An Anomaly Detector Based on Multi-aperture Mapping for Hyperspectral Data

doi:10.11947/j.AGCS.2016.20160119

Acta Geodaetica et Cartographica Sinica ›› 2016, Vol. 45 ›› Issue (10): 1222-1230.doi: 10.11947/j.AGCS.2016.20160119

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An Anomaly Detector Based on Multi-aperture Mapping for Hyperspectral Data

LI Min¹, ZHU Guokang², ZHANG Xuewu¹, FAN Xinnan¹, LI Puhuang¹

1. College of Internet of Things, Hohai University, Changzhou 213022, China;
2. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China

Received:2016-03-25 Revised:2016-08-26 Online:2016-10-20 Published:2016-11-08
Supported by:
The National Natural Science Foundation of China (Nos.41301448;61503235;61273170;61573128;61671202);The National Key Research Program of China (No.2016YEC0401606);The Fundamental Research Funds for the Central Universities (No.2015B25214)

Abstract

Abstract:

Considering the correlationship of spectral content between anomaly and clutter background, inaccurate selection of background pixels induced estimation error of background model. In order to solve the above problems, a multi-aperture mapping based anomaly detector was proposed in this paper. Firstly, differing from background model which focused on feature extraction of background, multi-aperture mapping of hyperspectral data characterized the feature of whole hyperspectral data. According to constructed basis set of multi-aperture mapping, anomaly salience index of every test pixel was proposed to measure the relative statistic difference. Secondly, in order to analysis the moderate salience anomaly precisely, membership value was constructed to identify anomaly salience of test pixels continuously based on fuzzy logical theory. At same time, weighted iterative estimation of multi-aperture mapping was expected to converge adaptively with membership value as weight. Thirdly, classical defuzzification was proposed to fuse different detection results. Hyperspectral data was used in the experiments, and the robustness and sensitivity to anomaly with lower silence of proposed detector were tested.

Key words: hyperspectral data, anomaly detection, multi-aperture model, fuzzy logical theory

CLC Number:

P237

LI Min, ZHU Guokang, ZHANG Xuewu, FAN Xinnan, LI Puhuang. An Anomaly Detector Based on Multi-aperture Mapping for Hyperspectral Data[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(10): 1222-1230.

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An Anomaly Detector Based on Multi-aperture Mapping for Hyperspectral Data

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