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  

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

作　　者：方海泉 蒋云钟[2] 冶运涛[2] 曹引 FANG Haiquan;JIANG Yunzhong;YE Yuntao;CAO Yin(School of Mathematical Sciences, Peking University, Beijing 100871;Institute of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038)

机构地区：[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

出　　处：《北京大学学报（自然科学版）》2019年第4期692-698,共7页Acta Scientiarum Naturalium Universitatis Pekinensis

基　　金：国家自然科学基金(51309254)资助

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。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.

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) 

分 类 号：P332[天文地球—水文科学] TP751[水利工程—水文学及水资源] TP183[天文地球—地球物理学]