Recursive SAM-based band selection for hyperspectral anomaly detection

Yuanlei He; Daizhi Liu; Shihua Yi

doi:10.1117/12.865958

22 October 2010 Recursive SAM-based band selection for hyperspectral anomaly detection

Yuanlei He, Daizhi Liu, Shihua Yi

Proceedings Volume 7658, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology; 76582K (2010) https://doi.org/10.1117/12.865958
Event: 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies, 2010, Dalian, China

Abstract

Band selection has been widely used in hyperspectral image processing for dimension reduction. In this paper, a recursive SAM-based band selection (RSAM-BBS) method is proposed. Once two initial bands are given, RSAM-BBS is performed in a sequential manner, and at each step the band that can best describe the spectral separation of two hyperspectral signatures is added to the bands already selected until the spectral angle reaches its maximum. In order to demonstrate the utility of the proposed band selection method, an anomaly detection algorithm is developed, which first extracts the anomalous target spectrum from the original image using automatic target detection and classification algorithm (ATDCA), followed by maximum spectral screening (MSS) to estimate the background average spectrum, then implements RSAM-BBS to select bands that participate in the subsequent adaptive cosine estimator (ACE) target detection. As shown in the experimental result on the AVIRIS dataset, less than five bands selected by the RSAM-BBS can achieve comparable detection performance using the full bands.

Citation Download Citation

Yuanlei He, Daizhi Liu, and Shihua Yi "Recursive SAM-based band selection for hyperspectral anomaly detection", Proc. SPIE 7658, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology, 76582K (22 October 2010); https://doi.org/10.1117/12.865958

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KEYWORDS

Target detection

Detection and tracking algorithms

Algorithm development

Hyperspectral target detection

Hyperspectral imaging

Sensors

Dimension reduction

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