With the hyperspectral imaging technology, hyperspectral data provides abundant spectral information and plays a more important role in the 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 multitemporal 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 article, we propose an original HACD algorithm based on autoencoder (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 siamese autoencoder 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 the mean square error 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.

中文翻译：

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基于自编码器的高光谱异常变化检测

利用高光谱成像技术，高光谱数据可提供丰富的光谱信息，并在地质勘测，植被分析和军事侦察中发挥更重要的作用。与正常变化检测不同，高光谱异常变化检测（HACD）有助于发现多时相高光谱图像（HSI）之间那些微小但重要的异常变化。在以前的工作中，大多数经典方法使用线性回归来建立两个HSI之间的映射关系，然后从残差图像中检测异常。但是，多时间HSI之间的实际频谱差异可能非常复杂且具有非线性，从而导致这些线性预测变量的性能受到限制。在本文中，我们提出了一种基于自动编码器（ACDA）的原始HACD算法，以给出非线性解决方案。当面对复杂的成像条件时，建议的ACDA可以构建有效的预测器模型。在ACDA模型中，部署了两个暹罗自动编码器网络来从两个方向构造两个预测器。预测器用于对背景的光谱变化建模，以获得在另一个成像条件下的预测图像。然后，计算预测图像和相应的预期图像之间的均方误差，以获得损失图，在该图中，未抑制像素的光谱差异得到了高度抑制，异常变化被突出显示。最终，我们将两个方向的两个损耗图中的最小值作为最终异常变化强度图。