期刊
NEURAL NETWORKS
卷 119, 期 -, 页码 222-234出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2019.08.012
关键词
Adversarial autoencoders (AAE); Hyperspectral anomaly detection; Unsupervised feature learning; Spectral constraint; Background suppression
资金
- National Natural Science Foundation of China [61801359, 61571345, 91538101, 61501346, 61502367, 61701360]
- Young Talent fund of 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
Anomaly detection in hyperspectral images (HSIs) faces various levels of difficulty due to the high dimensionality, redundant information and deteriorated bands. To address these problems, we propose a novel unsupervised feature representation approach by incorporating a spectral constraint strategy into adversarial autoencoders (AAE) without any prior knowledge in this paper. Our approach, called SC_AAE (spectral constraint AAE), is based on the characteristics of HSIs to obtain better discrimination represented by hidden nodes. To be specific, we adopt a spectral angle distance into the loss function of AAE to enforce spectral consistency. Considering the different contribution rates of each hidden node to anomaly detection, we individually fuse the hidden nodes by an adaptive weighting method. A bi-layer architecture is then designed to suppress the variational background (BKG) while preserving features of anomalies. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.
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