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Hyperspectral Image Classification Using Support Vector Machine in Ridgelet Domain

Kavitha, K., Arivazhagan, S., Kanaga Sangeetha, I.
National Academy science letters 2015 v.38 no.6 pp. 475-478
data collection, energy, entropy, hyperspectral imagery, image analysis, optics, remote sensing, spectrometers, support vector machines, Indiana
Classification of diverse classes available in the hyperspectral imagery is one of the recent research issues in remote sensing field. This work proposes a new technique that classifies hyperspectral images on ridgelet transformed domain. For hyperspectral image ridgelet coefficients are calculated. Co-occurrence features such as energy, entropy and contrast are extracted for the obtained ridgelet co-efficients and extracted features exhibit inter-pixel relationship. As stated in many literatures, the support vector machines (SVM) along with radial basis function kernel is used for classifying the classes which are non-linearly distributed in the hyperspectral scene. One-against-all binary hierarchical tree strategy is adopted while classifying images by SVM. Hyperspectral image captured over the North Western Indiana by airborne visible infra red imaging sensor (AVIRIS) and a subset of Pavia University data captured by reflective optics system imaging spectrometer (ROSIS) have been chosen for the experiment. The proposed algorithm produced the accuracies of 92.33 and 94.08 % for AVIRIS and ROSIS data sets respectively.