AMFF-LWBENet: A Novel Deep Learning Network Model for Extracting Lake Water Bodies From Remote Sensing Images

Abstract

The extraction of lake water bodies from remote sensing images is vital for water resource management, environmental protection, climate change research, disaster monitoring, and land use planning. This study presents a novel model, AMFF-LWBENet (A Lake Water Body Extraction Network based on ASPP and Multi-scale Feature Fusion), designed to tackle challenges such as insufficient spatial detail, poor edge recognition, and low anti-noise performance found in existing lake water body extraction models. The model utilizes an encoder-decoder architecture, leveraging ResNet50 for downsampling to extract deep features, which are then fused more effectively through the multi-scale dense fusion (MDF) module, incorporating depthwise separable convolution and ASPP to enhance the integration of multi-scale features. Additionally, position and channel correlations in the feature map are captured using DANet, minimizing noise interference at the lake edge and improving segmentation accuracy. Bilinear interpolation is employed to upsample the feature map, and feature fusion is achieved through the cross-layer feature fusion (CFF) module, enabling precise lake water body extraction. The datasets utilized include a self-built W-H dataset, derived from Landsat images collected between 2015 and 2023, as well as the publicly available TP dataset. The AMFF-LWBENet achieved MIoU values of 97.52% on the W-H dataset and 97.5% on the TP dataset, surpassing current state-of-the-art semantic segmentation networks. These results suggest that AMFF-LWBENet is even more effective than many other networks in extracting lake water bodies, offering significantly enhanced support for a wide range of environmental and urban planning applications.

Keywords

• Remote sensing
• water body extraction
• deep learning
• semantic segmentation
• attention mechanism
• ResNet