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。

通过两阶段提示增强实现高保真湖泊提取：建立一种新的基准和评估

High-Fidelity Lake Extraction via Two-Stage Prompt Enhancement:  Establishing a Novel Baseline and Benchmark

Lake extraction from remote sensing imagery is challenging due to the complex
shapes of lakes and the presence of noise. Existing methods suffer from blurred
segmentation boundaries and poor foreground modeling. In this paper, we propose
a hybrid CNN-Transformer architecture, called LEFormer, for accurate lake
extraction. LEFormer contains four main modules: CNN encoder, Transformer
encoder, cross-encoder fusion, and lightweight decoder. The CNN encoder
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 better. Finally, a lightweight decoder is employed for mask
prediction. We evaluate the performance and efficiency of LEFormer on two
datasets, the Surface Water (SW) and the Qinghai-Tibet Plateau Lake (QTPL).
Experimental results show that LEFormer consistently achieves state-of-the-art
(SOTA) performance and efficiency on these two datasets, outperforming existing
methods. Specifically, LEFormer achieves 90.86% and 97.42% mIoU on the SW and
QTPL datasets with a parameter count of 3.61M, respectively, while being 20x
minor than the previous SOTA method.

提出了一种名为 LEFormer 的混合卷积神经网络 - 变换器架构，用于准确提取湖泊。LEFormer 包含 CNN 编码器、变换器编码器、交叉编码器融合和轻量级解码器四个主要模块，通过这种架构在两个数据集上实现了先进的性能和效率，优于现有方法。