Abstrato

Combined Features based Spatial Composite Kernel Formation for Hyperspectral Image Classification

K.Kavitha , S.Arivazhagan, D.Sharmila Banu

Formation of Composite Kernels without incorporating Spectral Features is investigated. A State of Art Spatial Feature Extraction Algorithm for making Novel Composite Kernels is proposed for classifying a heterogeneous classes present in Hyperspectral Images during the unavailability of the Spectral Features. As the classes in the hyper spectral images have different textures, textural classification is entertained. Gray Level Co-Occurrence, Run Length Feature Extraction is entailed along with the Principal Component and Independent Component Analysis. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. Composite kernels are formed only by using the calculated Spatial Features without using Spectral Features. The proposed Composite Kernel is learned and tested by SVM with Binary Hierarchical Tree approach. To demonstrate the proposed algorithm, Hyper spectral Image of Indiana Pines Site taken by AVIRIS is selected. Among the original 220 bands, a subset of 150 bands is selected. Co-Occurrence and Run Length features are calculated for the selected fifty bands. The Principle Components are calculated for other fifty bands. Independent Components are calculated for next fifty bands. Results are validated with ground truth and accuracies are calculated