A Superpixel-Guided Unsupervised Fast Semantic Segmentation Method of Remote Sensing Images

DOI码：10.1109/lgrs.2022.3198065

发表刊物：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

关键字：Deep learning (DL),fully convolutional networks (FCNs),remote sensing,semantic segmentation,superpixel,unsupervised learning

摘要：Semantic segmentation is one of the fundamental tasks of pixel-level remote sensing image analysis. Currently, most high-performance semantic segmentation methods are trained in a supervised learning manner. These methods require a large number of image labels as support, but manual annotations are difficult to obtain. To address the problem, we propose an efficient unsupervised remote sensing image segmentation method based on superpixel segmentation and fully convolutional networks (FCNs) in this letter. Our method can achieve pixel-level images segmentation of various scales rapidly without any manual labels or prior knowledge. We use the superpixel segmentation results as synthetic ground truth to guide the gradient descent direction during FCN training. In experiments, our method achieved high performance compared with current unsupervised image segmentation methods on three public datasets. Specifically, our method achieves an adjusted mutual information (AMI) score of 0.2955 on the Gaofen Image Dataset (GID), while processing each image of size 7200 x 6800 pixels in just 30 s.

合写作者：Chanjuan He,Tong Wang,Kun Zhu,Puyun Liao

论文类型：期刊论文

通讯作者：Xiaodong Zhang

文献类型：J

卷号：19

ISSN号：1545-598X

是否译文：否

发表时间：2022-08-11

收录刊物：SCI