A GF-3 SAR Image Dataset of Road Segmentation

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...

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Bibliographic Details
Published in:Information technology and control Vol. 50; no. 1; pp. 89 - 101
Main Authors: Sun, Zengguo, Zhao, Mingmin, Jia, Bai
Format: Journal Article
Language:English
Published: Kaunas University of Technology 25.03.2021
Subjects:
deep learning
GF-3 SAR image dataset
road segmentation
Mask R-CNN
ISSN:1392-124X, 2335-884X
Online Access:Get full text
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Summary: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.
ISSN:1392-124X
2335-884X
DOI:10.5755/j01.itc.50.1.27987