Feature extraction scheme for a textural hyperspectral image classification using gray-scaled HSV and NDVI image features vectors fusion

Hyperspectral images can be represented as a cube data structure. As a consequence, a spatial classification could be a difficult task. In this work, we describe a novel feature extraction methodology in order to perform a Hyperspectral image spatial classification. We turn the hyperspectral data into a gray-scaled HSV image and a Normalize Difference Vegetation Index (NDVI) representation. Afterwards, Haralick texture features are computed for both images, and the resulted features vectors are fused calculating the determinants of the matrices composed of these characteristics. To test the experimental accuracy of the proposed method, we employ five Hyperspectral images and a Maximum Likelihood Classifier (MLC). The current proposal is compared against other state-of-the-art methods, such as the employment of Principal Components Analysis (PCA).