Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 58, Issue 7, Pages 5224-5236Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.2975295
Keywords
Anomaly detection; Hyperspectral imaging; Gallium nitride; Training; Feature extraction; Generative adversarial networks; Anomaly detection; background distribution estimation; generative adversarial network (GAN); hyperspectral image (HSI); semisupervised learning
Categories
Funding
- National Natural Science Foundation of China [61801359, 61571345, 91538101, 61501346, 61502367, 61701360, U1704130]
- Young Talent Fund of the University Association for Science and Technology in Shaanxi of China [20190103]
- China Postdoctoral Science Foundation [2017M620440, 2019T120878]
- 111 Project [B08038]
- Fundamental Research Funds for the Central Universities [JB180104]
- Natural Science Basic Research Plan in Shaanxi Province of China [2019JQ153, 2016JQ6023, 2016JQ6018]
- Yangtse Rive Scholar Bonus Schemes [CJT160102]
- Ten Thousand Talent Program
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Limited by the anomalous spectral vectors in unlabeled hyperspectral images (HSIs), anomaly detection methods based on background distribution estimation often suffer from the contamination of anomalies, which decreases the estimation accuracy and, thus, weakens the detection performance. To address this problem, we proposed a novel semisupervised spectral learning (SSL) for the hyperspectral anomaly detection framework based on the generative adversarial network (GAN). GAN is applied and developed to estimate the background distribution in a semisupervised manner and obtain an initial spectral feature because of its strong representational capability and adversarial training advantage. In the proposed framework, an initial spatial feature is generated via morphological attribute filtering. Finally, an exponential constrained nonlinear suppression fusion technique is adopted to suppress the background and combine the complementary information in different features to obtain a fused detection map. The performance of the proposed anomaly detection technique is evaluated on a series of HSIs. Experimental results demonstrate that our method can outperform state-of-the-art anomaly detection methods.
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