Abstract: Hyperspectral anomaly change detection can find anomaly changes from multi-temporal hyperspectral remote sensing images.These anomaly changes are rare,different from the overall background change trend,difficult to be found,but very intere-sting.For the problems of small-sized data sets,existing noise disturbance,and limitation of linear prediction models,the detection performance of the conventional hyperspectral anomaly change detection methods are greatly degraded.At present,Autoencoder has been successfully applied to hyperspectral anomaly change detection.However,when processing multi-temporal hyperspectral images,a single autoencoder only focuses on the reconstruction quality of images,while usually ignores the complex spectral changes in these images as it obtains bottleneck features.To tackle this problem,the multi-temporal hyperspectral anomaly change detection based on dual space conjugate Autoencoder(DSCAE) method is proposed.The proposed method contains two conjugate autoencoders that construct their own latent features from different directions.In the training process of the proposed method,first,two hyperspectral images at different times respectively obtain their corresponding feature representation in the latent space by their encoders.Then,the predicted image at another time can be obtained by their decoders.Second,different constraints are imposed in the sample space and the latent space,respectively.Moreover,the corresponding loss functions are minimized in the two spaces.Finally,the anomaly loss maps are obtained by the conjugate autoencoders for the two images.The minimization operation is conducted on the two obtained anomaly loss maps to derive the final anomaly change intensity maps to simultaneously decrease the background spectral difference between the two input images and highlight anomaly changes.Experimental results on the benchmark data sets for the hyperspectral anomaly change detection demonstrate that DSCAE achieves better detection performance in comparison with its 10 pertinent methods.

Key words: Hyperspectral image anomaly change detection, Autoencoder, Deep learning, Anomaly detection, Multi-temporal hyperspectral images