文章基本信息

标题：A GF-3 SAR Image Dataset of Road Segmentation
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作者：Zengguo Sun ; Mingmin Zhao ; Bai Jia 等
期刊名称：European Integration Studies
印刷版ISSN：2335-8831
出版年度：2021
卷号：50
期号：1
页码：89-101
DOI：10.5755/j01.itc.50.1.27987
语种：English
出版社：Kaunas University of Technology
摘要：We constructed a GF-3 SAR image dataset based on road segmentation to boost the development of GF-3 synthetic aperture radar (SAR) image road segmentation technology and make GF-3 SAR images be applied to practice better. We selected 23 scenes of GF-3 SAR images in Shaanxi, China, cut them into road chips with 512 × 512 pixels, and then labeled the dataset using LabelMe labeling tool. The dataset consists of 10026 road chips, and these road images are from different GF-3 imaging modes, so there is diversity in resolution and polarization. Three segmentation algorithms such as Multi-task Network Cascades (MNC), Fully Convolutional Instance-aware Semantic Segmentation (FCIS), and Mask Region Convolutional Neural Networks (Mask R-CNN) are trained by using the dataset. The experimental result measures including Average Precision (AP) and Intersection over Union (IoU) show that segmentation algorithms work well with this dataset, and the segmentation accuracy of Mask R-CNN is the best, which demonstrates the validity of the dataset we constructed.
关键词：GF-3 SAR image dataset;road segmentation;deep learning;Mask R-CNN