Wavelet transform based land cover classification of hyperspectral images

This paper aims at the wavelet transform based algorithm for landcover classification of Hyperspectral remote sensing images using Support Vector Machines (SVM). In this paper Feature Extraction and Hyperspectral pixel classification are done based on Discrete Wavelet Transform (DWT) features which includes the Statistical Features and the Gray Level Co-occurrence Features. The experiment is performed on a hyperspectral dataset acquired from ROSIS sensor and the experimental results indicate that it provides an Overall accuracy of about 98.28%. When compared to the other methods, the wavelet transform based method increases the overall classification accuracy.