Bio-inspired anomaly target detection of multi-spectral remote sensing data

Min Li; Xuewu Zhang; Xinnan Fan; Zhuo Zhang

doi:10.1117/12.2031228

26 October 2013 Bio-inspired anomaly target detection of multi-spectral remote sensing data

Min Li, Xuewu Zhang, Xinnan Fan, Zhuo Zhang

Proceedings Volume 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 89210D (2013) https://doi.org/10.1117/12.2031228
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

Aimed to the limitation of present anomaly detection algorism under clutter background for multi-spectral remote sensing data, especially for the situations of dense spread target and exist different attributive of background objects, a bio-inspired anomaly detection algorithm was proposed. Simulate the information processing and fusion mechanism of fly multi-apertures vision system, multi-level background model was proposed to analysis and describe feature of clutter background. Then the threshold value can be chose adaptively according to the level of background model. The proposed algorithm didn’t need the prior knowledge about anomaly, and avoids the choosing of the background widow size. A fusion mechanism was proposed to fuse the different detection results with different level background model. Simulation experiment validated the effectiveness of proposed method.

Citation Download Citation

Min Li, Xuewu Zhang, Xinnan Fan, and Zhuo Zhang "Bio-inspired anomaly target detection of multi-spectral remote sensing data", Proc. SPIE 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 89210D (26 October 2013); https://doi.org/10.1117/12.2031228

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