  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.
