Single remote sensing image dehazing aims to eliminate atmospheric scattering effects without auxiliary information. It serves as a crucial preprocessing step for enhancing the performance of downstream tasks in remote sensing images. Conventional approaches often struggle to balance haze removal and detail restoration under non-uniform haze distributions. To address this issue, we propose a Dual-domain Feature Fusion Network (DFFNet) for remote sensing image dehazing. DFFNet consists of two specialized units: the Frequency Restore Unit (FRU) and the Context Extract Unit (CEU). As haze primarily manifests as low-frequency energy in the frequency domain, the FRU effectively suppresses haze across the entire image by adaptively modulating low-frequency amplitudes. Meanwhile, to reconstruct details attenuated due to dense haze occlusion, we introduce the CEU. This unit extracts multi-scale spatial features to capture contextual information, providing structural guidance for detail reconstruction. Furthermore, we introduce the Dual-Domain Feature Fusion Module (DDFFM) to establish dependencies between features from FRU and CEU via a designed attention mechanism. This leverages spatial contextual information to guide detail reconstruction during frequency domain haze removal. Experiments on the StateHaze1k, RICE and RRSHID datasets demonstrate that DFFNet achieves competitive performance in both visual quality and quantitative metrics.

The advancement of remote sensing technology and high-resolution sensors has led to a significant improvement in the spatial resolution of remote sensing images, resulting in the capture of more abundant image texture details. This has created a robust data foundation for remote sensing image interpretation. Semantic segmentation is a critical technology in remote sensing image interpretation, as it enables pixel-by-pixel classification of objects in images. Due to its advanced classification capabilities, semantic segmentation has found wide application in many fields, such as landslide monitoring,^1,2 plant disease monitoring,³ and water quality monitoring,⁴ in the field of disaster monitoring. In urban planning, it has been utilized for road detection,⁵ building change detection,⁶ illegal building detection,⁷ 3D scene reconstruction.^8,9 In the field of land cover mapping,¹⁰ it has been used to detect forest land, agricultural land, bare land, irrigated land, and dry land. As the classification of ground objects becomes increasingly refined, the demand for more advanced and sophisticated classification and segmentation algorithms has significantly increased.

High-resolution remote sensing image shadow detection has wide applications in target recognition, land information retrieval, and other fields. However, current shadow detection technologies still face challenges, including shadow omission and difficulty in defining boundaries. To address these challenges, this paper proposes a shadow detection method based on a Multi-Branch Channel-Spatial Attention Network (MBCSANet), which combines the Multi-Branch Feature Aggregation Module (MFAM) and the Channel-Spatial Parallel Attention Feature Fusion Module (C-SPAFFM). The MFAM effectively integrates shadow information at different scales, reducing missed detections caused by changes in shadow size and shape. The CSPAFFM enhances channel information to highlight boundary features and optimizes spatial information to more accurately capture spatial variations in shadows, thereby further reducing the possibility of missed detections. The effectiveness of the proposed method was validated on the public dataset AISD and the self-constructed satellite image dataset SISD. On the AISD dataset, the F1-score, OA, IOU, and BER metrics were 93.76%, 97.36%, 88.33%, and 4.19%, respectively. On the SISD dataset, these metrics reached 91.37%, 94.91%, 84.25%, and 6.87%. Experimental results show that the proposed method performs well in shadow detection tasks for high-resolution remote sensing images.

Detecting small objects in remote sensing images is challenging due to their size, which results in limited distinctive features. This limitation necessitates the effective use of contextual information for accurate identification. Many existing methods often struggle because they do not dynamically adjust the contextual scope based on the specific characteristics of each target. To address this issue and improve the detection performance of small objects (typically defined as objects with a bounding box area of less than 1024 pixels), we propose a novel backbone network called the Dynamic Context Branch Attention Network (DCBANet). We present the Dynamic Context Scale-Aware (DCSA) Block, which utilizes a multi-branch architecture to generate features with diverse receptive fields. Within each branch, a Context Adaptive Selection Module (CASM) dynamically weights information, allowing the model to focus on the most relevant context. To further enhance performance, we introduce an Efficient Branch Attention (EBA) module that adaptively reweights the parallel branches, prioritizing the most discriminative ones. Finally, to ensure computational efficiency, we design a Dual-Gated Feedforward Network (DGFFN), a lightweight yet powerful replacement for standard FFNs. Extensive experiments conducted on four public remote sensing datasets demonstrate that the DCBANet achieves impressive mAP@0.5 scores of 80.79% on DOTA, 89.17% on NWPU VHR-10, 80.27% on SIMD, and a remarkable 42.4% mAP@0.5:0.95 on the specialized small object benchmark AI-TOD. These results surpass RetinaNet, YOLOF, FCOS, Faster R-CNN, Dynamic R-CNN, SKNet, and Cascade R-CNN, highlighting its effectiveness in detecting small objects in remote sensing images. However, there remains potential for further improvement in multi-scale and weak target detection. Future work will integrate local and global context to enhance multi-scale object detection performance.

Multi-label feature selection (MFS) is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels. However, traditional centralized methods face significant challenges in privacy-sensitive and distributed settings, often neglecting label dependencies and suffering from low computational efficiency. To address these issues, we introduce a novel framework, Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization (DHBCPSO-MSR). Leveraging the federated learning paradigm, Fed-MFSDHBCPSO allows clients to perform local feature selection (FS) using DHBCPSO-MSR. Locally selected feature subsets are encrypted with differential privacy (DP) and transmitted to a central server, where they are securely aggregated and refined through secure multi-party computation (SMPC) until global convergence is achieved. Within each client, DHBCPSO-MSR employs a dual-layer FS strategy. The inner layer constructs sample and label similarity graphs, generates Laplacian matrices to capture the manifold structure between samples and labels, and applies -norm regularization to sparsify the feature subset, yielding an optimized feature weight matrix. The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset. The updated weight matrix is then fed back to the inner layer for further optimization. Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.