Decision fusion for dual-window-based hyperspectral anomaly detector

Wei Li; Qian Du

doi:10.1117/1.JRS.9.097297

26 February 2015 Decision fusion for dual-window-based hyperspectral anomaly detector

Wei Li, Qian Du

Author Affiliations +

Funded by: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 097297 (February 2015). https://doi.org/10.1117/1.JRS.9.097297

Abstract

In hyperspectral anomaly detection, the dual-window-based detector is a widely used technique that employs two windows to capture nonstationary statistics of anomalies and background. However, its detection performance is usually sensitive to the choice of window sizes and suffers from inappropriate window settings. In this work, a decision-fusion approach is proposed to alleviate such sensitivity by merging the results from multiple detectors with different window sizes. The proposed approach is compared with the classic Reed-Xiaoli (RX) algorithm as well as kernel RX (KRX) using two real hyperspectral data. Experimental results demonstrate that it outperforms the existing detectors, such as RX, KRX, and multiple-window-based RX. The overall detection framework is suitable for parallel computing, which can greatly reduce computational time when processing large-scale remote sensing image data.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Wei Li and Qian Du "Decision fusion for dual-window-based hyperspectral anomaly detector," Journal of Applied Remote Sensing 9(1), 097297 (26 February 2015). https://doi.org/10.1117/1.JRS.9.097297

Published: 26 February 2015

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Cited by 26 scholarly publications.

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KEYWORDS

Sensors

Target detection

Hyperspectral target detection

Image fusion

Detection and tracking algorithms

Hyperspectral imaging

Data fusion

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