Accurate semantic segmentation of urban remote sensing images (URSIs) is essential for urban planning and environmental monitoring. However, it remains challenging due to the subtle texture differences and similar spatial structures among geospatial objects, which cause semantic ambiguity and misclassification. Additional complexities arise from irregular object shapes, blurred boundaries, and overlapping spatial distributions of objects, resulting in diverse and intricate edge morphologies. To address these issues, we propose TEFormer, a texture-aware and edge-guided Transformer. Our model features a texture-aware module (TaM) in the encoder to capture fine-grained texture distinctions between visually similar categories, thereby enhancing semantic discrimination. The decoder incorporates an edge-guided tri-branch decoder (Eg3Head) to preserve local edges and details while maintaining multiscale context-awareness. Finally, an edge-guided feature fusion module (EgFFM) effectively integrates contextual, detail, and edge information to achieve refined semantic segmentation. Extensive evaluation demonstrates that TEFormer yields mIoU scores of 88.57% on Potsdam and 81.46% on Vaihingen, exceeding the next best methods by 0.73% and 0.22%. On the LoveDA dataset, it secures the second position with an overall mIoU of 53.55%, trailing the optimal performance by a narrow margin of 0.19%.

Mamba has gained significant attention for its outstanding long-range context modeling capability while maintaining linear complexity, compared with Transformer. In this article, a hybrid Mamba and convolutional neural network (CNN) architecture is proposed for remote sensing image change detection (RSICD), named Hybrid-MambaCD, which leverages the advantages of CNN for local detail information extraction and Mamba for global context information extraction, providing an efficient solution for RSICD tasks. First, the region-channel attention mechanism (RCAM) is designed to enhance the CNN features from both channel and region dimensions, enabling the network to focus more on change regions while suppressing interference from background areas. Second, an iterative global-local feature fusion (IGLFF) strategy is proposed, which performs an adaptive weighted fusion of global and local features across multiple scales in a progressive manner, enhancing the representation ability of the features. Experimental results on three public datasets of LEVIR-CD, WHU-CD, and DSIFN-CD show that compared to the existing RSICD methods, the proposed Hybrid-MambaCD achieves the state-of-the-art (SOTA) detection performance.

Synthetic aperture radar (SAR) images are pivotal in remote sensing applications. However, due to the physical characteristics of coherent imaging, current SAR image compression methods are susceptible to speckle noise, leading to distortion and higher compression rates. To address these issues, we propose a content-adaptive transformation network that dynamically adjusts the receptive field size based on image content, thereby mitigating noise impact and capturing detailed features more effectively. In addition, we developed a context-aware entropy model (CAEM) to better explore channel correlations within the latent feature space, which helps to reduce redundancy in latent features. Experimental results demonstrate that our method achieves state-of-the-art performance compared to traditional image compression standards and deep learning-based models, significantly enhancing the compression ratio and image reconstruction quality for the Sandia and ICEYE datasets.

Colon polyps in colonoscopy images exhibit significant differences in color, size, shape, appearance, and location, posing significant challenges to accurate polyp segmentation. In this paper, a Weighted Dual-branch Feature Fusion Network is proposed for Polyp Segmentation, named WDFF-Net, which adopts HarDNet68 as the backbone network. First, a dual-branch feature fusion network architecture is constructed, which includes a shared feature extractor and two feature fusion branches, i.e. Progressive Feature Fusion (PFF) branch and Scale-aware Feature Fusion (SFF) branch. The branches fuse the deep features of multiple layers for different purposes and with different fusion ways. The PFF branch is to address the under-segmentation or over-segmentation problems of flat polyps with low-edge contrast by iteratively fusing the features from low, medium, and high layers. The SFF branch is to tackle the the problem of drastic variations in polyp size and shape, especially the missed segmentation problem for small polyps. These two branches are complementary and play different roles, in improving segmentation accuracy. Second, an Object-aware Attention Mechanism (OAM) is proposed to enhance the features of the target regions and suppress those of the background regions, to interfere with the segmentation performance. Third, a weighted dual-branch the segmentation loss function is specifically designed, which dynamically assigns the weight factors of the loss functions for two branches to optimize their collaborative training. Experimental results on five public colon polyp datasets demonstrate that, the proposed WDFF-Net can achieve a superior segmentation performance with lower model complexity and faster inference speed, while maintaining good generalization ability.