Hyperspectral Image Classification Using SVM (International Journal of Engineering and Computer Science, 2014)
HyperSpectral image classification has been used for many purposes in remote sensing, and vegetation research, environmental monitoring and also for land cover classification. A hyperspectral image consists of many layers in which each layer represents a specific wavelength. This paper aims to classify the hyperspectral images to produce a thematic map accurately. Spatial information of hyperspectral images is collected by applying morphological profile and local binary pattern. Support vector machine (SVM) is an efficient classification algorithm for classifying the hyperspectral images. Genetic algorithm is used to obtain the best feature subjected for image classification. The classes and thematic map are generated by using future extraction. Experiment is carried out with AVIRIS Indian Pines and ROSIS Pavia University. This method produces the accuracy as 93% for Indian Pines and 92% for Pavia University.