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Journal of Applied Remote Sensing • Open Access

Decision fusion for dual-window-based hyperspectral anomaly detector
Author(s): Wei Li; Qian Du

Paper 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.

Paper Details

Date Published: 26 February 2015
PDF: 11 pages
J. Appl. Rem. Sens. 9(1) 097297 doi: 10.1117/1.JRS.9.097297
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Wei Li, Beijing Univ. of Chemical Technology (China)
Qian Du, Mississippi State Univ. (United States)

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