The extraction of lakes from remote sensing images is a complex challenge due
to the varied lake shapes and data noise. Current methods rely on multispectral
image datasets, making it challenging to learn lake features accurately from
pixel arrangements. This, in turn, affects model learning and the creation of
accurate segmentation masks. This paper introduces a unified prompt-based
dataset construction approach that provides approximate lake locations using
point, box, and mask prompts. We also propose a two-stage prompt enhancement
framework, LEPrompter, which involves prompt-based and prompt-free stages
during training. The prompt-based stage employs a prompt encoder to extract
prior information, integrating prompt tokens and image embeddings through self-
and cross-attention in the prompt decoder. Prompts are deactivated once the
model is trained to ensure independence during inference, enabling automated
lake extraction. Evaluations on Surface Water and Qinghai-Tibet Plateau Lake
datasets show consistent performance improvements compared to the previous
state-of-the-art method. LEPrompter achieves mIoU scores of 91.48% and 97.43%
on the respective datasets without introducing additional parameters or GFLOPs.
Supplementary materials provide the source code, pre-trained models, and
detailed user studies.

本文介绍了一种统一的基于提示的数据集构建方法，使用点、盒、和掩模提示提供了近似的湖泊位置，并提出了一种两阶段基于提示的增强框架 LEPrompter，在训练过程中使用提示编码器提取先验信息，通过自关注和交叉关注将提示标记和图像嵌入进行整合，在推理期间关闭提示，从而实现了自动湖泊提取。在 Surface Water 和青藏高原湖泊数据集上的评估结果表明，与最先进方法相比，LEPrompter 在两个数据集上都取得了一致的性能提升，mIoU 得分分别达到 91.48% 和 97.43%，而且没有引入额外的参数或 GFLOPs。辅助材料包括源代码、预训练模型和详细的用户研究。