Hyperspectral Anomaly Change Detection Based on Auto-encoder

• URL: http://arxiv.org/abs/2010.14119v1
• Date: Tue, 27 Oct 2020 08:07:08 GMT
• Title: Hyperspectral Anomaly Change Detection Based on Auto-encoder
• Authors: Meiqi Hu, Chen Wu, Liangpei Zhang, and Bo Du
• Abstract summary: Hyperspectral anomaly change detection (HACD) helps to find those small but important anomaly changes between hyperspectral images (HSI) In this paper, we propose an original HACD algorithm based on auto-encoder (ACDA) to give a nonlinear solution. The experiments results on public "Viareggio 2013" datasets demonstrate the efficiency and superiority over traditional methods.
• Score: 40.32592332449066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the hyperspectral imaging technology, hyperspectral data provides abundant spectral information and plays a more important role in geological survey, vegetation analysis and military reconnaissance. Different from normal change detection, hyperspectral anomaly change detection (HACD) helps to find those small but important anomaly changes between multi-temporal hyperspectral images (HSI). In previous works, most classical methods use linear regression to establish the mapping relationship between two HSIs and then detect the anomalies from the residual image. However, the real spectral differences between multi-temporal HSIs are likely to be quite complex and of nonlinearity, leading to the limited performance of these linear predictors. In this paper, we propose an original HACD algorithm based on auto-encoder (ACDA) to give a nonlinear solution. The proposed ACDA can construct an effective predictor model when facing complex imaging conditions. In the ACDA model, two systematic auto-encoder (AE) networks are deployed to construct two predictors from two directions. The predictor is used to model the spectral variation of the background to obtain the predicted image under another imaging condition. Then mean square error (MSE) between the predictive image and corresponding expected image is computed to obtain the loss map, where the spectral differences of the unchanged pixels are highly suppressed and anomaly changes are highlighted. Ultimately, we take the minimum of the two loss maps of two directions as the final anomaly change intensity map. The experiments results on public "Viareggio 2013" datasets demonstrate the efficiency and superiority over traditional methods.

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