A Practical Framework for Surface Water Extraction from GF1/GF6 Wide-Field-View Imagery

Abstract

Gaofen-1/Gaofen-6 (GF1/GF6) wide-field-view (WFV) imagery provides repeated wide-area observations for regional surface-water monitoring, but its use is complicated by fragmented water patterns, vegetation–water mixtures, terrain and cloud shadows, and cross-scene radiometric variability. This study presents a seven-channel nested U-Net (UNet++) framework with a residual logit refinement branch (RLRB) for binary surface-water extraction. The input combines Blue, Green, Red, near-infrared, normalized difference water index, normalized difference vegetation index, and digital-elevation-model-derived slope channels. Five complete GF1/GF6 scenes were locked before dataset partitioning, and an 8192 m exclusion buffer was used to exclude spatially overlapping or adjacent non-test tiles from model development, thereby reducing the risk of spatial leakage. The resulting quantitative test set contained 2783 valid 512 by 512 pixel tiles derived from the five scenes withheld at scene level. The UNet++ model with RLRB and edge-consistency supervision achieved an mIoU of 0.941, Water IoU of 0.889, and Water F1 of 0.941. A controlled 2×2 experiment separated the effects of residual refinement and edge-consistency supervision, while feature, loss-component, random-seed, rule-based-baseline, and complete-scene analyses further characterized the framework. Complete-scene sliding-window inference required approximately 3.7 min per scene on a single NVIDIA GeForce RTX 5060 Laptop GPU, demonstrating the feasibility of georeferenced water-mask generation under the specified computational setting.