A GF-3 SAR Image Dataset of Road Segmentation
| Autor: | Sun Zengguo, Mingmin Zhao, Bai Jia |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Synthetic aperture radar
Pixel Intersection (set theory) business.industry Computer science Deep learning LabelMe 020206 networking & telecommunications Pattern recognition 02 engineering and technology Convolutional neural network Computer Science Applications Image (mathematics) Control and Systems Engineering 020204 information systems 0202 electrical engineering electronic engineering information engineering Segmentation Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | Information Technology and Control. 50:89-101 |
| ISSN: | 2335-884X 1392-124X |
| DOI: | 10.5755/j01.itc.50.1.27987 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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