A Subpixel Target Detection Approach to Hyperspectral Image Classification

• Published in: IEEE transactions on geoscience and remote sensing 2017-09, Vol.55 (9), p.5093-5114
• Main Authors: Xue, Bai, Yu, Chunyan, Wang, Yulei, Song, Meiping, Li, Sen, Wang, Lin, Chen, Hsian-Min, Chang, Chein-I
• Format: Article
• Language: English
• Subjects: Band expansion (BE); band ratio expansion process (BREP); band selection (BS); BS-then-nonlinear expansion (BSNE); Classification; constrained energy minimization (CEM); Correlation; correlation BE process (CBEP); Detection; Detectors; Energy conservation; hyperspectral image classification; Hyperspectral imaging; Image classification; Image detection; iterative CEM (ICEM); nonlinear BE process (NBEP); Object detection; Otsu’s method; Overall accuracy (OA); Pixels; Precision rate; Spatial data; Support vector machine (SVM); Support vector machines; Target detection
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2017.2702197

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral-spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu's method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates.