CD-CTFM：一种融合多尺度特征的轻量级CNN-Transformer遥感云检测网络
CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features
AI总结：
本文提出轻量级CNN-Transformer网络CD-CTFM，通过轻量级特征金字塔融合多尺度特征，并在跳跃连接中引入通道-空间注意力模块，在38-Cloud和MODIS数据集上以更低计算成本实现了与最先进方法相当的云检测精度。
摘要：
遥感图像中的云不可避免地影响信息提取，从而阻碍后续的卫星图像分析。因此，云检测是必要的预处理步骤。然而，现有方法存在计算量和参数量大的问题。本文提出了一种轻量级CNN-Transformer网络CD-CTFM来解决该问题。CD-CTFM基于编码器-解码器架构，并引入了注意力机制。在编码器部分，我们采用一个结合CNN和Transformer的轻量级网络作为骨干，有利于同时提取局部和全局特征。此外，设计了一个轻量级特征金字塔模块，用于融合具有上下文信息的多尺度特征。在解码器部分，我们将一个轻量级通道-空间注意力模块集成到编码器和解码器之间的每个跳跃连接中，在提取低级特征的同时抑制无关信息，且不引入大量参数。最后，所提模型在两个云数据集38-Cloud和MODIS上进行了评估。结果表明，CD-CTFM达到了与最先进方法相当的精度。同时，CD-CTFM在效率方面优于最先进方法。
英文摘要：
Clouds in remote sensing images inevitably affect information extraction, which hinder the following analysis of satellite images. Hence, cloud detection is a necessary preprocessing procedure. However, the existing methods have numerous calculations and parameters. In this letter, a lightweight CNN-Transformer network, CD-CTFM, is proposed to solve the problem. CD-CTFM is based on encoder-decoder architecture and incorporates the attention mechanism. In the decoder part, we utilize a lightweight network combing CNN and Transformer as backbone, which is conducive to extract local and global features simultaneously. Moreover, a lightweight feature pyramid module is designed to fuse multiscale features with contextual information. In the decoder part, we integrate a lightweight channel-spatial attention module into each skip connection between encoder and decoder, extracting low-level features while suppressing irrelevant information without introducing many parameters. Finally, the proposed model is evaluated on two cloud datasets, 38-Cloud and MODIS. The results demonstrate that CD-CTFM achieves comparable accuracy as the state-of-art methods. At the same time, CD-CTFM outperforms state-of-art methods in terms of efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
