List of Tables
3. THE THEORY OF IMAGE PROCESSING AND ANALYSIS
3.4 IMAGE ENHANCEMENT AND TRANSFORMATION
shifted from date to date and hence the pixels in these targets did not represent the same ground location on all the images. Apart from this, their application of the method was as outlined in Eckhardt et al. (1990).
Munyati (2000), in a study of changes occurring in parts of the Kafue River floodplain, also used the empirical scene normalisation method of Eckhardt et al. (1990). A number of target areas were identified on the images, with irrigation reservoirs representing the wet target areas and unvegetated bare soil the dry targets. Munyati (2000) reported R2 values of above 0.99 when normalising Landsat Thematic Mapper images to the reference image (also a Landsat TM image) but obtained lower R2 values (e.g. 0.91) when relating Landsat MSS images to the reference image. This was most likely due to differences in the spectral and spatial resolution between the multispectral and thematic mapper scanners. In another change-detection study, Roberts et al. (1999) used the pseudoinvariant feature technique developed by Schott et al. (1988). A variation they employed was to first convert the reference image to surface reflectance by using signatures from a library of reflectance spectra. An advantage of this variation was that subject images normalised relative to this reference image would then also be calibrated in terms of surface reflectance.
The various relative radiometric correction techniques described above, whether using pseudoinvariant targets, dry/wet targets or bright/dark targets all had the same aim, namely to normalise a series of target images relative to a selected reference image so that all the images appeared to have been obtained under the same atmospheric conditions and with the same sensor. These techniques have been successfully applied in a number of studies and have the advantage of being relatively easy to implement. In addition, if the reference image is converted to values of surface reflectance, then relative normalisation of the target images will convert them to surface reflectance too.
techniques are usually applied after images have been corrected for geometric and atmospheric effects but before classification is attempted (Lillesand & Kiefer, 2000).
A common image enhancement technique is to improve the visual display of satellite images by manipulating the contrast so that features on the image are accentuated. This is done by increasing the range of the image pixel values so that they are displayed using the full range of colours or shades available on the computer screen. This results in images with features that are much clearer and more easily discernible than on the original image.
Band ratioing is another common image enhancement technique in which pixel values in one band are divided by the corresponding pixels values in another band. This technique is useful for removing the effects of scene illumination and topography and also for highlighting subtle variations occurring in images (Schowengerdt, 1997; Lillesand &
Kiefer, 2000). Surface features that have similar reflectances in one part of the spectrum might have different reflectances at other wavelengths. These differences are highlighted in ratioed images.
Band ratioing forms the basis of many vegetation indices, which rely on the ratio of the red to the near-infrared bands of an image. The red and near-infrared regions of the spectrum are particularly useful for vegetation studies because of the highly distinctive reflectance characteristics of vegetation in these spectral regions. Chlorophyll is a strong absorber of radiation in the red part of the spectrum, leading to low reflectance values of pixels that contain green vegetation. The opposite occurs in the near-infrared where radiation is reflected and is not absorbed by chlorophyll (Chen et al, 1999; Eastman, 2001). The result is that healthy green vegetation has a low reflectance in the red part of the spectrum and high reflectance in the near-infrared. The simplest vegetation index is the Ratio Vegetation Index (RVI), which simply divides the near-infrared band by the red band. Higher values of RVI represent greater amounts of vegetation, low values represent little or no vegetation.
A problem with the RVI is that it is not linear and it is also susceptible to errors arising from division by zero (Eastman, 2001). As a result of this, numerous other vegetation indices have been developed, the most popular of which is the Normalised Difference Vegetation Index (NDVI) (Myneni & Asrar, 1994; Gao, 1996; Eastman, 2001):
(MR - RED) NDVI=
(NIR + RED)
The linear scale produced by the NDVI ranges between -1 and +1, with positive values representing vegetation and values below zero showing areas with no vegetation. The NDVI is a good indicator of biomass (e.g. Gao, 1996) and is also sensitive to variations in precipitation that occur during the seasonal growth cycles of vegetation (Lunetta, 1999).
Other vegetation indices include:
• the Transformed Vegetation Index (TVI), which modifies the NDVI to provide values with a normal distribution
• the Soil Adjusted Vegetation Index (SAVI), which minimises the effect of soil on the index
• the Atmospherically Resistant Vegetation Index (ARVI). which corrects for atmospheric effects
Another important class of transformation techniques performs linear rotations of the data axes in spectral space. Perhaps the best known of these transformations is the Principal Components Analysis (PCA), which produces a series of output images that are uncorrelated with one another and which contain decreasing amounts of information.
Spectral data from different bands in satellite images are often highly correlated, especially in the visible regions of the spectrum. This can be seen in Figure 3.1a, which shows the high correlation between bands 1 and 2 of a Landsat image. The reasons for this correlation are many, including factors such as overlap between the sensor bands, the effects of topographic shading, and the low reflectance of vegetation throughout the visible spectrum (Schowengerdt, 1997). The PCA removes the correlation in the original spectral data by defining new axes (components) in such a way as to minimise the amount of correlation between components. The origin of these new axes is situated at the mean of the data points and, by defining each successive axis as being orthogonal to all the preceding component axes, the data along the component axes are decorrelated (Lillesand & Kiefer, 2000). This can be seen in Figure 3.1b which shows principal component axes 1 and 2 plotted against spectral data from bands 1 and 2 of a Landsat image. The data plotted along the first principal component contains the most variance, with each successive component containing a decreasing percentage of variance or information (Lunetta, 1999).
(a) (b)
20
10
Correlation coefficient = 0.93
20
5 Band 1
10
• Component
M" Axis 1
Component Axis 2
10
Figure 3.1: Landsat Thematic Mapper data showing (a) the high correlation between bands 1 and 2, and (b) the first two principal components for the same dataset.
Applying a PCA to a multispectral satellite image reduces data redundancy while at the same time preserving all the information present in the original image. A disadvantage of PCA is that it is a data dependent technique, meaning that principal components from different images cannot be compared directly (Schowengerdt, 1997).