基于深度学习的遥感图像水体提取综述OA北大核心CSTPCD

Timely and accurate detection and statistical analysis of the spatial distributions and time-series variations of water bodies like rivers and lakes holds critical significance and application value.It has become a significant interest in current remote sensing surface observation research.Conventional water body extraction methods rely on empirically designed index models for threshold-based segmentation or classification of water bodies.They are susceptible to shadows of surface features like vegetation and buildings,and physicochemical characteristics like sediment content and saline-alkali concentration in water bodies,thus failing to maintain robustness under different spatio-temporal scales.With the rapid acquisition of massive multi-source and multi-resolution remote sensing images,deep learning algorithms have gradually exhibited prominent advantages in water body extraction,garnering considerable attention both domestically and internationally.Thanks to the powerful learning abilities and flexible convolutional structure design schemes of deep neural network models,researchers have successively proposed various models and learning strategies to enhance the robustness and accuracy of water body extraction.However,there lacks a comprehensive review and problem analysis of research advances in this regard.Therefore,this study summarized the relevant research results published domestically and internationally in recent years,especially the advantages,limitations,and existing problems of different algorithms in the water body extraction from remote sensing images.Moreover,this study proposed suggestions and prospects for the advancement of deep learning-based methods for extracting water bodies from remote sensing images.