Advancements in satellite remote sensing have enhanced Earth observation, enabling the acquisition of images crucial for various remote sensing applications.However, cloud cover degrades image quality and obstructs surface information.Traditional cloud removal techniques struggle to restore both low-level and high-level features, especially under complex conditions.To address these challenges, we propose STGAN-CR, a novel framework integrating Swin Transformer with Generative Adversarial Networks (GANs) to optimize image detail recovery.Leveraging the Swin Transformer's global modeling capabilities, our method enhances feature extraction and restoration, overcoming limitations of conventional models.We introduce a new evaluation metric focused on scene classification accuracy postde-clouding to better assess practical utility.Extensive experiments and ablation studies show that STGAN-CR outperforms existing models in visual quality and classification performance.These advancements offer an effective solution for enhancing the quality and utility of remote sensing images, balancing the restoration of both low-level and high-level features, and providing more meaningful de-clouded images for downstream applications.

In the field of change detection, detecting changes in images with different resolutions is crucial for both long-term interval scenes and scenarios that require rapid detection. However, existing methods face two main issues. Firstly, they require more stringent prior knowledge. The SPM-based methods require pixel-level class labeling of high-resolution(HR) images, while the approaches based on image super-resolution require HR images corresponding to low-resolutionr(LR) images. Such prior knowledge is either costly for labeling or difficult to meet in realistic scenarios. The second is that redundant error accumulation affects detection accuracy. Whether in traditional sub-pixel mapping(SPM) methods or deep learning methods based on image super-resolution, the final detection results are obtained after generating HR images from LR images. The redundant error produced in this step will be accumulated and affect the final detection results. Although the unsupervised methods do not have this problem, the detection accuracy is not as good as that of the supervised. To address these issues, we propose a multi-scale Transformer network(MTN). This model first uses a multi-scale feature extractor(MFE) to extract multi-scale features and perform scale matching at the feature level. Then, the Transformer is used to extract long-range relationships of ground objects on the multi-scale features to enhance the features. Finally, the multi-scale features are fused, and a classifier composed of a convolutional network is used to obtain binary change detection results. In addition, we consider that the edges of objects may be affected during the scale matching process, and introduce a CEBoundary Loss to better detect object edges. The results on the LEVIR and Google datasets demonstrate the effectiveness of our proposed method. The source code of MTN is available at https://github.com/Gavin-debug/MultiResolutionCD.

The automatic segmentation of prostate cancer (PCa) from the multi-modal magnetic resonance imaging (MRI) is of prime importance for the initial staging and prognosis of patients. Nevertheless, the challenge of how to utilize multi-modal image features more efficiently still needs resolving, especially in the segmentation scenario. In this paper, we propose a crossmodal self-attention distillation network that can fully exploit the encoded information of the intermediate layers from different modalities, and the learned attention maps of different modalities are then transferred among modalities to provide significant spatial information with more details incorporated. Furthermore, we propose a novel spatial correlated feature fusion module that is able to learn more complementary correlation and nonlinear information from different modality images. To evaluate the effectiveness of the proposed approach, we conduct extensive experiments on the PCa MRI dataset, and the experiment results demonstrate that our proposed approach could achieve state-of-the-art performance.