一种适用于高山复杂气候条件下的加权掩膜云去除模型

吴仁哲, 刘国祥, 张瑞, 吕继超, 杨知涵, 曹华林, 于睿麟

吴仁哲, 刘国祥, 张瑞, 吕继超, 杨知涵, 曹华林, 于睿麟. 一种适用于高山复杂气候条件下的加权掩膜云去除模型[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20240020
引用本文: 吴仁哲, 刘国祥, 张瑞, 吕继超, 杨知涵, 曹华林, 于睿麟. 一种适用于高山复杂气候条件下的加权掩膜云去除模型[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20240020
WU Renzhe, LIU Guoxiang, ZHANG Rui, LÜ Jichao, YANG Zhihan, CAO Hualin, YU Ruilin. A Cloud Removal Model for High-Mountain Canyon Terrain Under Weighted Mask Conditions[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240020
Citation: WU Renzhe, LIU Guoxiang, ZHANG Rui, LÜ Jichao, YANG Zhihan, CAO Hualin, YU Ruilin. A Cloud Removal Model for High-Mountain Canyon Terrain Under Weighted Mask Conditions[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240020

一种适用于高山复杂气候条件下的加权掩膜云去除模型

基金项目: 

国家自然科学基金(批准号: U22A20565,42371460,and 42171355)

国家重点研发计划(批准号: 2023YFB2604001)。

详细信息
    作者简介:

    吴仁哲,博士生,主要从事遥感智能解译及冰川冰湖动态演化研究。mrwurenzhe@my.swjtu.edu.cn

    通讯作者:

    刘国祥,博士,教授。rsgxliu@swjtu.edu.cn

A Cloud Removal Model for High-Mountain Canyon Terrain Under Weighted Mask Conditions

  • 摘要: 云雾覆盖是制约光学遥感研究的关键因素之一,尤其是在藏东南地区,其平均云量高达 62%, 严重 降低了光学遥感影像的可用性。与时序去云方法相比,单幅影像去云具有更强的时效性,但实现难度较高。 且现有的云层不透度估计方法难以恢复厚云覆盖区域的地表信息,而生成对抗网络方法则常常伴随伪影且可 解释性差。 针对上述问题,本文提出了一种适用于高山复杂气候条件下的加权掩膜云去除模型。 该模型采用 改进的 Transformer 模块作为生成器, 并重构了多头注意力机制,有效融合了薄云去除与厚云地表信息修复 的能力。经 Sentinel-2 Level-2A 数据测试表明,该模型在高保真恢复地表信息的同时,有效减少了厚云修复 时的伪影现象。 与现有云去除方法相比, 该模型在指标和视觉质量上均取得了较好的结果, 实现了平均绝对 值误差 0.0256、均方根误差 0.035 6、峰值信噪比 30.1851 以及结构相似性 0.899 6。 该研究成果可为高山复杂 气候条件下的云雾去除提供参考,并为山地研究提供数据支持。
    Abstract: Cloud cover significantly impedes optical remote sensing research, especially in southeastern Tibet where the average cloud cover reaches 62%, drastically reducing the availability of optical remote sensing images. Compared to temporal cloud removal methods, single-image cloud removal offers more immediate results, though it is more challenging. Existing cloud opacity estimation methods struggle to recover surface information in areas with thick cloud cover, and generative adversarial network approaches often produce artifacts and lack interpretability. To address these issues, this study introduces a weighted mask cloud removal model tailored for the complex alpine climate conditions. Methods: Targeting the stratocumulus phenomenon in remote sensing images, the model establishes assumptions about cloud opacity and brightness compensation. It incorporates an improved Transformer module based on these principles, along with an image restoration strategy, to accurately repair surface pixels obscured by clouds. The model enhances the multi-head attention mechanism by integrating a weighted mask based on cloud opacity and introduces a progressive mask updating strategy. This iterative process begins at the cloud edges, gradually restoring the remote sensing images. The model effectively combines thin and thick cloud removal capabilities, merging the strengths of both in repairing cloud-covered surface information. Results: The model's effectiveness was evaluated under various cloud cover scenarios, comparing its performance with existing models and assessing robustness in alpine regions with snow interference. The results show significant advantages in texture preservation and minimal artifact occurrence when cloud cover is below 20%. The model outperforms comparative methods for cloud cover between 20%-30% and exhibits fewer distortions above 30% cloud cover, generating only minimal artifacts in the densest cloud areas. It maintains stability under snow-covered conditions. The model's accuracy was tested on four metrics, showing MAE=0.025 6, RMSE=0.035 6, PSNR=30.185 1, SSIM=0.899 6, with approximately 61.6 million parameters and a computational complexity of 7.73 GMac. Conclusions: This research innovatively combines cloud opacity estimation and image generation techniques to develop a weighted mask cloud removal model for challenging alpine climate conditions. Utilizing generated cloud opacity and brightness compensation, the model simulates cloud layers and generates weighted masks. It refines the multi-head attention mechanism within the improved Transformer structure and uses weighted masks to regulate neuronal activation, effectively constraining the model's response under complex cloud conditions. A sliding window-based weighted mask updating strategy was introduced to optimize the training process, allowing the weighted mask to gradually diminish and eventually disappear. This study is the first to integrate thin and thick cloud removal strategies, offering a new perspective for the advancement of cloud removal technology and significantly enhancing the usability of optical images in complex alpine climates, thus providing valuable technical support for mountainous scientific research.
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出版历程
  • 收稿日期:  2024-06-02
  • 网络出版日期:  2024-06-26

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