Deep change feature analysis network for observing changes of land use or natural environment

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Change detection on surface of earth plays an important role in global-scale pattern of climate and biogeochemistry of the world, which helps to comprehend the connections and associations between human and nature. Remote Sensing and Geographic Information Systems can possibly provide accurate data in regards to land use and land cover changes. However, pixel-based change detection methods are limited in suppressing outliers and noise; they often fail to process remote sensing images with high spatial-/spectral-resolution. To conquer these drawbacks, a superpixel-level change detection and analysis method is proposed in this paper. Superpixels are the atomic regions gathering pixels with similar property, which will be more efficient and robust than pixels. Deep neural network is a powerful feature learning and classification tool, it can represent superpixel abstractly and classify them robustly. The learning progress of deep architectures includes unsupervised sample selection and supervised feature learning, unsupervised progress aims at selecting training samples for deep neural network, supervised progress aims at learning the representation of superpixels and fine-tuning the whole network to finish classification. Experimental results on multi-temporal images have demonstrated that the proposed approach can handle the task of change detection and analysis effectively and accurately.