Synthetic aperture radar imagery has shown a significant potential especially in maritime surveillance, making SAR-based object detection a prominent research area. However, detecting ships in SAR images remains a challenge due to its complex background interference, densely arranged ship formations, and diverse target types. Faced with these problems, this paper proposes SFONet, a joint spatial-frequency (SF) domain algorithm for multiclass ship-oriented detection. A novel SF attention module is introduced to capture and integrate spatial and frequency domain features effectively. Moreover, with the branch processing and convolutional residual enhancement operations, the extracted information is refined deeply and fused appropriately. Consequently, it can mitigate the impact of complex background interference and preserve critical details for accurate object detection. In case of the angle prediction, the traditional oriented bounding box is mapped to a Gaussian bounding box for target encoding. In the meanwhile, a strategy combining probabilistic intersection-over-union and distribution focal loss enhances the prediction accuracy significantly. Extensive experiments based on SSDD+, HRSID, RSDD, and SRSDD datasets are provided, demonstrating the superior performance of SFONet in both single-class and multiclass ships oriented detection tasks. Ablation studies further validate the key components for improving the accuracy of detection and classification.

Efficient and adaptive mission planning for Earth Observation Satellites (EOSs) remains a challenging task due to the growing complexity of user demands, task constraints, and limited satellite resources. Traditional heuristic and metaheuristic approaches often struggle with scalability and adaptability in dynamic environments. To overcome these limitations, we introduce AEM-D3QN, a novel intelligent task scheduling framework that integrates Graph Neural Networks (GNNs) with an Adaptive Exploration Mechanism-enabled Double Dueling Deep Q-Network (D3QN). This framework constructs a Directed Acyclic Graph (DAG) atlas to represent task dependencies and constraints, leveraging GNNs to extract spatial–temporal task features. These features are then encoded into a reinforcement learning model that dynamically optimizes scheduling policies under multiple resource constraints. The adaptive exploration mechanism improves learning efficiency by balancing exploration and exploitation based on task urgency and satellite status. Extensive experiments conducted under both periodic and emergency planning scenarios demonstrate that AEM-D3QN outperforms state-of-the-art algorithms in scheduling efficiency, response time, and task completion rate. The proposed framework offers a scalable and robust solution for real-time satellite mission planning in complex and dynamic operational environments.

Compared with generic object detection, remote sensing image small object detection typically encounters problems such as information loss, noise characteristic interference, and disturbance of the bounding box. Different from the con ventional method of bounding box regression, we propose a self-learning feature point network (SFPNet) for small object detection, which can captures the geometric information of the object in arbitrary directions as well as enhance the representation of small objects by leveraging feature points. Specifically, a feature points orientation transformation module is proposed to accurately obtain classification and localization information. To improve the learning ability of weak sample objects, a positive and negative sample evaluation and balancing strategy is proposed. Additionally, we introduce a self-learning method of feature points under spatial constraints, which enhances the self-refinement and object expression ability of the feature points while eliminating false alarms. To enhances the training of hard-to-distinguish samples, we propose online feature point hard example mining. Experimental results on three benchmark datasets (including AI-TOD, SODA-A, and DOTA v2) show that the proposed method achieves more than 1.3% improvement in small object detection accuracy at different scales.

Shore-based maritime surveillance radars suffer from systematic range and azimuth errors that degrade target-tracking accuracy. This paper proposes a Vision Transformer (ViT) variant that corrects these errors using Automatic Identification System (AIS) data as the ground truth, modelling nonlinear error patterns via self-attention without requiring explicit physical models of the underlying error sources. Evaluated on the Maritime Target Detection and Tracking (MTDSP) dataset (≈80,000 paired radar-AIS observations), the proposed method reduces range mean absolute error (MAE) by 98.5% (514.76 m → 7.77 m) and azimuth MAE by 89.8% (1.37° → 0.14°) relative to uncalibrated measurements. Controlled experiments isolating architectural components confirm that self-attention, patch embedding, and multi-task learning each contribute measurable gains, particularly in tail-error robustness. These results demonstrate the viability of Transformer-based architectures for correcting radar systematic errors in maritime surveillance.

There are differences in imaging characteristics between infrared and visible light images: visible light images can provide rich texture and color information, but imaging is limited in harsh weather conditions. Infrared images are based on the target’s thermal radiation characteristics and have the ability to resist environmental interference but lack details and background information. Effectively integrating the two can significantly enhance scene understanding ability and improve environmental perception and target recognition performance in applications such as intelligent driving. However, existing fusion methods still face challenges, especially in complex scenes where it is difficult to balance the full preservation of target information with the complete presentation of background details, often resulting in difficulties in extracting differentiated features from different modalities. This article proposes a target detection method based on the visible light infrared fusion diffusion model. This method introduces the Stable Diffusion architecture and designs a target perception spatial fusion weight module that can adaptively generate a spatial fusion weight map based on modal differences. By implementing a multi-stage dynamic fusion strategy, the fusion ratio is automatically adjusted at different diffusion stages. A full-step multi-step prediction mechanism is adopted to improve fusion quality and stability. Compared with existing methods, the method proposed in this article has significant advantages. Experiments on multiple publicly available datasets have shown that this method outperforms existing mainstream methods in key metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and ean Absolute Error (MAE) and also demonstrates good detection performance in downstream tasks for object detection.