When dealing with low-quality source images, existing image fusion methods either fail to handle degradations or are restricted to specific degradations. This study proposes an unsupervised unified degradation-robust image fusion network, termed as URFusion, in which various types of degradations can be uniformly eliminated during the fusion process, leading to high-quality fused images. URFusion is composed of three core modules: intrinsic content extraction, intrinsic content fusion, and appearance representation learning and assignment. It first extracts degradation-free intrinsic content features from images affected by various degradations. These content features then provide feature-level rather than image-level fusion constraints for optimizing the fusion network, effectively eliminating degradation residues and reliance on ground truth. Finally, URFusion learns the appearance representation of images and assigns the statistical appearance representation of high-quality images to the content-fused result, producing the final high-quality fused image. Extensive experiments on multi-exposure image fusion and multi-modal image fusion tasks demonstrate the advantages of URFusion in fusion performance and suppression of multiple types of degradations. The code is available at https://github.com/hanna-xu/URFusion.

Semantic segmentation of high-resolution remote sensing images poses challenges such as scale variability, diverse objects, and obstruction by surface elements. These factors often lead existing methods to suffer from issues like missed and false detections, as well as coarse segmentation boundaries. To tackle these challenges, this article proposes a CNN-transformer complementary and fusion network, termed as CTCFNet. It aims to enhance segmentation accuracy and robustness by extracting and integrating the complementary global and local information from high-resolution remote sensing images. The CTCFNet operates through two primary stages: feature extraction and fusion. In the feature extraction stage, a feature extractor employs convolutional neural network (CNN) and pyramid vision transformer (PVT) blocks to extract both local and global features. A boundary loss is also proposed to improve the segmentation performance for object textures and boundaries. In the feature fusion stage, a feature aggregation module (FAM) is first designed to effectively fuse local and global features at the same scale, facilitating the feature extractor to obtain more comprehensive representations. On this basis, a bi-directional decoder (BiDecoder) reconstructs multiscale features through both top-down and bottom-up directions, resulting in more precise segmentation outputs. Experiments on several high-resolution remote sensing image datasets demonstrate that the proposed method outperforms the state-of-the-art methods in terms of segmentation accuracy and generalization. The code is available athttps://github.com/ChenLu0000/CTCFNet.

Cloud and cloud shadow segmentation is one of the most important issues in remote sensing image processing. Most of the remote sensing images are very complicated. In this work, a dual-branch model composed of Transformer and convolution network is proposed to extract semantic and spatial detail information of the image respectively to solve the problems of false detection and missed detection. To improve the model’s feature extraction, a Mutual Guidance Module is introduced so that the Transformer Branch and the Convolution Branch can guide each other for feature mining. Finally, in view of the problem of rough segmentation boundary, this work uses different features extracted by the Transformer Branch and the Convolution Branch for decoding, and repairs the rough segmentation boundary in the decoding part to make the segmentation boundary clearer. Experimental results on the Landsat-8, Sentinel-2 data, the public dataset HRC_WHU of Wuhan University and the public dataset SPARCS demonstrate the effectiveness of our method and its superiority to existing state-of-the-art Cloud and Cloud Shadow Segmentation approaches.