Thin cloud contamination in remote sensing images presents a significant challenge affecting data quality, resulting in imprecise analysis and interpretation across applications including land cover classification, environmental monitoring, and disaster assessment. Conventional thin cloud removal methods typically depend on feature extraction at a single scale and inadequately capture the multi-scale characteristics of clouds, leading to suboptimal declouding results. Furthermore, deep learning-based approaches, particularly those utilizing generative adversarial network (GAN), frequently encounter detail loss and texture blur in generated images and demonstrate limited capability in modeling local features accurately. To address these challenges, this study introduces a novel GAN-based method incorporating a convolutional block attention module (CBAM) and a multi-scale attention mechanism. The proposed approach aims to enhance the accuracy of thin cloud removal while maintaining the spectral and spatial details of the original imagery, thus improving the overall quality of remote sensing data.

The proposed framework integrates the GAN architecture with CBAM and multi-scale attention mechanism for effective thin cloud removal. The generator network is engineered to capture global and local features of the input image, enabling the model to restore detailed surface information while removing thin clouds effectively. The discriminator network assesses the authenticity of the generated image, ensuring high similarity to the real cloud-free image. The multi-scale attention mechanism serves a crucial function by implementing parallel convolution branches with independent parameter optimization strategies. This approach enables differentiated feature expression, enhancing the model’s capacity to process cloud contamination and underlying surface features at various scales. Furthermore, CBAM is integrated for enhanced feature extraction at different scales. CBAM applies sequential channel and spatial attention to feature maps, adaptively emphasizing important features while suppressing irrelevant noise. This integration of multi-scale attention and CBAM substantially improves the model’s capability to restore image brightness and recover fine details. Comprehensive experiments were conducted on the RICE1 dataset and a custom remote sensing cloud removal dataset based on Sentinel-2 imagery. The model’s performance is evaluated using quantitative metrics including peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed method is compared against several state-of-the-art thin cloud removal techniques, including Haze Removal, FFA-Net, C²PNet, CGAN, and SpA-GAN, to demonstrate its effectiveness.

Experimental results show that the proposed method surpasses traditional thin cloud removal techniques in both visual quality and quantitative metrics. The integration of CBAM with the multi-scale attention mechanism substantially enhances the model’s ability to recover detailed surface information while effectively removing thin clouds (Figs. 8?11). Comparative analysis reveals that the proposed method achieves a PSNR of 31.321 dB and an SSIM of 0.894, exceeding the performance of state-of-the-art methods (Tables 1 and 2). The generated images are further analyzed based on the average brightness of the RGB channels (Figs. 11 and 12). The results indicate that the cloud-free images generated by the proposed method most closely match the real images in terms of RGB channel brightness, validating the method’s effectiveness in preserving spectral details. An ablation study examines the synergistic contribution of the two attention mechanisms (Table 4 and Fig. 14). The results confirm that their combination significantly enhances model performance, demonstrating their complementary role in improving image quality. Specifically, the multi-scale attention mechanism facilitates feature capture at different scales, while CBAM enhances feature extraction accuracy through channel and spatial dimension focus.

This study presents a novel method for thin cloud removal from remote sensing images based on GAN enhanced with CBAM and multi-scale attention mechanism. The proposed approach enhances cloud removal accuracy while preserving the spectral and spatial details of the original image. Experimental results validate the effectiveness and robustness of the proposed method, demonstrating its superior performance compared to state-of-the-art techniques in terms of visual quality and quantitative metrics. The integration of CBAM and multi-scale attention mechanism proves instrumental in achieving these results, underscoring their significance in enhancing model performance. The proposed method offers a promising solution for improving remote sensing data quality. Future research will concentrate on optimizing the model architecture and expanding its applicability to additional types of cloud pollution and remote sensing datasets.