基于双空间共轭自编码器的多时相高光谱异常变化检测

Multi-temporal Hyperspectral Anomaly Change Detection Based on Dual Space Conjugate Autoencoder

高光谱异常变化检测能够从多时相高光谱遥感图像中寻找到数量稀少、与整体背景变化趋势不同、难以发现且令人感兴趣的异常变化。数据集规模较小、存在噪声干扰以及线性预测模型存在局限性等问题,极大地降低了传统高光谱异常变化检测方法的检测性能。目前,自编码器已被成功地应用于高光谱异常变化检测。然而,单个自编码器在处理多时相高光谱图像时,仅关注图像的重构质量,在获取瓶颈特征时往往忽略了图像中复杂的光谱变化信息。为了解决该问题,提出了一种基于双空间共轭自编码器的多时相高光谱异常变化检测(Multi-temporal Hyperspectral Anomaly Change Detection Based on Dual Space Conjugate Autoencoder,DSCAE)方法。所提方法包含两个共轭的自编码器,即它们从不同方向构造各自的潜在特征。在该方法的训练过程中,首先,两幅不同时刻的高光谱图像经过各自的编码器分别获得相应的潜在空间特征表示,再分别经过各自的解码器获得另一时刻的预测图像;其次,在样本空间和潜在空间中施加不同的约束条件,并在两个空间中最小化相应的损失函数;最后,两幅输入图像经过共轭自编码器后获得各自的异常损失图,对所得的两幅异常损失图采用取小运算得到最终的异常变化强度图,以便在减小输入图像间背景光谱差异的同时突出异常变化。在高光谱异常变化检测基准数据集上的实验结果表明,与10种相关方法相比,DSCAE展现了更优的检测性能。

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.