Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/148701
Title: Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery
Authors: Wei Li;Guodong Wu;Qian Du
Year: 2017
Publisher: IEEE
Abstract: In this letter, a novel anomaly detection framework with transferred deep convolutional neural network (CNN) is proposed. The framework is designed by considering the following facts: 1) a reference data with labeled samples are utilized, because no prior information is available about the image scene for anomaly detection and 2) pixel pairs are generated to enlarge the sample size, since the advantage of CNN can be realized only if the number of training samples is sufficient. A multilayer CNN is trained by using difference between pixel pairs generated from the reference image scene. Then, for each pixel in the image for anomaly detection, difference between pixel pairs, constructed by combining the center pixel and its surrounding pixels, is classified by the trained CNN with the result of similarity measurement. The detection output is simply generated by averaging these similarity scores. Experimental performance demonstrates that the proposed algorithm outperforms the classic Reed-Xiaoli and the state-of-the-art representation-based detectors, such as sparse representation-based detector (SRD) and collaborative representation-based detector.
URI: http://localhost/handle/Hannan/148701
volume: 14
issue: 5
More Information: 597,
601
Appears in Collections:2017

Files in This Item:
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7875485.pdf1.6 MBAdobe PDF
Title: Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery
Authors: Wei Li;Guodong Wu;Qian Du
Year: 2017
Publisher: IEEE
Abstract: In this letter, a novel anomaly detection framework with transferred deep convolutional neural network (CNN) is proposed. The framework is designed by considering the following facts: 1) a reference data with labeled samples are utilized, because no prior information is available about the image scene for anomaly detection and 2) pixel pairs are generated to enlarge the sample size, since the advantage of CNN can be realized only if the number of training samples is sufficient. A multilayer CNN is trained by using difference between pixel pairs generated from the reference image scene. Then, for each pixel in the image for anomaly detection, difference between pixel pairs, constructed by combining the center pixel and its surrounding pixels, is classified by the trained CNN with the result of similarity measurement. The detection output is simply generated by averaging these similarity scores. Experimental performance demonstrates that the proposed algorithm outperforms the classic Reed-Xiaoli and the state-of-the-art representation-based detectors, such as sparse representation-based detector (SRD) and collaborative representation-based detector.
URI: http://localhost/handle/Hannan/148701
volume: 14
issue: 5
More Information: 597,
601
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7875485.pdf1.6 MBAdobe PDF
Title: Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery
Authors: Wei Li;Guodong Wu;Qian Du
Year: 2017
Publisher: IEEE
Abstract: In this letter, a novel anomaly detection framework with transferred deep convolutional neural network (CNN) is proposed. The framework is designed by considering the following facts: 1) a reference data with labeled samples are utilized, because no prior information is available about the image scene for anomaly detection and 2) pixel pairs are generated to enlarge the sample size, since the advantage of CNN can be realized only if the number of training samples is sufficient. A multilayer CNN is trained by using difference between pixel pairs generated from the reference image scene. Then, for each pixel in the image for anomaly detection, difference between pixel pairs, constructed by combining the center pixel and its surrounding pixels, is classified by the trained CNN with the result of similarity measurement. The detection output is simply generated by averaging these similarity scores. Experimental performance demonstrates that the proposed algorithm outperforms the classic Reed-Xiaoli and the state-of-the-art representation-based detectors, such as sparse representation-based detector (SRD) and collaborative representation-based detector.
URI: http://localhost/handle/Hannan/148701
volume: 14
issue: 5
More Information: 597,
601
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7875485.pdf1.6 MBAdobe PDF