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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.