Thin Cloud Detection in Remote Sensing Images: A Physics-Inspired Class Center Residual Attention Network

High-precision cloud detection is essential for remote sensing applications such as agricultural monitoring and disaster response. However, thin clouds severely limit detection accuracy. The difficulty lies in their semi-transparent nature, which causes their reflected signals to couple with the reflectance of various underlying surfaces. This coupling leads to inconsistent cloud signatures and significant intra-class variability. To address this, we propose a Class Center Residual Attention Network (CCRANet), a radiative transfer theory-inspired framework that employs a class center approach to extract the intrinsic reflective characteristics of thin clouds. Specifically, the core of the network is the Class Center Attention (CCA) module, which extracts invariant intrinsic features of thin clouds, supplemented by the Class Center Residual (CCR) module to eliminate surface-induced interference. Experiments on three public datasets (Landsat-8, CSWV, and CloudS26) show that CCRANet achieves a mean Intersection over Union (mIoU) of 85.93% on the Landsat-8 dataset, outperforming the classic DeeplabV3+ baseline by 10.23 percentage points. In particular, it achieves 22.58 percentage point improvement in thin cloud IoU over DeeplabV3+ in snow/ice scenarios, significantly reducing false positive detections caused by surface spectral similarity.