Existing lightweight flood extent mapping (FEM) methods, leveraging satellite imagery, offer respite from the burden of extensive data processing. However, these methods often grapple with suboptimal accuracy of the FEM results. To tackle this challenge, this paper introduces an innovative lightweight historical information fusion framework aimed at enhancing FEM results through the utilization of dense multi-source satellite image time series (MSSITS). Firstly, we propose an ultra-lightweight convolutional neural network comprising fundamental network modules, such as convolution layers, batch normalization layers, and parametric rectified linear unit layers. This network swiftly extracts initial FEM results from dense MSSITS. Secondly, we devise a historical information fusion strategy to refine the initial FEM results of the current image by fusing historical FEM results exhibiting high temporal correlation. During the fusion stage, we harness the complementary attributes of dense MSSITS. Specifically, the fusion of diverse satellite sensor data aids in mitigating false alarms in the initial FEM results, thereby facilitating the acquisition of refined FEM results. This approach effectively diminishes the impact of noise interferences, including terrain shadows and surging flood waves in synthetic aperture radar images, as well as cloud occlusions and cloud shadows in optical images. Experimental findings, based on the dense MSSITS of four authentic flood events, demonstrate that our proposed method yields relatively superior FEM performance while maintaining a low FEM complexity in comparison with commonly used semantic segmentation methods.

中文翻译：

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稠密多源卫星图像时间序列历史信息融合用于洪水范围测绘


现有的轻量级洪水范围测绘 (FEM) 方法利用卫星图像，可以缓解大量数据处理的负担。然而，这些方法常常难以解决 FEM 结果的次优精度问题。为了应对这一挑战，本文引入了一种创新的轻量级历史信息融合框架，旨在通过利用密集的多源卫星图像时间序列（MSSITS）来增强有限元结果。首先，我们提出了一种超轻量级卷积神经网络，包括基本网络模块，例如卷积层、批量归一化层和参数校正线性单元层。该网络快速从密集的 MSSITS 中提取初始 FEM 结果。其次，我们设计了一种历史信息融合策略，通过融合表现出高时间相关性的历史 FEM 结果来细化当前图像的初始 FEM 结果。在融合阶段，我们利用密集 MSSITS 的互补属性。具体而言，不同卫星传感器数据的融合有助于减少初始 FEM 结果中的误报，从而有助于获取精细的 FEM 结果。这种方法有效地消除了噪声干扰的影响，包括合成孔径雷达图像中的地形阴影和汹涌的洪水波，以及光学图像中的云遮挡和云阴影。基于四个真实洪水事件的密集 MSSITS 的实验结果表明，与常用的语义分割方法相比，我们提出的方法可产生相对优越的 FEM 性能，同时保持较低的 FEM 复杂度。