  Lake extraction from remote sensing images is challenging due to the complex
lake shapes and inherent data noises. Existing methods suffer from blurred
segmentation boundaries and poor foreground modeling. This paper proposes a
hybrid CNN-Transformer architecture, called LEFormer, for accurate lake
extraction. LEFormer contains three main modules: CNN encoder, Transformer
encoder, and cross-encoder fusion. The CNN encoder effectively recovers local
spatial information and improves fine-scale details. Simultaneously, the
Transformer encoder captures long-range dependencies between sequences of any
length, allowing them to obtain global features and context information. The
cross-encoder fusion module integrates the local and global features to improve
mask prediction. Experimental results show that LEFormer consistently achieves
state-of-the-art performance and efficiency on the Surface Water and the
Qinghai-Tibet Plateau Lake datasets. Specifically, LEFormer achieves 90.86% and
97.42% mIoU on two datasets with a parameter count of 3.61M, respectively,
while being 20 minor than the previous best lake extraction method. The source
code is available at https://github.com/BastianChen/LEFormer.
