A CBIR System for Hyperspectral Remote Sensing Images Using Endmember Extraction

Abstract

With the rapid development of remote sensing technology, searching the similar image is a challenge for hyperspectral remote sensing image processing. Meanwhile, the dramatic growth in the amount of hyperspectral remote sensing data has stimulated considerable research on content-based image retrieval (CBIR) in the field of remote sensing technology. Although many CBIR systems have been developed, few studies focused on the hyperspectral remote sensing images. A CBIR system for hyperspectral remote sensing image using endmember extraction is proposed in this paper. The main contributions of our method are that: (1) the endmembers as the spectral features are extracted from hyperspectral remote sensing image by improved automatic pixel purity index (APPI) algorithm; (2) the spectral information divergence and spectral angle match (SID–SAM) mixed measure method is utilized as a similarity measurement between hyperspectral remote sensing images. At last, the images are ranked with descending and the top-[Formula: see text] retrieved images are returned. The experimental results on NASA datasets show that our system can yield a superior performance.