Wavelet Transform-Based Land Cover Classification of Satellite Images

Detection of urban expansion from land cover remote sensing images is a challenge due to the complexity of urban landscapes. Initially, the original satellite image is preprocessed and then segmented to have segments from different land classes such as of hilly land regions, vegetation area, building area, and water bodies. Different feature extraction methods such as first-order statistics, gray-level co-occurrence matrix (GLCM), and wavelet transform-based technique were applied in this paper, and the results are compared. The features of the segmented area are extracted, and then, final classification is carried out using the proposed probabilistic neural network (PNN) classifier. The classified satellite image is obtained and compared with the original image. The proposed technique is evaluated by means of accuracy parameter and produces better results using wavelet transform first-order statistics combined with PNN classifier.