River Extraction from High-Resolution Satellite Images Combining Deep Learning and Multiple Chessboard Segmentation

doi:10.13209/j.0479-8023.2019.045

Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2019, Vol. 55 ›› Issue (4): 692-698.DOI: 10.13209/j.0479-8023.2019.045

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River Extraction from High-Resolution Satellite Images Combining Deep Learning and Multiple Chessboard Segmentation

FANG Haiquan^1,†, JIANG Yunzhong², YE Yuntao², CAO Yin²

1. School of Mathematical Sciences, Peking University, Beijing 100871 2. Institute of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038

Received:2018-05-29 Revised:2018-10-23 Online:2019-07-20 Published:2019-07-20
Contact: FANG Haiquan, E-mail: fanghaiquan22(at)126.com

基于深度学习和多次棋盘分割法的高分辨率影像河流提取

方海泉^1,†, 蒋云钟², 冶运涛², 曹引²

1. 北京大学数学科学学院, 北京 100871 2. 中国水利水电科学研究院水资源研究所, 北京 100038

通讯作者: 方海泉, E-mail: fanghaiquan22(at)126.com
基金资助:
国家自然科学基金(51309254)资助

Abstract

Abstract:

Using existing methods to extract rivers, especially the small river from remote sensing images, is liable to be interrupted. The combination of deep learning and multiple chessboard segmentation is applied to river extraction from high resolution remote sensing images. Three GF-2 satellite remote sensing images in mountain area, plain and city are used for experiment. The results show that compared with the existing methods, extracted river by proposed method is more continuous. The small rivers accounts for two pixel widths can also be extracted in GF-2 satellite remote sensing images.

Key words: deep learning, multiple chessboard segmentation, high resolution satellite images, river extraction; convolution neural network (CNN)

摘要：

针对目前从遥感影像中提取的河流, 尤其是细小河流容易出现中断的情况, 将深度学习与多次棋盘分割法相结合, 应用于高分辨率遥感影像的河流提取。基于对山区、平原和城市3景高分二号卫星遥感影像的实验表明, 与现有的方法相比, 该方法提取的河流更加连续, 并且能够提取高分二号卫星遥感影像中两个像元的细小河流。

关键词: 深度学习, 多次棋盘分割法, 高分辨率遥感影像, 河流提取, 卷积神经网络(CNN)

FANG Haiquan, JIANG Yunzhong, YE Yuntao, CAO Yin. River Extraction from High-Resolution Satellite Images Combining Deep Learning and Multiple Chessboard Segmentation[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2019, 55(4): 692-698.

方海泉, 蒋云钟, 冶运涛, 曹引. 基于深度学习和多次棋盘分割法的高分辨率影像河流提取[J]. 北京大学学报自然科学版, 2019, 55(4): 692-698.

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River Extraction from High-Resolution Satellite Images Combining Deep Learning and Multiple Chessboard Segmentation

基于深度学习和多次棋盘分割法的高分辨率影像河流提取

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