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A Hybrid Change Detection Method using Deep Feature Representations for VHR Images

With the successful launch of numerous advanced satellites, the acquisition of massive very-high-resolution (VHR) remote sensing images is available now. However, it is still a challenging task to effectively exploit these different types of data for monitoring the Earth surface changes. In this paper, we propose a novel hybrid change detection method based on deep feature representations for multitemporal VHR images, combining both the advantages of pixel-based change detection and object-based change detection techniques. Firstly, high-level and useful feature representations are extracted from raw data via unsupervised feature learning. Then, uncertainty change analysis is implemented to generate a pseudobinary change map, which will be used to select reliable training samples according to the superpixel segmentation map that is produced in advance. Finally, based on these samples, a robust support vector machine classifier is trained well to discriminate changed pixels from unchanged ones, thus making it easy to accurately detect the changes occurred between the considered images. Experimental results on two real VHR data sets demonstrate the effectiveness and flexibility of the proposed change detection approach.