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Proceedings Paper

Feature-level fusion of multiple target detection results in hyperspectral image based on RX detector
Author(s): Xu Sun; Bing Zhang; Lina Yang; Lianru Gao; Wenjuan Zhang
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Paper Abstract

Target detection is an important research content in hyperspectral remote sensing technology, which is widely used in securities and defenses. Nowadays, many target detection algorithm have been proposed. One of the key evaluation indicators of these algorithms performance is false-alarm rate. The feature-level fusion of different target detection results is a simple and effective method to reduce false-alarm rate. But the different value ranges of different algorithms bring difficulties for data fusion. This paper proposed a feature-level fusion method based on RXD detector, which is to integrate multiple target detection results into a multi-bands image, and fuse detection results using principal theory of abnormal detection. Experiments revealed that, this method is not restricted by the quantity of target detection algorithms and not influenced by different value ranges of different algorithms, which can reduce false-alarm rate effectively.

Paper Details

Date Published: 2 May 2012
PDF: 6 pages
Proc. SPIE 8391, Automatic Target Recognition XXII, 83910R (2 May 2012); doi: 10.1117/12.918794
Show Author Affiliations
Xu Sun, Ctr. for Earth Observation and Digital Earth (China)
Bing Zhang, Ctr. for Earth Observation and Digital Earth (China)
Lina Yang, Institute of Remote Sensing Applications (China)
Lianru Gao, Ctr. for Earth Observation and Digital Earth (China)
Wenjuan Zhang, Ctr. for Earth Observation and Digital Earth (China)

Published in SPIE Proceedings Vol. 8391:
Automatic Target Recognition XXII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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