The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-8
28 Nov 2014
 | 28 Nov 2014

Texture Based Hyperspectral Image Classification

B. Kumar and O. Dikshit

Keywords: Hyperspectral, Spectral, Texture, Geometric Moments, Classification, Support Vector Machine

Abstract. This research work presents a supervised classification framework for hyperspectral data that takes into account both spectral and spatial information. Texture analysis is performed to model spatial characteristics that provides additional information, which is used along with rich spectral measurements for better classification of hyperspectral imagery. The moment invariants of an image can derive shape characteristics, elongation, and orientation along its axis. In this investigation second order geometric moments within small window around each pixel are computed which are further used to compute texture features. The textural and spectral features of the image are combined to form a joint feature vector that is used for classification. The experiments are performed on different types of hyperspectral images using multi-class one-vs-one support vector machine (SVM) classifier to evaluate the robustness of the proposed methodology. The results demonstrate that integration of texture features produced statistically significantly better results than spectral classification.