Rapid identification of areas affected by changes is a challenging task in many remote sensing applications. Sentinel-1 (S1) images provided by the European Space Agency (ESA) can be used to monitor such situations due to its high temporal and spatial resolution and indifference to weather. Though a number of deep learning based methods have been proposed in the literature for change detection (CD) in multi-temporal SAR images, most of them require labeled training data. Collecting sufficient labeled multi-temporal data is not trivial, however S1 provides abundant unlabeled data. To this end, we propose a solution for CD in multi-temporal S1 images based on unsupervised training of deep neural networks (DNNs). Unlabeled single-time image patches are used to train a multilayer convolutional-autoencoder (CAE) in unsupervised fashion by minimizing the reconstruction error between the reconstructed output and the input. The trained multilayer CAE is used to extract multi-scale features from both the pre and post change images that are analyzed for CD. The multi-scale features are fused according to a detail-preserving scale-driven approach that allows us to generate change maps by preserving details. The experiments conducted on a S1 dataset from Brumadinho, Brazil confirms the effectiveness of the proposed method.
Quick identification of post-earthquake destroyed buildings is critical for disaster management. It can be performed in unsupervised way by comparing pre-disaster and post-disaster Very-High-Resolution (VHR) SAR images. Spatial context needs to be modeled for effective change detection (CD) in VHR SAR images as they are complex and characterized by spatial correlation among pixels. We propose a unsupervised context-sensitive method for CD in multi-temporal VHR SAR images using pre-trained Convolutional-Neural-Network (CNN) based feature extraction. The sub-optimal CNN, pre-trained on an aerial optical image dataset and further optimized for using on SAR images by tuning the batch normalization layer of the CNN, enables us to obtain multi-temporal deep features that are pixelwise compared to identify the changed pixels. Detected changed pixels are further analyzed based on the double bounce property of the buildings in SAR images to detect the pixels corresponding to destroyed buildings. Experimental results on a dataset made up of a pair of multi-temporal VHR SAR images acquired by COSMO-SkyMed constellation on the city of L’Aquila (Italy) demonstrates effectiveness of the proposed approach.