Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels

Abstract

Texture information allows characterizing the regions of interest in a scene. It refers to the spatial organization of the fundamental microstructures in natural images. Texture extraction has been a challenging problem in the field of image processing for decades. In this paper, different techniques based on the classic Bag of Words (BoW) approach for solving the texture extraction problem in the case of hyperspectral images of the Earth surface are proposed. In all cases the texture extraction is performed inside regions of the scene called superpixels and the algorithms profit from the information available in all the bands of the image. The main contribution is the use of superpixel segmentation to obtain irregular patches from the images prior to texture extraction. Texture descriptors are extracted from each superpixel. Three schemes for texture extraction are proposed: codebook-based, descriptor-based, and spectral-enhanced descriptor-based. The first one is based on a codebook generator algorithm, while the other two include additional stages of keypoint detection and description. The evaluation is performed by analyzing the results of a supervised classification using Support Vector Machines (SVM), Random Forest (RF), and Extreme Learning Machines (ELM) after the texture extraction. The results show that the extraction of textures inside superpixels increases the accuracy of the obtained classification map. The proposed techniques are analyzed over different multi and hyperspectral datasets focusing on vegetation species identification. The best classification results for each image in terms of Overall Accuracy (OA) range from 81.07% to 93.77% for images taken at a river area in Galicia (Spain), and from 79.63% to 95.79% for a vast rural region in China with reasonable computation times.