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.
KeywordsFirst-order statistics Gray-level co-occurrence matrix Wavelet transform Satellite image processing Probabilistic neural network
- 1.S. Zhang, Q. Zhou, New feature extraction algorithm for satellite image non-linear small objects, in IEEE Symposium on Electrical and Electronics Engineering (2012)Google Scholar
- 4.TM. Lillesand, RW. Kiefer, JW. Chipman, Remote Sensing and Image Interpretation (Wiley International edition, Hoboken, 2004), pp. 586–592Google Scholar
- 6.L.A. Ruiz, A. Fdez-Sarría, J.A. Recio, Texture feature extraction for classification of remote sensing data using wavelet decomposition:a comp study. Remote Sens. Spatial Inf. Sci., in Proceedings of International Archives Photogramm (2004), pp. 1109–1115Google Scholar
- 7.M. Acharyya, M.K. Kundu, Wavelet-based texture segmentation of remotely sensed images. in Proceedings of International Conference Image Analysis and Processing (2001), pp. 69–74Google Scholar