DecloudFormer: Quest the key to consistent thin cloud removal of wide-swath multi-spectral images

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Pub Date : 2025-04-15 DOI:10.1016/j.patcog.2025.111664

Mingkai Li , Qizhi Xu , Kaiqi Li , Wei Li

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Abstract

Wide-swath images contain clouds of various shapes and thicknesses. Existing methods have different thin cloud removal strengths in different patches of the wide-swath image. This leads to severe cross-patch color inconsistency in the thin cloud removal results of wide-swath images. To solve this problem, a DecloudFormer with cross-patch thin cloud removal consistency was proposed. First, a Group Layer Normalization (GLNorm) was proposed to preserve both the spatial and channel distribution of thin cloud. Second, a CheckerBoard Mask (CB Mask) was proposed to make the network focus on different cloud-covered areas of the image and extract local cloud features. Finally, a two-branch DecloudFormer Block containing the CheckerBoard Attention (CBA) was proposed to fuse the global cloud features and local cloud features to reduce the cross-patch color difference. DecloudFormer and compared methods were tested for simulated thin cloud removal performance on images from QuickBird, GaoFen-2, and WorldView-2 satellites, and for real thin cloud removal performance on images from Landsat-8 satellite. The experiment results demonstrated that DecloudFormer outperformed the existing State-Of-The-Art (SOTA) methods. Furthermore, DecloudFormer makes it possible to process thin cloud covered wide-swath image using a small video memory GPU. The source code are available at the link.

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DecloudFormer：寻找宽幅多光谱图像中一致的薄云去除的关键

宽幅图像包含各种形状和厚度的云。现有方法在宽幅图像的不同斑块上具有不同的薄云去除强度。这导致宽幅图像的薄云去除结果中存在严重的跨斑块颜色不一致。为了解决这一问题，提出了具有跨补丁薄云去除一致性的DecloudFormer。首先，提出了一种组层归一化（GLNorm）方法来保持薄云的空间分布和通道分布；其次，提出了一种棋盘掩码（CheckerBoard Mask, CB Mask），使网络聚焦于图像的不同云覆盖区域，提取局部云特征；最后，提出了一种包含棋盘注意（CheckerBoard Attention， CBA）的双分支DecloudFormer块，融合全局云特征和局部云特征，以减小斑块间的色差。测试了DecloudFormer和比较方法对QuickBird、高分2号和WorldView-2卫星图像的模拟薄云去除性能，以及对Landsat-8卫星图像的真实薄云去除性能。实验结果表明，DecloudFormer优于现有的最先进的（SOTA）方法。此外，DecloudFormer使使用小型显卡GPU处理薄云覆盖的宽幅图像成为可能。源代码可从链接获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Pattern Recognition 工程技术-工程：电子与电气

CiteScore

14.40

自引率

16.20%

发文量

683

审稿时长

5.6 months

期刊介绍： The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.