Unsupervised Change Detection for VHR Remote Sensing Images Based on Temporal-Spatial-Structural Graphs

With the aim of automatically extracting fine change information from ground objects, change detection (CD) for very high resolution (VHR) remote sensing images is extremely essential in various applications. However, the increase in spatial resolution, more complicated interactive relationships of ground objects, more evident diversity of spectra, and more severe speckle noise make accurately identifying relevant changes more challenging. To address these issues, an unsupervised temporal-spatial-structural graph is proposed for CD tasks. Treating each superpixel as a node of graph, the structural information of ground objects presented by the parent–offspring relationships with coarse and fine segmented scales is introduced to define the temporal-structural neighborhood, which is then incorporated with the spatial neighborhood to form the temporal-spatial-structural neighborhood. The graphs defined on such neighborhoods extend the interactive range among nodes from two dimensions to three dimensions, which can more perfectly exploit the structural and contextual information of bi-temporal images. Subsequently, a metric function is designed according to the spectral and structural similarity between graphs to measure the level of changes, which is more reasonable due to the comprehensive utilization of temporal-spatial-structural information. The experimental results on both VHR optical and SAR images demonstrate the superiority and effectiveness of the proposed method.