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Semisupervised Spectral Learning With Generative Adversarial Network for Hyperspectral Anomaly Detection

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.