The old-fashioned hard classification techniques are parametric in nature and they expect data to follow a Gaussian distribution, they have been found to be performing poorly on high resolution satellite images. The classes in these images tend to exhibit wide-ranging coinciding in spectral space. Digital image classification is the process of sorting all the pixels in an image into a finite number of individual classes. Decision making was performed in two stages: feature extraction using the Wavelet Packet Transforms (WPT) and the ANFIS trained with the back propagation gradient descent method in combination with the least squares method for classification .Decision tree algorithm based approach is analysed for the selection of a subset from the combination of Wavelet Packet Spatial Features and Wavelet Packet Co-occurrence (WPC) textural feature set, which are used to classify the multispectral images. Overall accuracy, sensitivity, specification are used to assess the accuracy of the classified data.
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Wavelet packet transforms, Wavelet packet spatial, Wavelet packet co-occurance, Decision tree, adaptive neuro fuzzy inference system.