Classification on hyperspectral images using enhanced covariance descriptor

Pattern classification is a vital area of computer vision. Classification of hyperspectral images is difficult and complex due to their high-dimensional characteristics. Covariance descriptor is often used in the area of pattern recognition on 2-dimensional images. In this study, we propose a different approach to classical covariance descriptor in hyper-spectral image classification. The proposed approach covers partial covariance matrix with efficient features instead of classical method. The performance of new approach is compared with the recent work in that area. We used AVIRIS hyperspectral data for implementations.