Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 14, Issue 5, Pages 597-601Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2657818
Keywords
Anomaly detection; convolutional neural network (CNN); deep learning; hyperspectral imagery
Categories
Funding
- National Natural Science Foundation of China [NSFC-91638201, 61571033]
- Higher Education and High-Quality and World-Class Universities [PY201619]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available