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2 May 2012 Feature-level fusion of multiple target detection results in hyperspectral image based on RX detector
Xu Sun, Bing Zhang, Lina Yang, Lianru Gao, Wenjuan Zhang
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Proceedings Volume 8391, Automatic Target Recognition XXII; 83910R (2012) https://doi.org/10.1117/12.918794
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States
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.
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Xu Sun, Bing Zhang, Lina Yang, Lianru Gao, and Wenjuan Zhang "Feature-level fusion of multiple target detection results in hyperspectral image based on RX detector", Proc. SPIE 8391, Automatic Target Recognition XXII, 83910R (2 May 2012); https://doi.org/10.1117/12.918794
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KEYWORDS
Detection and tracking algorithms
Target detection
Image fusion
Sensors
Hyperspectral imaging
Image processing algorithms and systems
Image segmentation
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