Gang Zhou

Remote sensing binary change detection aims to identify real changes in bi-temporal images while suppressing pseudo-change responses induced by complex backgrounds and temporal variations in otherwise unchanged regions. Although existing state-space-based methods incorporate cross-temporal interaction, integrating explicit difference priors into state-space parameter generation and subsequent boundary-aware decoding remains an important design problem. To address this problem, CA-Mamba, a change-aware Mamba decoding framework, is proposed. An Asymmetric Change-Aware State-Space Mixer (ACSSM) is introduced to condition state-space parameter generation on bi-temporal difference priors and to modulate the two temporal branches asymmetrically. The Cascaded Difference Prior Module (CDPM), the Change Boundary Enhancement (CBE) module, and the Change Representation Constructor (CRC) are further integrated into a unified pipeline for difference-prior generation and propagation, change-aware mixing, boundary enhancement, and region–boundary collaborative representation construction. Experiments on four public datasets, namely, LEVIR-CD, WHU-CD, CDD, and CLCD, show competitive detection performance. Ablation results support the use of signed-difference pathways and the overall CRC design under the evaluated settings. The efficiency analysis characterizes the trade-offs among detection accuracy, reported computational cost, inference latency, and parameter storage.

High-resolution remote sensing semantic segmentation using RGB imagery and digital surface models (DSM) remains challenging because of insufficient shallow cross-modal fusion and inadequate coordination between global semantics and local spatial information. To address these issues, we propose MHBA-TransUNet, a hybrid encoder–decoder network for RGB–DSM semantic segmentation. In the encoder, a Shallow Cross-modal Feature Fusion Module (SCFFM) progressively integrates complementary RGB and DSM features through dynamic spatial recalibration, while a lightweight dual-stream Transformer with a self-attention–cross-attention–self-attention (SA–CA–SA) configuration models intra-modal context and bidirectional cross-modal interactions. During decoding, a Global–Local Alignment Module (GLAM) combines Block-Distance Linear Attention (BDLA), feature-wise modulation, and channel reweighting to coordinate deep semantic features with shallow spatial details. Experiments on the ISPRS Vaihingen, ISPRS Potsdam, and US3D datasets achieve mIoU scores of 85.15%, 88.01%, and 85.49%, respectively, outperforming representative state-of-the-art methods. Ablation studies further verify the effectiveness and complementarity of SCFFM and GLAM, while the US3D results demonstrate stable performance across diverse urban scenes.

Small object detection in remote sensing imagery is inherently constrained by several persistent factors within the conventional spatial domain. On one hand, the small proportion of tiny targets in the entire image results in sparse spatial feature representations that are easily submerged by complex background noise. On the other hand, effectively capturing long-range dependencies in dense-object scenarios remains difficult, which limits the model's capacity for global context modeling. Collectively, these factors represent significant performance bottlenecks for existing spatial-based object detection algorithms. To remedy these shortcomings, TSF-DETR, a novel detection framework anchored by a Two-Stage Frequency-Domain Enhancement strategy, is proposed in this study. Specifically, a Remote Sensing Frequency Enhancement Module (RSEM) is integrated within the backbone as the first stage to implement a hierarchical differentiated mechanism for frequency-domain feature refinement. In the subsequent neck stage, a Multi-Scale Frequency Domain (MSFD) module is leveraged to execute multi-scale and multi-orientational Gabor filtering, establishing a robust “Global FFT Enhancement – Local Gabor Optimization” synergy to suppress background noise. Furthermore, to fortify the model's global perception, a tailored Linear Multi-Head Self-Attention (LMSA) mechanism is incorporated. Distinguishable from standard linear attention designs, this modified LMSA enhances the model's ability to capture long-range dependencies and aggregate global semantic context, thereby improving detection precision while maintaining high computational efficiency. Extensive evaluations on four benchmark datasets—VisDrone2019, DIOR, RSOD and USOD—demonstrate that TSF-DETR outperforms the RT-DETR baseline and several state-of-the-art methods, validating its efficacy in strengthening small object representations and enhancing detection accuracy across diverse remote sensing scenarios.