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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (4) : 130-135     DOI: 10.6046/zrzyyg.2020397
Information extraction of Mars dunes based on U-Net
GUO Xiaozheng(), YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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The remote sensing-based information recognition of Mars dunes has important significance for the exploration of the interactions between the Martian atmosphere and the dune surface. Aiming at the low accuracy of the automatic information extraction of Mars dunes using the traditional machine learning method, this paper designs a method combining texture feature extraction and deep learning to automatically identify the information of Mars dunes. In detail, this method conducts information extraction based on the texture feature extraction of Mars remote sensing images combined with a deep learning model, thus realizing the end-to-end semantic segmentation of the remote sensing images. According to experiment results, the U-Net method can fully utilize the rich texture information in the remote sensing images and the extraction accuracy of dunes of this method was 96.7%, which was 3.2 percentage points higher than that of the traditional random forest method. Furthermore, compared to the traditional random forest method, the U-net method extracted more accurate and clearer contours of Mars dunes and yielded better extraction effects of highly fragmented dunes. Therefore, the U-net method can be used for accurate and automatic information extraction of Mars dunes.

Keywords Mars dunes      deep learning      random forest      texture feature      automated extraction     
ZTFLH:  TP79  
Issue Date: 23 December 2021
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Xiaozheng GUO
Yunjun YAO
Xiaotong ZHANG
Xiang ZHAO
Cite this article:   
Xiaozheng GUO,Yunjun YAO,Kun JIA, et al. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
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类型 编号 中心经纬度/(°) 影像获取日期 太阳入射
训练影像 1 125.4,76.4 2010/8/15 78
2 334.7,-48.2 2009/4/27 46
3 242.4,13.7 2009/8/24 42
4 297.5,76.5 2010/7/26 59
5 335,-40.9 2010/9/28 73
测试影像 6 182.5,81.3 2010/8/7 64
Tab.1  Image data
Fig.1  Example of LBP transform
Fig.2  Flowchart of HiRISE sand dune extraction experiment
Fig.3  Train examples of samples of RF
Fig.4  Train example of sample of U-Net
Fig.5  Sand dune extraction results
方法 FNR/% FPR/% AR/% 时间/ s
U-Net 0.3 3.7 96.7 80
RF 3.0 12.1 93.5 46
Tab.2  Comparison of accuracy evaluation of sand dune extraction models
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