Edge-Aware Semi-Supervised Built-up Area Extraction Using Multi-Temporal Synthetic Aperture Radar Images
To address the challenges of identifying and annotating built-up areas in synthetic aperture radar(SAR)images,a novel semi-supervised method for extracting built-up areas that combined improved pseudo-labeling techniques with an edge enhancement strategy was proposed.Initially,SAR images from the same location but at different time were introduced as a natural data augmentation method,and the pseudo-labels were determined by voting based on the prediction results of multi-temporal images.Subsequently,an edge-enhancement auxiliary module was designed,which corrected the body features of the built-up areas through feature map warping and improved edge features with skip connections.Separate supervision for the body and edge features was performed.Moreover,a dataset for extracting built-up areas in multi-temporal SAR images,which included two types of sensors and two urban areas,was constructed.This dataset contains 1,000 annotated images and 800 groups of unlabeled temporal images.Experimental validations based on this dataset have demonstrated that on the constructed test set,the baseline method trained with full data achieves an intersection over union(IoU)of 63.43%,while the proposed method reaches an IoU of 63.46%and 68.24%when using 10%and full data,respectively.Remarkably,using only 10%of the annotated data,the proposed method can achieve the precision that the baseline method has obtained with full annotated data.
built-up area extractionsynthetic aperture radarsemi-supervised learningedge extraction
国家科技期刊平台
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