Scribble-Based Weakly Supervised Deep Learning for Road Surface Extraction From Remote Sensing Images
نویسندگان
چکیده
Road surface extraction from remote sensing images using deep learning methods has achieved good performance, while most of the existing are based on fully supervised learning, which requires a large amount training data with laborious per-pixel annotation. In this article, we propose scribble-based weakly road method named ScRoadExtractor, learns easily accessible scribbles such as centerlines instead densely annotated ground truths. To propagate semantic information sparse to unlabeled pixels, introduce label propagation algorithm, considers both buffer-based properties networks and color spatial super-pixels, produce proposal mask categories road, nonroad, unknown. The mask, along auxiliary boundary prior detected images, is utilized train dual-branch encoder–decoder network designed for precise segmentation. We perform experiments three diverse sets that comprised high-resolution satellite aerial across world. results demonstrate ScRoadExtractor exceeds classic scribble-supervised segmentation by 20% intersection over union (IoU) indicator outperforms state-of-the-art at least 4%.
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ژورنال
عنوان ژورنال: IEEE Transactions on Geoscience and Remote Sensing
سال: 2022
ISSN: ['0196-2892', '1558-0644']
DOI: https://doi.org/10.1109/tgrs.2021.3061213