RiverMapper: step-wisely mapping the surface rivers on optical remote sensing images

Peng Zhang; Hengyue Pan; Ke Yang; Yong Dou; Xin Niu

doi:10.1117/12.2639214

15 July 2022 RiverMapper: step-wisely mapping the surface rivers on optical remote sensing images

Peng Zhang, Hengyue Pan, Ke Yang, Yong Dou, Xin Niu

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 122581T (2022) https://doi.org/10.1117/12.2639214
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

Accurately mapping the surface rivers is important in ecological environment monitoring and disaster prevention. The development of remote sensing technology and computer vision greatly improves the efficiency of this task. However, there are few methods that map the rivers from an image directly. The existing automatic river mapping methods usually had two successive stages: waterbody extraction and flow-path extraction, where the latter methods were very dependent on the waterbody masks generated by the former methods. Errors in waterbody masks caused breaks and redundancies in the extracted graphs. This paper proposed RiverMapper, which mapped the rivers step-wisely without dividing into two stages. Following the directions and actions predicted by the convolution neural network, RiverMapper walked along the rivers step by step and cropped the fixed-size image patches at each step for segmentation. Final river graphs were constructed by the waterbody mask patches and those tracks generated by RiverMapper. We applied RiverMapper on optical remote sensing images containing the Changjiang River and the Huanghe River. Without the degradation of the performance on waterbody extraction, RiverMapper outperformed other methods in terms of the local topological and geometrical similarity between the predicted and the ground-truth river graphs.

Citation Download Citation

Peng Zhang, Hengyue Pan, Ke Yang, Yong Dou, and Xin Niu "RiverMapper: step-wisely mapping the surface rivers on optical remote sensing images", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 122581T (15 July 2022); https://doi.org/10.1117/12.2639214

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