When conducting studies such as this, it is important to ensure that the correct image classification is carried out. Image classification is performed with the objective to, “automatically categorise all pixels in an image into land cover classes or themes,” (Lillesand et al., 2008: 545). Image processing software allows for two types of classification processes; supervised and unsupervised classification (Lillesand et al., 2008; Richards and Richards, 1999; Sahu, 2007). In the following quote, Lillesand (2008) describes the clear distinction between these two classification processes:
“The fundamental difference between these techniques is that supervised classification involves a training step followed by a classification step. In the unsupervised approach the
image data are first classified by aggregating them into the natural spectral groupings, or clusters, present in the scene. Then the image analyst determines the land cover identity of these spectral groups by comparing the classified image data to ground reference data,”
(Lillesand et al., 2008: 547).
Evident from the above mentioned quote, supervised classification permits the analyst or researcher to categorise the pixels according to their own specific descriptions of the relevant land cover types in the acquired scene (Lillesand et al., 2014; Richards and Richards, 1999; Sahu, 2007). The analyst’s prior knowledge of the study area is then utilised to successfully adapt the computer algorithms to the scene in question (Lillesand et al., 2014; Sahu, 2007). In contrast, unsupervised classification has the relevant LULC classes delineated after the classification process which makes the procedure unnecessarily tedious and time-consuming (Lillesand et al., 2014). It is for this reason and more that this study employs the methods of supervised classification.
Selecting a suitable classification technique to use for the purpose of LULC mapping is essential as it has a direct effect on the accuracy of the classification. Currently, there are a vast number of supervised classification methods being utilised by different researchers however, it must be noted that the selection of a particular classification technique will differ according to the nature of the study in question (Varshney and Arora, 2004).
In order to identify the best classifier to utilize in this study, the following three commonly used classifiers were assessed; the Parallelepiped Classifier, which is also commonly known as the box classifier, the Minimum Distance to Mean (MDM) classifier and the Maximum Likelihood Classifier (MLC). Using the latest image of the study area (2012), each of the above mentioned classifications were conducted in order to identify the most suitable and accurate classifier algorithm. Selecting the 2012 SPOT 5 image as the base image to test each classifier was ideal due to the fact that the corresponding data (Google Earth Imagery and aerial photographs) were easy to attain. While each of the previously mentioned algorithms has particular strengths and weaknesses in terms of its application, it is important to choose the classifier that generates the greatest accuracy and in order to conduct this comparison, the ERDAS Imagine 2013 program was utilised for this research endeavour.
5.6.1 Parallelepiped Classifier
The parallelepiped classifier, or the box classifier, is considered to be one of the easiest and uncomplicated classifiers algorithms in comparison to others known (Aronoff, 2005; Richards and Jia, 2006). Based on the simple Boolean ‘and/or’ function, the threshold of each class’s signature is utilised in order to determine if the pixel in the question belongs to a specific class (Jensen, 2005; Teodoro et al., 2009).
Perumal and Bhaskaran (2010) have also identified that while making use of two bands, the parallelepiped classifier determines the training area of the pixels in each subsequent bands based on the minimum and maximum pixel value in the image. Therefore, pixels that are able to fall above the low threshold and below the high threshold of a particular class parallelepiped are assigned to that class (Lillesand et al., 2014;
Schowengerdt, 2006; Teodoro et al., 2009). While one flaw of this method is that a single pixel may fall within the overlap area between two or more class parallelepiped, the second is that pixels can also not fall within any of that class parallelepiped, thus leaving that pixel unclassified (Lillesand et al., 2014; Teodoro et al., 2009)
5.6.2 Minimum Distance to Mean (MDM) Classifier
The MDM classifier is another commonly used algorithm due to the ease in its functionality and ability to be computationally efficient (Acharya and Ray, 2005; Lillesand et al., 2014). By first calculating the mean of each class and then the Euclidean distance of each pixel from the mean, the MDM thereafter assigns pixels to the class that has the lowest or minimum distance to the mean. In other words, the distance between the pixel in question and the mean must be at the absolute minimum (Patil et al., 2012; Perumal and Bhaskaran, 2010).
However, it must be noted that a pixel will be considered as unclassified should it be further than the user- defined distance from any particular class mean (Aronoff, 2005; Joseph, 2005). Aronoff (2005) goes further on to state that while the MDM is highly effective when classifying large images, the algorithm fails to account for a wider range of spectral values which can result in the incorrect classification of pixels.
5.6.3 Maximum Likelihood (ML) Classifier
Considered to be a far more computationally intensive algorithm, the ML classifier (also known as the Gaussian ML classifier) is conducted by calculating the likelihood that a given pixel belongs to a set of predefined classes, thereafter the algorithm continues by assigning each pixel to the specific class for which the probability is the highest (Jensen, 2005; Keuchel et al., 2003).
One of the key differences between the ML classifier and other algorithms is that it does not require any extended training processes (Pal and Mather, 2003). The algorithm states that all training data acquired for each class within each band tend to follow a normal (Gaussian) distance (Jensen, 2005; Keuchel et al., 2003;
Pal and Mather, 2003). Due to the ML classifier’s computationally intensive nature, the algorithm has a considerably slower processing time when compared to other classifiers, especially when processing larger images (Aronoff, 2005; Patil et al., 2012).