NT-Net: A Semantic Segmentation Network for Extracting Lake Water Bodies From Optical Remote Sensing Images Based on Transformer

The automatic extraction of lake water is one of the research hotspots in the field of remote sensing image processing. Due to the small interclass variance between lakes and other ground objects, and the complex texture characteristics of lake boundaries, existing methods often have problems such as over-segmentation and inaccurate boundary segmentation when segmenting lake water bodies. To alleviate these problems, this article designs an end-to-end semantic segmentation network [noise-canceling transformer network (NT-Net)] for the automatic extraction of lake water bodies from remote sensing images. Aiming at the problem of over-segmentation caused by nonlake objects, an interference attenuation module is designed in the network. This module can model the key features that are distinguishable and suitable for segmenting lake water by analyzing the difference in feature representation between lakes and other ground objects, thus suppressing the feature representation of nonlake objects. To more accurately segment the lake boundary, a multilevel transformer module is designed. This module can capture the context association of boundary information and enhance the feature representation of boundary information by using the self-attention mechanism. The comparative experimental results show that, compared with the current mainstream semantic segmentation networks, the method in this article has advantages in extracting lake water bodies comprehensively and coherently.