Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 19, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2021.3050308
Keywords
Feature extraction; Collaboration; Detectors; Hyperspectral imaging; Task analysis; Mathematical model; Anomaly detection; Collaborative representation; hyperspectral anomaly detection (AD); hyperspectral images (HSIs); spectral-spatial information; stacked autoencoders (SAEs)
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Funding
- National Natural Science Foundation of China [61971153, 62002083, 62071136, 61801142]
- Fundamental Research Funds for the Central Universities [3072020CFJ0804, 3072020CFJ0805]
- Heilongjiang Postdoctoral Foundation [LBH-Z20038, LBH-Q20085]
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This study introduces a novel spectral-spatial hyperspectral anomaly detection method, SSCRSAE, which combines deep feature extraction and AD tasks while utilizing spatial information. Experimental results show its performance surpasses eight other state-of-the-art anomaly detectors.
Nowadays, due to the ability of extracting deep features, the deep learning-based anomaly detection (AD) methods for hyperspectral images (HSIs) have been widely studied. However, all these AD methods treat the tasks of feature extraction and AD separately. Besides, most of them also do not make use of abundant spatial information of HSIs. Thus, a spectral-spatial hyperspectral AD method via collaborative representation constraint stacked autoencoders (SSCRSAE) is proposed. First, the collaborative representation constraint is imposed on the stacked autoencoders to extract deep nonlinear features that are more suitable for the collaborative representation-based detector (CRD). Then, CRD is used to for obtaining the preliminary detection result, which is more convenient for real HSIs because of no need for assuming the distribution of the background. Finally, aiming at further improving the SSCRSAE detector's performance, a novel spectral-spatial AD procedure is designed for calculating the final detection result by considering the spatial information of an HSI. Experimental results express that the proposed SSCRSAE exceeds eight state-of-the-art anomaly detectors used for comparison.
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