABSTRACT
YOSS ANDREAS ARMAN (2006). Land Use Classification with Back Propagation Neural Network and The Maximum Likelihood Method: A Case Study in Ciliwung Watershed, West Java, Indonesia. Under the supervision of I WAYAN ASTIKA and LILIK BUDI PRASETYO.
Ciliwung Watershed is located at West Java Province; this area ranges from 106°47'29” to 107°0'25" E and from 6°24'16" to 6°46'23"N, having hard relief with slope 15% – 30%. Ciliwung watershed area has 117 km in length and 347 km2, it is one of water catchments that suffering damage relatively serious from the upstream to the downstream. Nevertheless, in less than 10 years since 1985, the conservation area has changed into settlement area or cultivation area.
The objectives of the research are to apply back propagation neural network and the maximum likelihood classification method for land use classification and compare the performance of the two methods. A one periods of Landsat-7 ETM+ (December 22, 2001) images with the path/row 122/065 was used for classification in the Ciliwung Watershed.
This research compared parametric method (maximum likelihood) and non-parametric method (back propagation neural network) that occupied the same Landsat-7 ETM+ and the same training area. Six bands (band 1, 2, 3, 4, 5, 7) from Landsat Image were used as input data for both classification methods. In this case, band 6 and 8 on the image not utilized for this research because both of band have different resolution with the others. Before Landsat-7 ETM+ used for classification process, it was corrected geometrically, atmospherically, and topographically. The purpose of these corrections was to decrease the error that can occur during the making of training area and classification process afterward. Landuse was classified into 8 classes: tea garden, settlement, paddy field, grass, forest, farm, bush, and water body. The target of the training area was based on Ikonos image interpretation, instead of using field data. There were 4000 pixels used for whole class categories.
TABLE OF CONTENTS
STATEMENT... i
ACKNOWLEDGMENT ... ii
CURRICULUM VITAE... iii
ABSTRACT ... iv
TABLE OF CONTENTS... v
LIST OF FIGURE ... vii
LIST OF TABLE ... viii
LIST OF APPENDICES ... ix
1. INTRODUCTION... 1
1.1. Background ... 1
1.2. Objectives ... 3
1.3. Problem Statement ... 3
2. LITERATURE REVIEW... 5
2.1. Watershed Characteristic ... 5
2.1.1. Watershed Boundaries ... 5
2.1.2. Watershed Area... 5
2.2. The Impact of Land Use Change to Water Quality ... 6
2.3. Remote Sensing ... 6
2.3.1. Geometric Correction... 7
2.3.2. Topographic Correction ... 8
2.3.3. Remote Sensing Data ... 8
2.3.5. Landsat Thematic Mapper ... 9
2.3.6. Landsat-7 Enhance Thematic Mapper Plus (ETM+) ... 12
2.4. Classification... 13
2.4.1. Maximum Likelihood Classification Method ... 14
2.4.2. Back Propagation Neural Network Classification Method... 15
2.4.3. Classification Accuracy Assessment ... 21
3. MATERIAL AND METHODOLOGY ... 24
3.1 Time and Location ... 24
3.2 Data Source ... 24
3.3 Required Tools ... 26
3.4. Methodology ... 26
3.4.1. Insitu Measurement and Observation ... 28
3.4.2. Image Preprocessing ... 28
3.4.2.1. Geometric Correction... 28
3.4.4. The Method Comparison ... 39
4. RESULT AND DISCUSSION... 40
4.1. Digital Image Pre-Processing... 40
4.1.1. Atmospheric Correction ... 40
4.1.2. Geometric Correction ... 41
4.1.3. Topographic Correction ... 44
4.2 Digital Image Processing ... 49
4.2.1. Image Classification... 49
4.2.1.1. Classification Image with Maximum Likelihood ... 50
4.2.1.2. Classification Image with B.P.Neural Network... 52
4.3 Classification Accuracy Assessment ... 59
4.4. Comparison Neural Network and Maximum Likelihood Method... 73
5. CONCLUSION AND RECOMMENDATIONS ... 76
5.1 Conclusion ... 76
5.2 Recommendation ... 76
LIST OF FIGURES
No Caption Page
Figure 2.1 Electromagnetic Spectrum... 9
Figure 2.2 Back Propagation Neural Network... 16
Figure 3.1 Interest Area of Research ... 25
Figure 3.2 Flow Chart of General Methodology ... 27
Figure 3.3 Flow Chart of Image Preprocessing... 32
Figure 3.4 The Model of Back Propagation Neural Network Method... 38
Figure 4.1 Rectification of Landsat 7 ETM + Imagery Path/row 122/065... 42
Figure 4.2 Subset of Remotely Sensed Data to Focus Study Area ... 44
Figure 4.3 Digital Elevation Model... 45
Figure 4.4 Model of Topographic Correction ... 46
Figure 4.5 Histogram before Topographic Correction ... 47
Figure 4.6 Histogram after Topographic Correction ... 48
Figure 4.7 Landsat 7 ETM+ 2001 before and after Corrected ... 48
Figure 4.8 Training Area for Classification... 50
Figure 4.9 The Classification Result of Ciliwung Watershed Using Maximum Likelihood Classification Method ... 50
Figure 4.10 Creates Data for Neural Network ... 51
Figure 4.11 Training Data for Neural Network ... 54
Figure 4.12 Classify Process for Neural Network ... 54
Figure 4.13 The Classification Result of Ciliwung Watershed Using Back Propagation Neural Network Classification Method ... 58
Figure 4.14 Data Vector as Reference ... 62
Figure 4.15 Vector Cutting of Classification Result Using Back Propagation Neural Network... 63
Figure 4.16 Vector Cutting of Classification Result Using Maximum Likelihood... 64
Figure 4.17 Ommission Error for Back Propagation Neural Network Method.... 71
Figure 4.18 Ommission Error for Maximum Likelihood Method ... 72
LIST OF TABLE
No Caption Page
Table 2.1 The Characteristics of Landsat Thematic Mapper (TM)... 10
Table 2.2 The Characteristics of Landsat 7 ETM + ... 12
Table 2.3 The Characteristics Sensor of Landsat 7 ETM + ... 12
Table 2.4 Example of Confusion Matrix (Error Matrix)... 21
Table 3.1 The Example of Input (X) And Output (Y) ... 34
Table 3.2 The Example of Data Training... 36
Table 4.1 Comparing The DN Value Before And After Performed Histogram Adjustment Of Image 2003... 41
Table 4.2 The Geographical Coordinates of Ciliwung Watershed ... 43
Table 4.3 Minnaert Coefficient (k) Per Band... 47
Table 4.4 Overall Accuracy for Back Propagation Neural Network Method... 56
Table 4.5 The Producer’s Accuracy and User’s Accuracy for Back Propagation and Maximum Likelihood Classification Methods ... 65
Table 4.6 The Confusion Matrix for Back Propagation Neural Network with 4000 sample and 1000 Iterations... 66
Table 4.7 The Confusion Matrix For Maximum Likelihood Classification Method ... 67
LIST OF APPENDICES
Appendix Caption Page
Appendix 1.Metadata of Imageries (Landsat 7 ETM+) ... 80
Appendix 2.Neural Network Report with some samples and iterations ... 83
Appendix 3.Accuracy Assessment for Back Propagation Neural Network... 92
Research Title : Land use Classification with Back Propagation Neural Network and Maximum Likelihood Method (Case Study of Ciliwung Watershed, West Java Province)
Name : Yoss Andreas Arman
Student ID : G.051024011
Study Program : Master of Science in Information Technology for Natural Resource Management
Approved by,
Advisory Board
Dr. Ir. I. Wayan Astika, M.Si Dr. Ir. Lilik Budi Prasetyo, M.Sc
Supervisor Co-Supervisor
Endorsed by,
Program Coordinator Dean of the Graduate School
Dr. Ir. Tania June, M.Sc Dr. Ir. Khairil A. Notodiputro, M.Sc
I. INTRODUCTION
1.1 Background
Water is a source of live for human being that has dynamic
characteristic that flows from higher land to the lower land. The annihilation
of higher land will cause the declining of water infiltration area and as a
consequence, water will flow directly to the land surface and might cause land
erosion and flooding. Flood occurrence that can be interpreted as rainwater
excess, which flows from its storage, is a natural phenomenon where rainwater
cannot be accommodated by land or surface storage in term of pool, lake or
body river and drainage channel. Ciliwung watershed is one of areas that has
been experiencing such damage.
Ciliwung watershed has a length of 117 km and an area of 347 km2, and is one of water catchments that suffers damage relatively serious from the
upstream to the downstream. The most damaging area is in the upstream in
Ciawi District and Cisarua District, where initially those areas categorized as
conservation land. Nevertheless, in less than 10 years since 1985 (Harijono,
2002), the conservation area has changed into settlement area or tea plantation.
This transformation occurred due to the weakness of local government control
in releasing permit for land usage. It is recorded that over 40% villas located
in upstream area do not have Building Constructing Permit (IMB), and
One of the difficulties of local government in decreasing land opening
in upstream area is due to the reference differentiation regarding total of
conservation area. Generally, there is a conflict of interest between
Agricultural Department and Forestry Department. Each department has their
own reference regarding the area that can be and cannot be utilized. In
assisting the government in rearranging the upstream area, needs to have the
same reference with high accuracy. GIS and remote sensing technology is one
of the solutions to have an accurate and efficient land use map. Along with the
improvement of GIS and remote sensing technology, the land changing that
occurs in Ciliwung watershed can be identified and compared from time to
time. Land use data and map of Ciliwung watershed can be derived from
remote sensing data using classification techniques.
This research will investigate classification of image interpretation by
using two methods, i.e. maximum likelihood method and back propagation
neural network method. It is expected from one of those two methods can give
fine or better accuracy in differentiating land cover located in Ciliwung
watershed. Based on the previous researches, back propagation has high level
of accuracy compared to maximum likelihood method. Koeshardono (1990),
stated that back propagation neural network has 4% better in accuracy than
maximum likelihood method, and so it is with the research that has been done
by Widyastuti (2000) in mangrove area, where the result shown that back
propagation neural network was more accurate than maximum likelihood
The researches above constitute the foundation of this study in order to
prove how both methods can properly classify Ciliwung watershed land use,
where it has been known for its heavy topographic characteristic.
1.2 Objectives
The main objectives of this research are:
1. To apply back propagation neural network and maximum likelihood
classification method for land use classification and compare the
performance of the two methods.
2. To produce a map of land use in the Ciliwung Watershed using the
best result of the two methods.
1.3 Problem Statement
As mention earlier, population around the watershed will give big pressure
to the land cover; this is not only for upstream area but for all area near the river.
The land cover will be changed to land use, for example forest to settlement, this
phenomenon can not be prevented because people need land for their living. For
proper planning exercise information on the above aspect should be made
available separately, however, the remote sensing digital data available from
satellite image were found mixed up at many points. Although the land cover
information from visual interpretation of the image can be extracted more
accurately, difficulty had been is experienced in extracting information on land
use like water conservation and others. The satellite based remote sensing has
aggregate information related to image feature of unknown objects. As a
consequence, the information result for land use provided by human interpreter is
less accurate and overlapping occurs in many places. For extraction from the
remote sensing image the research present a relatively more comprehensive and
scientific methodology using Back propagation neural network model. This
II. LITERATURE REVIEW
2.1. Watershed Characteristics 2.1.1. Watershed Boundaries
The area upon which water falls, and the network through which it travels to an outlet is referred to as a drainage system. The flow of water
through a drainage system is a only a subset of what is commonly referred to as the hydrologic cycle, which also includes precipitation,
evapotranspiration, and groundwater (Seyhan, 1977).
A drainage basin is an area that drains water and other substances to a common outlet as concentrated drainage. Other common terms for a
drainage basin are watershed, basin, catchments, or contributing area. This area is normally defined as the total area flowing to a given outlet, or pour
point. An outlet or pour point is the point at which water flows out of an area. This is usually the lowest point along the boundary of the drainage basin. The boundary between two basins is referred to as a drainage divide
or watershed boundary (Ilyas, 1985).
2.1.2. Watershed area
Watershed is a land region which is limited by natural boundaries includes topography functioning to accommodate, keep and draining water accepted go to closest river system and then accumulating basin or lake or
everlasting with minimum damage influent of this management will reflect at floods treats, river sediment. All of those will influence various activities and life sectors in the down stream.
2.2 The Impact of Landuse Change to Water Quality
On the watershed area, there are some land use, such as forest,
plantation, and agriculture of dry farming, rice field, settlement, fisheries, industry and soon Manan (1992). The quality of water from the water territorial can be influenced by land use in there. The contamination of
substance can degrade the water quality in part of river, especially from domestic waste, industrial disposal, activity of mining and waste from
agriculture activity. A contamination that happen by land usage activity in upstream will give influenced in downstream.
2.3 Remote Sensing
Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a
device that is not in contact with the object, area, or phenomenon under investigation (Lillesand and Kiefer, 2000). The advance of other support technologies, especially in the related fields of electronic computing and the
microchip, has begun to exercise what will doubtless grow to be a profound influence upon the design of what are often called ‘in situ’ sensor network,
and through them upon the types of questions we may hope to solve though the analysis and interpretation of such conventional data. Remote Sensing can be defined as the science of observation from a distance. Thus it is
immersed in, or at least touch, the objects of the observation and measurement (Barett and Curtis, 1982).
Remote sensing is the technique of collecting information from a
distance. By convention, “from distance” is generally considered to be large relative to what a person can reach out and touch, hundreds of feet, hundred
of miles, or more. Remote sensing techniques are used intensively to gather measurements, satellite-based system can now measure phenomena that change continuously over time and cover large, often inaccessible areas
(Aronoff, 1991).
Remote sensing systems, deployed on satellite, provide a repetitive and
consistent view of the earth that is invaluable to monitoring the earth system and the effect of human activities on the earth (Schowengerdt, 1997).
The major spectral regions used for earth remote sensing are shown in this
table. These particular spectral regions are of interest because they contain relatively transparent atmospheric “window”, through which (barring clouds
in the non-microwave regions) the ground can be seen from above, and because there are effective radiation detector in these regions.
2.3.1. Geometric Correction
Geometric correction that most often used to make digital remote sensor data truly useful is geometric rectification. Geometric rectification is
which the geometry of an image area is made planimetric (Haralicck, 1973 in Jensen, 1986).
2.3.2 Topographic Corrections
Besides geometric and radiometric error there are topographic mistakes caused by illumination differences and angle of earth surface
(Smith and Brown, 1997). An area hit by a lot of sunshine will be seen brightness and on the contrary, although both of places have some cover type.
According to Jansa (1988), satellite image interpretation influenced by shadow effect caused by relief of earth surface. Some factors have an
effect in topographic error, such as Sun Azimuth angle, elevation, slope and aspect of earth surface.
2.3.3 Remote Sensing Data
Each cover type on the earth has a specific characteristic to absorb, reflected and transmitted electromagnetic energy. Distinguished the fact
between an object and others on the earth’s surface and then can be using in the remote sensing technologies.
Remote sensing satellite record reflected and emitted radiant flux
from earth surface object or materials. Difference reflected and emitted each material are wavelength’s. The difference was used to know and interpreted
2.3.4 Landsat Thematic Mapper (TM)
Landsat TM is a generation earth resources satellite, and combines reasonable spatial resolution (cell size of 30 meters by 30 meters) with the
reasonable range of spectral band (7 bands in visible and near, short and mid-infrared wavelengths) Four detectors for the thermal-IR band provide
four scan lines on each active scan. The TM sensor has a spatial resolution of 30 meters for bands 1 through 5, and band 7, and a spatial resolution of 120 meters for band 6 (Lillesand,1994). The thematic mapper spectral bands
of Landsat - TM is shown in Table 2.1.
The Figure 2.1 below illustrates where in the EM spectrum the TM
sensor can 'see'. The rectangles depict the bandwidth recorded within that region of the spectrum.
Figure 2.1. Electromagnetic Spectrum (Lillesand and Kiefer, 2000) Landsat TM image is useful for image interpretation for much wider
Landsat data are being used to support a wide range of applications in such areas as global change research, agriculture, forestry, geology, resources management, geography, mapping, water quality, and
oceanography. Landsat data have potential applications for monitoring the conditions of the earth’s land surface. The landsat TM archive has over
300,000 scenes with a data volume of over 50 terabytes (USGS, 1999). Table 2.1 The Characteristics of Landsat Thematic Mapper (TM) Spectral Bands
(Lillesand and Kiefer, 2000).
Band Spectral Resolution (Nominal Spectral Location µm) & Principal Application
1 0.45 - 0.52
(Blue)
Provide increased penetration of water bodies, as well as supporting analyses of land use, soil, and vegetation characteristics. The shorter-wavelength cutoff is just below the peak transmittance of clear water, while the upper-wavelength cutoff is the limit of blue chlorophyll absorption for healthy green vegetation. Atmospheric scattering and absorption substantially influenced wavelengths below 0.45 µm
2 0.52 - 0.60
(Green)
This band spans the region between the blue and red chlorophyll absorption bands and therefore corresponds to the green reflectance of healthy vegetation.
3 0.63 - 0.69
(Red)
This is the red chlorophyll absorption band of healthy green vegetation discrimination. It is also useful for soil-boundary delineations. This band may exhibit more contrast than band 1 and 2 because of the reduced effect of atmospheric attenuation. The 0.69 µm cutoff is significant because it represents the beginning of a spectral region from 0.68 to 0.75 µm, where vegetation reflectance crossover take place that can reduce the accuracy of vegetation investigations.
4 0.76 - 1.90
(Reflective Infrared)
The lower cutoff for this band was placed above 0.75 µm. this band is especially responsive to the amount of vegetation biomass present in a scene. It is useful for crop identification and emphasizes soil/ crop and land/ water contrasts.
5 1.55 - 1.75
(Mid Infrared)
Band
Spectral Resolution (µm) &
Nominal Spectral Location Principal Application
6 10.04 - 12.5
(Thermal infrared)
This band measures the amount of infrared radiant flux emitted from surfaces. The apparent temperature is a function of the emissivities and the true or kinetic temperature of the surface. It is useful for locating geothermal activity, thermal inertia mapping for geologic investigations, vegetation classification, vegetation stress analysis, and soil moisture studies. The sensor often captures unique information on differences in topographic aspect in mountainous areas.
7 2.08 - 2.35
(Mid-infrared)
This is an important band for the discrimination of geologic rock formations. It has been shown to be particularly effective in identifying zones of hydrothermal alteration in rock.
2.3.5 Landsat-7 Enhance Thematic Mapper Plus (ETM+)
Landsat 7 was officially integrated into NASA’s Earth Observing System (EOS) in 1994. It was launched on April 15, 1999 from Vandenburg
Air Force base, CA, using a Delta-II Expendable launch vehicle into a sun-synchronous orbit.
Landsat 7 provides a unique suite of high-resolution observations of the terrestrial environment. It was designed to achieve three main objectives (NASA, 1999):
Maintain data continuity by providing data that are consistent in terms of geometry, spatial resolution, calibration, coverage
characteristics, and spectral characteristics with previous landsat data;
and to expand the use of such data for global-change research and commercial purposes.
Landsat 7 is a three-axis stabilized platform carrying single
nadir-pointing instrument, keeping the instrument pointed toward earth to within 0.05 degrees, and contains an improved Thematic Mapper sensor called the
Enhanced Thematic Mapper (ETM+). The ETM+ instrument is a derivate of the Landsat 4 and 5 Thematic Mapper sensors (Jensen, 2000).
Table 2.2. The Characteristics of Landsat 7 ETM+ (Lillesand And Kiefer, 2000).
Band Spectral Resolution (µm) Spatial Resolution (m) at Nadir
1 0.450 - 0.515 30 x 30
2 0.525 - 0.605 30 x 30
3 0.630 - 0.690 30 x 30
4 0.750 - 0.900 30 x 30
5 1.55 - 1.75 30 x 30
6 10.40 - 12.50 60 x 60
7 2.08 - 2.35 30 x 30
8 (pan) 0.52 - 0.90 15 x 15
Table 2.3. The Characteristics Sensor of Landsat 7 ETM+ (Lillesand And Kiefer, 2000).
Sensor Technology Scanning Mirror Spectrometer
Swath Width 185 km (FOV=15
o )
Off-track viewing No
Data Rate 250 images per day @ 31,450 km2
Revisit 16 days
Orbit and Inclination
705 km, sun-synchronous Inclination= 98.2o
Landsat 7 has a 378-gigabit solid-state recorder that can hold 42 minutes of sensor data and 29 hours of housekeeping telemetry data. This is necessary because the ETM+ obtains 150 megabits of
data each second. Landsat data production facilities have had to be capable of handling very large quantities of data (Barett and Curtis,
1982). 2.4 Classification
Classification is a process in which all the pixels in an image that have
similar spectral signatures are identified. It is typically used to process satellite imagery. There are two methods of classification: supervised and
unsupervised classification. The difference between supervised and unsupervised classification is that, for supervised classification, we specify the classes we are interested in the supervised classification as is utility sorts
out the image into these classes. On the other hand, for unsupervised classification, we can choose to give the utility class means to start with, or
we can ask to make them up entirely its own (Anonymous, 1998).
Remote sensed image data sampled from satellite includes specific problems such as large image data size and difficulty in extracting
characteristics of the image data. In the past, a statistical method is used without considering these problems. This method is based on an assumption
The back-propagation network (or back-propagation preceptor) is probably the most well known and widely used of neural network systems. The term “back-propagation” refers to the training method by which the
connection weights of the network are adjusted. The back-propagation network is a type of multilayer feed-forward network (Anonymous, 1996).
2.4.1 Maximum Likelihood Classification Method
The maximum likelihood classification method is the most commonly used supervised classification method for remote sensing
image data. This developed in the following statistically acceptable manner (Richard, 1986)
The maximum likelihood classifier quantitatively evaluates both the variance and co-variance of the category spectral response patterns when classifying an unknown pixel (Lillesand, 1994).
The maximum likelihood classification method, as shown in the following equation, includes a pixel “X” (value vector)
classified into “k” class if the probability of “x” in “k” class is the highest compared to the other class (Koeshardono, 1999)
X Æ k if Lk(X) = max {L1(X), L2(X), L3(X),…….Lk(X)}
Where:
Lk(X) = probability X to be “k” class
Lk
=
⎭ ⎬ ⎫ ⎩ ⎨ ⎧− 2 2 2 1 exp 2 1 ) 2 ( 1 k n d Ck
π ... (1)
2 k
d = Mahalanobis distance
2 k
X = pixel vector value (X = (x1,x2,x3,…..xn)t) k = class (k= 1,2,3…,k)
N = number of data band Mk = mean vector for class “k”
∑
= = m i ki k X m M 1 1... (2)
ki
X = pixel vector value for training data “i” in “k” class M = the number of training data for “k” class
k
C = covariant matrix “k” class
(
)(
∑
− −= 1 2
M X M X m
Ck ki ki
)
... (3)k
C = determinant matrix Ck
1
− k
C = inverse matrix Ck
t
Z = transposed matrix Z
2.4.2 Back propagation neural network Neural Network Classification Method
2.4.2.1 Back propagation neural network Model
Back propagation neural network is a learning algorithm
using multilayer feed forward network with a different transfer function in artificial neuron. Back propagation neural network learning algorithm is usually implemented in multi (three or more)
layer neural network. This learning algorithm accommodates both real and integer numbers for input and output. The general
The back propagation neural network arises from the method in which correction are made to the weights (Patterson, 1996). During the learning phase, input patterns are presented to
the network in some sequences. Each during pattern is propagated forward layer by layer until an output pattern is computed. The
computed output is then compared to a desired value or target output and error value is determined. The errors are used as input to feedback connection from which adjustments are made to the
synaptic weights layer by layer in back direction. The backward linkages are used for the learning phase, whereas the forward
connection are used for both the learning and operational phase. The processes inside the feed-forward back propagation neural network algorithm network are described in the Figure 2.2
X 0 X 1 X i h 0 h 1 h 2 h 3 h j Y1 vjk wij Y2
Xi : input variable of node i in input layer
hj : output of node j in hidden layer Yk : output of node k in output layer
(predicted value of node k) wij : weights connecting node i in
input layer and node j in hidden layer
vjk : weights connecting node j in hidden layer and node k in output layer. OUTPUT LAYER INPUT LAYER HIDDEN LAYER
2.4.2.2Back propagation neural network Learning Algorithm In the back propagation neural network learning algorithm’ the training instance set for the network must be presented many
times in order for the interconnection weight between the neurons to settle into a state for correct classification of input pattern. The
basic learning algorithm of back propagation neural network modifies the interconnection weight on the network so that signal error is minimized (closer to zero).
Back propagation neural network learning algorithm can be done step by step as follows (Patterson, 1996):
1) Initialization:
a. Normalization of input data Xi and Target tk in form of (0,
1) range
b. Randomize of weight wij and vjk using (-1,1) value. c. Initialize of threshold unit activation, x0 = 1 and h0 = 1.
2) Active of input layer-hidden layer units with:
... (4)
∑
+
=
wijxie
hj
1
1
3) Active of hidden layer-output layer units with:
... (5)
∑
+
=
vjkhje
yk
1
1
computing error of the nodes in output layer, denotes δk then adjusts weight vjk:
δk = yk (1-tk)(tk-yk) ... (6)
vjk = v jk + β.δk.hj... (7) Where: β is constant of momentum
tk is prediction value
5) This process to compute errors of the nodes in input layer,
denoted τk, and adjust weight vjk
τk = hj (1-hj) ∑k δk. vjk... (8)
wij = wij + β . τk . xi ... (9) 6) Move to the next training set, and repeat step 2. Learning
process is stopped if yk are close enough to tk. The termination can be based on the error E. for instance, learning process is
stopped when E<0.0001
2
) (
5 .
0 p p
kE = p tk −yk
∀
∑
... (10)
tkp = target value of p-th data from training set node k yp = prediction value of p-th data from training set node k The trained neural network can be used to predict target (t) by
inputting values from input layer (x)
2.4.2.3 Back Propagation Neural Network Classification Method.
During the last decade, researchers have already done
satellite. There are many researchers who use neural network in their research (Sadly, 1998):
a. Chang (1994) used a dynamic learning neural network for
remote sensing applications and obtained a good result and concluded that neural network is a feasible classifier for a very
large volume image.
b. Similar study was also carried out by Bischof (1992) and
showed that the neural network outperforms the maximum
likelihood method.
c. Yoshida (1994) proposed a neural network classification
method for remotely sensed data analysis in order to improve neighborhood relations between pixels and decrease the error probability for pattern classification and obtained a more
realistic and noiseless result compared to a conventional statistic method.
Back propagation neural network neural network usually includes an input layer, one or several hidden layers and an output layers as the biological neural network does. Three types of layers include
(Fu, 1994):
¾ The input layer: The nodes, which encode the instance
¾ The hidden layer: The nodes, which are not directly
observable and hence hidden. They provide nonlinearities for the network.
¾ The Output layer: The nodes which encode possible
concept (or value) to be assigned to the instance under
consideration. For example, each input unit represents a class of object.
The processing neurons in each layer are called processing
units or simply known as units and neurons. The units of the input layer, hidden layers and output layer can also be called
respectively input units, hidden units and output units, respectively. The direction of information transmission is from the input layer to the output layer. The neural network must be trained
in advance in order to make discriminate analysis with it. The purpose of the training is to adjust the association strength (or
coefficients of weights) between the neurons. The criteria of the training are to make the error between the computed output dependent vector and the known dependent vector of the trained
patterns. The process of the training is just to transmit backward the error to the network, adjust the weight among the units
The purpose of classification is to automatically categorize image pixels into classes based on land cover type. Back propagation neural network is a supervised classification method
that was developed by Rumelhard et al. in 1986. Back propagation neural network is consists of many layers of neurons such as input
layer, hidden layer (may be more than layer) and output layers. Each layer consists of many neurons.
2.4.3 Classification Accuracy Assessment
A classification is not complete until its accuracy is assessed (Lillesand, 1994). Classification accuracy assessment is necessary to
compare the image classification with the reference data.
The reference data can be obtained from (Koeshardono, 1999):
o Ground truth obtained from the research area
o Test site obtained from visual interpretation satellite image o Aerial photography
Accuracy is determined empirically by selecting a sample pixel from the thematic map checking their labels against classes determined from reference data.
Table 2.4. Example of Confusion Matrix (Lillesand, 1994).
Training Set Data (Know Cover Types)* Classification Data
W S F U C H
Row Total User’s Acc (%)
W S F U C H 480 0 0 0 0 0 0 52 0 16 0 0 5 0 313 0 0 38 0 20 40 126 38 24 0 0 0 0 342 60 0 0 0 0 79 359 485 72 353 142 459 481 99 72 87 89 74 75
Column Total 480 68 356 248 402 438 1992
Producer’s Accuracy (%) 100 76 88 51 85 82
Overall Accuracy = (480 + 52 + 313 +126 + 342)/1992 = 84 %
*……. W = Water S = Sand F = Forest U = Urban C = Corn H = Hay
The confusion matrix is prepared by classifying the training set pixels as shown in Table 2.4.The known category types of pixels
used for training are listed versus the categories chosen by the classifier. From this information, classification errors of omission
and commission can be studied. In an ideal case, all non-diagonal elements of the contingency table would be zero, indicating no misclassification. Commission errors are represented by
non-diagonal elements of the tables where pixels are classified into a category to which they do not actually belong. Omission errors
represent the reserve type of situation.
Producer’s accuracy result from dividing the number of correctly classified pixels in each category by the number of
that category (row total). Overall accuracy is computed by dividing the total number of correctly classified pixels
The Kappa statistic is a measure of the difference between the actual agreement between reference data and an automated classifier and the chance agreement between the reference data and
III. METHODOLOGY
3.1. Time and Location
The study was conducted from September 2005 to July 2006. Case
study area was Ciliwung watershed of West Java Province, which covers an
area of 130,000 ha, and is located between 106°47'29" East to 107°0'25"
East in and between 6°24'16" North to 6°46'23" North (Figure 3.1).
3.2. Data source
There are three kinds of data that were used in this study:
1. Remote sensing data
The satellite imagery used in this study was Landsat satellite imagery
with series data (Landsat-7 ETM+ image, acquisition date of December
22nd, 2001). It is surrounding with the path/row 122/065, spatial
resolution is 30 x 30 meters (except band is 120 meters by 120 meters),
and temporal resolution is 16 days with 7 bands in 2001 acquired from
BTIC. (BIOTROP - Training and Information Center)
2. Topographic Map
The topographic map in vector data
(1209-124,1209-141,1209-142,1209-144,1209-231) were used in this study, and they were
published by Bakosurtanal that have a scale of 1:50.000
3. DEM (Digital Elevation Model) data
The data were provided by PPLH (Environmental Research Centre)
IPB.
The data were obtained from check field survey using Global
Positioning System (GPS). Field survey has purposed to take some of
ground control point. The type of GPS is Garmin III plus and this can
[image:36.595.129.489.215.690.2]3.3. Required Tools
The tools that used in this study consist of equipment, hardware and
software as follow:
1. Equipment consists of Global Positioning System (GPS) Garmin III Plus
2. Hardware consists of personal computer (PC) Pentium 4 2.2 GHz 512 MB RAM, and color printer
3. Software was used are ER Mapper Image Processing Software, ESRI ArcView GIS Software
3.4. Methodology
There are four stages of methodology were done in this study namely 1)
Collecting and pre-processing image, 2) Classification step by using
maximum likelihood and neural network, 3) Analysis and continued by 4)
Final result
Max. Like Back Pro NN
No
Yes
Test Side: -Kappa Stat. -Visual Int.
- Cropping area of interest - Geometric correct - Topographic correct
References: -Ikonos
-Thematic Map
Preprocessing data
Classification
Processing data -Image enhance -Band composite Comparison Corrected Imagery Geometric and Topographic Corrected Image Classification with NN Image Classification with M.Likelihood Image Classification with M. Likelihood
Image Classification with NN
Analysis Classification Step
Collecting Data and Pre-Processing Image Raw Image (Landsat TM) Good Accuracy Collecting Data: -Image -Digital Map -Thematic map -Contour Map
[image:38.595.74.551.99.693.2]3.4.1In situ Measurement and Observation
The field survey (in situ) data is very important when working with
remote sensing images. The field data will be used to compare ground feature
and corresponding image feature. Collect Ground Control Points (GCP’s),
which are determined from the topographic maps.
3.4.2Image Preprocessing
Preprocessing the remotely sensed data prior to analyzing it is to remove
some errors. Radiometric, geometric and topographic errors are the most
common types of error encountered in remotely sensed imagery.
3.4.2.1 Geometric Correction
This research used polynomial rectification. It is usually used to
transform an image from a RAW (or unknown) projection into a known
projection. This is known as georeferencing or geocoding. Prominent
features called control points are located on the RAW image and matched
either with points on an image in the desired projection, or with coordinates
typed in (possibly from a map).
The basic operation in geometric rectification:
1. Collecting GCP (Ground Control Point)
Ideally x’ would equal to xorig and y’ would equal to yorig but some
distortion usually happened in GCPs collection. Asimple way to measure
such distortion is by computing the RMS error for each control point
using the equation below:
where:
xorig and yorig = the original row and column coordinates of the GCP in
the image.
x’ and y’ = the computed or estimated coordinate in the original image.
RMS threshold is 0.5 pixel or RMSerror < 0.5 and less then 0.5 pixel is
expected.
2. Tranformation
It uses equation solution to transform the entire image. Polynomial
(Control Point) is used as type of rectification.
3. Resampling
Resampling is used to determine the pixel values to fill into the output
matrix from the original matrix (Lillesand and Kiefer, 2000).
Resampling technique chosen is nearest neighbor. The value for a pixel
in the output image could be assigned simply on the basis of the value of
closest pixel in the transformed image.
3.4.2.2 Topographic Correction
Topographic factor is one of contributors in any image
interpretation errors, and therefore, this effect must be removed. One of the
methods which have been used to correct to topographic effect is Minnaert
Function (Smith et.al., 1980), this representing correction between the
vector of vertical inclination and angle of the sun (the sun zenith angle).
image resolution), solar azimuth value of landsat metadata and solar
elevation acquired from landsat metadata also.
2. After all the data were obtained, then a model was established in order
to be able in perform image topographic correction. The model will
initially calculate cos i (cosine from effective incidence angle) by
using all raster data and value acquired from metadata. Complete
equation can be seen as follows:
cos i = cos e cos z +sin e sin z cos (фs – фn )... (12)
where:
e = surface normal zenith angle or terrain slope
z = solar zenith angle /solar elevation angle (get from metadata)
фs = solar azimuth angle (get form metadata)
фn = aspect (get from DEM)
3. After effective incidence angle value was obtained, and the model
will perform calculation in order to acquire Minnaert Constant value.
Minnaert constant value was obtained by using a linear equation, that
is: y = k x + b ...(13)
where: k = Minnaert constant
y = log (L cos e)...(14)
x = log (cos i cos e) ...(15)
b = logLn ...(16)
The model generated different value of Minnaert constant for each
4. After Minnaert constant and effective incident angle value were
obtained, the model will perform topographic correction for each layer
(per band) of Landsat-7 ETM+ 2001. The formula is:
L cos e = Ln cosk i cosk e...(17)
where:
L = Digital number (DN) before corrected
Ln = Digital number (DN) after corrected
cos i = cossine from effective incidence angle (Equation 12)
cos e = surface normal zenith angle/slope of the terrain surface
k = Minnaert constant (Equation 13).
5. After the calculation using Minnaert function is finished, the model
Atmospheric Correction
Ground Control Point (GCPs) Polynomial
Rectification Geometric Correction
Topographic Correction Raw Imagery
[image:43.595.154.491.86.380.2]Corrected Imagery Resampling
Figure 3.3. Flow Chart of Image Preprocessing (Lillesand and Kiefer, 2000)
3.4.3 Classification Method
Image cutting is done in order to extract the study area, because the
original image covers a very large area, while the study areas are only part of the
image. Cropping image is needed because the study area is limited only in
Ciliwung watershed area. They was classified into 8 classes such as forest (C1),
tea garden (C2), bush (C3), paddy field (C4), farm (C5), settlement (C6), grass
(C7) and water body (C8).
1. Processing image data: image enhancement and band composite.
The goal of image enhancement is to improve the visual interpretability of
an image by increasing the apparent distinction between features in the
scene (Lillesand, 1994). This research used linear enhancement and 542
bands composite was used for training area only. This combination is
because band 5 is an indication of vegetation moisture content, band 4 is
useful for determining vegetation types and band 2 is designed to measure
green reflectance peak of vegetation for vegetation discrimination and
vigor assessment. With this combination, more information for identifying
the land cover was obtained.
2. Classification: this research is to compare back propagation neural
network classification method with a maximum likelihood classification
method. The Artificial Neural Network (ANN) has the architecture parallel
with some node and unit. The neural network used in this study has three
layers representing the input, the hidden and the output. Every unit from
one node to another can be linked by weight. The steps of back
propagation neural network classification methods are :
a) Input layer: Landsat 7 ETM+ as the input units have eight bands or channels. They are divided to six thematic spectral
(band 1 to 5, and 7), one thermal spectral (band 6), and one
panchromatic spectral (band 8). In this study is only use
thematic spectral or six band, that are band 1 (wavelength
0.45-0.52 µm), band 2 (0.45-0.52-0.60 µm), band 3 (0.63-0.69 µm), band
4 (0.76-0.90 µm), 5 (1.55-1.75 µm), and band 7 (2.08-2.35 µm).
Besides spectral values, Landsat 7 ETM+ also consists of pixel
which have pattern is rectangular and number of pixel is
six digital numbers depend on image channel. The input are
band 1 (X1), band 2 (X2), band 3 (X3), band 4 (X4), band 5
(X5), and band 7 (X6). Set of input (X) can show in tabular
[image:45.595.108.515.234.490.2]form as follows Table 3.1
Table 3.1. The Example of Input (X) and Output (Y) Pattern of C1, C2, C3, C4, C5, C6, C7 and C8.
No X1 X2 X3 X4 X5 X6 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 MARK
1 65 100 45 77 78 167 1 0 0 0 0 0 0 0 C1 2 78 24 24 58 79 210 0 1 0 0 0 0 0 0 C2
3 111 5 78 69 75 222 0 0 0 1 0 0 0 0 C4
4 175 10 44 36 76 212 0 0 1 0 0 0 0 0 C3
5 220 78 125 25 74 213 0 0 0 0 0 1 0 0 C6
10 80 69 210 14 45 214 0 0 0 0 1 0 0 0 C5
….
100 123 15 124 74 46 210 0 0 0 0 0 0 1 0 C7 125 145 14 156 85 42 200 0 0 0 0 0 0 0 1 C8
150 160 45 178 96 43 214 0 1 0 0 0 0 0 0 C2
….
800 170 56 192 65 123 100 1 0 0 0 0 0 0 0 C1 900 224 78 200 145 111 98 0 0 1 0 0 0 0 0 C3
…
1000 210 120 200 225 122 99 0 0 0 1 0 0 0 0 C4
Note: X1- X6 = Band 1- Band7 and C1 = Forest, C2 = Tea Plantation, C3 = Bush, C4 = Rice
Field, C5 = Farm, C6 = Settlement, and C7 = Waterbody
b) Hidden layer: The training phase is the most important aspect in neural network modeling because the weights and the
network characteristics is defined to be used later on other
others datasets. In this case 6 nodes for processing element of inputs were used to define eight possible output-training
patterns (i.e. landuse classes). These eight output patterns
correspond to the six inputs to generate the relationship in the
learning is inclusive of supervised, which determines the output
from the input by using the training set (can see in Table 3.2
Training Set). The data for training is the training area that will
get also from Landsat 7 ETM+ image after cutting area of
interest, secondary information and fields check. The training
area cutting from Landsat image depends upon the classes that
will classify. Kind of data from training area is range of digital
number per-pixel. For example, training area for forest has
range of digital number 65-130 for band 1, 67-135 for band 2
and the others band without more distinguish of range. If from
input pixel 1 and band 1 has digital number 100, hence pixel 1
band 1 is forest (pattern formed is 1). The data training can be
shown in Table 3.1 So in this case number of data to be used as
data training is 25% of total data. The best combination to
obtain the best result is the learning rate 0, 9 and the
momentum 0, 1 (Sadly, 1998). Before calculated in hidden
layer was done, the input should be initialization by:
¾ Normalization of input data (Xi) and Target tk (will
explain next) in form of range (0,1).
¾ Randomize of weight wij and vjk using (0,1) value.
¾ Initialize of threshold unit activation, x0=1 and h0=1
node the hidden layer to node the output layer has a weight; it
will get by actives of hidden layer to output layer units
equations. The equations can show in Equation (7).To
minimize error of weight, vjk must be adjusted. This process is
called ‘backward’ step. The equation to minimize error can
show in (8) and (10). If result from hidden layer given big
error, than iteration will be done from input. Iteration for this
research had been done almost 100 until 1000. If not enough,
[image:47.595.46.543.351.583.2]hence iteration will continue until the error can not change
Table 3.2.The Example of Data Training.
Forest (Class 1) …. Bush (Class 7)
B1 B2 B3 B4 B5 B7 …. B1 B2 B3 B4 B5 B7
No
62-125 65-126 64-124 66-127 66-126 68-129 ….. 200-240 190-250 220-245 139-150 200-230 180-200
1 62 67 65 67 68 78 230 195 230 139 215 190
2
70 126 111 112 69 90 215 200 231 145 225 200
3 100 100 115 124 70 88 235 204 232 150 230 185
:
100 124 120 78 125 85 125 225 230 240 148 205 186
:
250 90 126 124 127 123 129 …..
212 250 241 142 210 190
Note: B1 = Band 1, B2 = Band 2, B3 = Band3, B4 = Band 4, B5 = Band 5, B7 = Band 7
c) Output layer: The output (y) is eight classes (forest, tea garden, bush, paddy field, farm, settlement, water body and
grass). Set of input (X) and output (Y) then composed in
tabular form as follows Table 3.2
training area for each class which has the accurate digital
number. The target was get from training area. The trained
neural network can be used to predict target (t) by inputting
values from input layer (x)
e) Validation: validation is attempted to see the level of accuracy and efficiency of a system. Validation step is conducted to test
the relationship between prediction and target output. It is done
by checking the performance by calculating the accuracy
between observed and predicted values of data sets:
% 100 (%) x
T Y
Accuracy = ... (18)
Where, Y is the number of valid prediction and T is the number
of data to be predicted. Number of validation dataset will be
used 10 % of total dataset. The intention of validation is to
identify the value of classification result with the number of
training set for each class
The model of artificial neural network by using back
Input units Hidden layers Output Layer X1 X3 X2 X4 X5 X6 Landsat 7 etm+ Band 1 Band 2 Band 4 Band 7 Band 5 Band 3 X0 h0 h2 h1 h3 h4 y1 y2 y8 y5 y3 y7 y4 y6 F T B A R S W U h22 h21 h23 h24
Figure 3.4. The Model of Back Propagation Method
The steps of maximum likelihood classification
method are (Lillesand, 1994):
a. Training Stage: The analyst identifies representative training areas and develops a numerical description of the spectral of
each land cover type of interest in the scene
b. Classification Stage: Each pixel in the image data set categorized into the land cover class it most closely resembles.
If the pixel is insufficiently similar to any training data set, it is
usually labeled “unknown”
c. Output Stage: After the entire data set has been categorized, the result is presented in the output. Being digital in character,
3.3.4 The Method Comparison
In order to indicate the accuracy and reliability of the
landuse classification with back propagation neural network and
maximum likelihood, some types of analysis was used. There are:
Confusion matrix calculation, Lillesand (1994):
a. Overall accuracy: ∑ number of pixels in one class/grand
total of pixels
b. Producer’s accuracy: numbers of pixels in one class/
column total
c. User’s accuracy: numbers of pixels in one class/ row total
d. Kappa statistic: (observed accuracy-chance agreement)/
(1-chance agreement)
IV. RESULT AND DISCUSSION
4.1 Digital Image Pre-Processing 4.1.1Atmospheric Correction
In this study, atmospheric correction was performed on image 2001.
Histogram adjustment was used to remove the atmospheric bias that may occur in
the imagery in each band. The procedure made use of the Scattergram Cut-off
Atmospheric Correction Technique. By principal, this technique makes use of the
cut-off information that is determined from the bivariate scattergram.
A line of best fit drawn through the distribution between the two bands
will intercept the shorter wavelength axis at a DN approximating the scattered
component. Each cut-off value was used to readjust the minimum value in the
histogram. This was accomplished by employing the simple formula “INPUT1 –
cut-off value” on the formula editor of the software.
The result of histogram adjustment for radiometric correction is presented
in Table 4.1 and the histogram performance before and after radiometric
correction can be seen in Figure 4.1. DN value of original data has increased the minimum brightness all band. Generally, after performed histogram adjustment
Table 4.1. Comparing the DN Value Before and After Performed Histogram Adjustment of Image 2001.
Band/Channel DN Value of Original Data Histogram Adjustment
1 56 – 255 0 – 199
2 40 – 255 0 – 215
3 28 – 255 0 – 227
4 29 – 211 0 – 182
5 23 – 255 0 – 232
7 13 – 255 0 – 242
The lowest digital number of 2001 imagery was zero, even when no
objects in the scene truly have a reflectance of zero.
4.1.2 Geometric Correction
To determine control point in geometric correction and image rectification
easier, it is needed to make a composite color image. The purpose of making
composite color image is to find general illustration about data that will be
processed further that is with manipulate visual appearance of the earth’s surface
object. Composite color image that made is band combination-542. By using this
composite color, the objects in the image to determining GCP’s is easier to be
recognized and should be taken from exact and no change objects like small
islands, intersection of road or river.
In this research, to conform the pixel grids and remove any geometric
distortions in the Landsat imagery, the Landsat-7 ETM+ image, December 22,
2001 has been registered to the UTM Zone South 48, WGS 84 coordinate system.
the Landsat-7 ETM + were registered to Then Topographic map utilizing similar
In selecting the GCP’s have to be careful, not only one should check that
the object selected on the two images is similar, but also must be sure that the two
have the same location on each image. The result of corrected image and
collection of GCP’s can be seen on Figure 4.1.
a) Row Landsat imagery with Ground Control Point
b) Rectified imagery: Registered to the UTM zone 48, WGS 84 coordinate system
Figure. 4.1. Rectification Of Landsat 7 ETM + Imagery Path/Row 122/065, Image Date: December 22, 2001 With Composite Band 542
Even the smallest amount of RMS error has the potential to introduce
some degradation to the change detection accuracy. This degradation has the
potential to affect the boundaries of the landuse classes incorporated in the study
as spurious differences can be detected because the land surface properties at
wrong locations are evaluated instead of the real changes at the same location
between one time and another. The spatial resolution of the imagery becomes an
Landsat-7 ETM+ image, December 22, 2001 were registered to an RMS
error of less than 0.5 pixels or exactly the average of RMS error is 0.19 pixels
(Appendix 4). This Root Mean’s Square error was deemed acceptable at the time
of registration.
After geometric correction have already finished, it is better to subset of
image that cover study area, because in some cases, Landsat 7 ETM + scenes are
much larger than a research study area. In these instances it is beneficial to reduce
the size of the image file to include only the area of interest (Figure 4.2). This not
only eliminates the extraneous data in the file, but it speeds up processing due to
the smaller amount of data to process. This is important when utilizing
multi-band data such as Landsat 7 ETM + imagery. This reduction of data is known as
subset (cropping). This process has been done for the both images. The image cut
by using vector data from vector of west java which was produced by
Bakorsurtanal. Table 4.2 shows the geographical coordinates of the cropping area.
Table 4.2. The Geographical Coordinates of Ciliwung Watershed.
Geo-position Top Left Bottom Right
Latitude 6o24’24.20”N 6o46’06.91”S
Longitude 104o47’41.37”E 107o00’01.59”E
Easting 421305.00E 460875.00E
a) Landsat imagery path/row 122/065 b) Ciliwung Watershed
Figure 4.2. Subset of Remotely Sensed Data to Focus Study Area.
4.1.3 Topographic Correction
Topographic correction refers to the compensation of the different
solar illuminations due to the irregular shape of the terrain. This effect causes a
high variation in the reflectance response for similar land cover types: shaded
areas show less than expected reflectance, whereas in sunny areas the effect is the
opposite. Therefore, the process of topographic normalization may be critical in
areas of rough terrain, as a preliminary step to the spectral and for
multi-temporal digital classification of landuse types.
Ciliwung has diverse topography type starting from 0% slope to 55%
slope. In Landsat-7 ETM+ image, the area where the slope is over 15% (such as
Cisarua and Puncak) will have many shadows around it, especially forest area. In
order to avoid misinterpretation during acquiring training area, then a process
called topographic correction should be performed first. In classification, process
In this study, topographic correction has been done by using Minnaert should represent the real spectral value for each desirable class. Topographic
correction process in this research used Minnaert method where the calculation
process done by using a model. Before the calculation process, all required data
should be ready, which are DEM data, solar elevation angle value and azimuth
angle value from Landsat-7 ETM+ metadata (acquisition date December 22,
2001). The picture of DEM used in this process can be seen in this Figure 4.3:
[image:56.595.145.497.233.637.2]Low (81 m) High (3000 m)
Entering data
Image corrected
[image:57.842.21.766.46.486.2]Minnaert constant
The value of solar azimuth angle for image 2001 is 122.4 while value of solar
elevation angle is 56.6, Minnaert Constants can be seen in Table 4.3 as follow:
Table 4.3. Minnaert Constant (k) per Band
Band Minnaert Constant
1 -0.2644394098037027 2 -0.2371046780587166 3 -0.2144840868697183 4 -0.2724998585326885 5 -0.2780220865837141 7 -0.2099449197308662
The value of the Minnaert constant’s lies between 0 and 1. It is use to describe the
roughness of the surface. Then Minnaert constant’s entered into the model and all
of digital number (DN) per band which found on image will be corrected so that
obtained an image which in the topography has been corrected. Figure 4.5 shows
the histogram performance of images as follow:
Figure 4.6. Histogram after Topographic Correction
Histogram values indicate that there are a few value changes after topographic
correction but this are not to obvious visually. The comparison was also done
visually, that is no significant different, as shown in Figure 4.7:
After Before
Figure 4.7. Landsat 7 ETM+ 2001 before and after Corrected
From the figure above, there is no change in image before and after topographic
before and after topographic correction was performed. In general, topographic
correction is very assistive to reduce interpretation error of image classification.
4.2. Digital Image Processing 4.2.1 Image Classification
In this research, the classification method used supervised
classification, namely back propagation neural network and maximum
likelihood. Both methods used Landsat 7 ETM + that had been
geometrically corrected, radiometrically corrected and topographically
corrected, and also used mode resample by nearest neighbourhood. The
training areas were determined based on the characteristics of spectral,
Ikonos image, and thematic map (Peta Penutupan Lahan, produced by
PPLH (Enviromental Research Centre) IPB in collaboration with Faculty
of Forestry-IPB. The pixels, which have the same spectral or visual
characteristic, were categorized in the same group.
Based on the Government Regulation (PP No. 47/1997) concerning
regional land use planning, Bogor-Puncak-Cianjur (BOPUNJUR) region is
categorized as a specific area that needs special management and land use
planning. According Government Regulation above, training area divided
into 8 classes:
1. Tea Garden
2. Settlement 3. Paddy Field
4. Grass
5. Forest
Training area was taken by using Ikonos image 2001. Training area was not taken
directly by ground check because the image that used too old (2001).
4.2.1.1 Classification Image with Maximum Likelihood Method
This method commonly used for image classification, this method
is spectral value approach which found on training area or in the other
word classification image done by supervised. Training area used for
classification process was obtained from vector data. Vector data can be
acquired by using Ikonos imagery as the source of training area. In order
to reduce the mistake in training area setting, Ikonos imagery, which is
used as the source should be corrected first by using Ground Control Point
so that it will have the same position of Landsat imagery
[image:61.595.172.488.409.733.2]Training area for every class is shown in Figure 4.8
4.2.1.1Classification Image with Back Propagation Neural Network
Classification by using Back Propagation Neural Network model
composed on several steps:
1. Creating Data
This Function creates and initiates a new back propagation neural network
segment for back propagation neural network processing. This back
propagation neural network segment can be trained to recognize classes
with the back propagation neural network train program. In this process
there are 2 types of data, which is spatial data in the form of Landsat 7
ETM + image and Training data area which is in form of vector. Data
vector has made equal precisely with data training for method maximum
likelihood of the size pixel 30 x 30 meters. The model has three
parameters, which are input units, number of hidden units and number of
samples. After repe
