The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. It addresses issues such as small object size, complex backgrounds, scale variation, and dense object distributions by incorporating three core modules: dynamic-cooperative multimodal fusion architecture (DyCoMF-Arch), multiscale wavelet-enhanced aggregation network (MWA-Net), and spatial-deformable dynamic enhancement module (SDDE-Module). DyCoMF-Arch builds a hierarchical feature pyramid using multistage spatial compression and expansion, with dynamic weight allocation to extract salient features. MWA-Net applies wavelet-transform-based convolution to decompose features, preserving high-frequency detail and enhancing representation of small-scale objects. SDDE-Module integrates spatial coordinate encoding and multidirectional convolution to reduce localization interference and overcome fixed sampling limitations for geometric deformations. Experiments on the NWPU VHR-10 and DIOR datasets show that HAF-YOLO achieved mAP50 scores of 85.0% and 78.1%, improving on YOLOv8 by 4.8% and 3.1%, respectively. HAF-YOLO also maintained a low computational cost of 11.8 GFLOPs, outperforming other YOLO models. Ablation studies validated the effectiveness of each module and their combined optimization. This study presents a novel approach for remote-sensing object detection, with theoretical and practical value.

The technology for target recognition in remote sensing satellite images is widely applied in daily life, and research on detecting and recognizing targets in remote sensing images holds significant academic and practical importance. To address the challenges of extreme scale variations, dense target distributions, and low‐resolution artefacts in remote sensing images, this paper proposes a new multi‐object detection network based on the YOLOv8 architecture—MD‐YOLOv8. The main contributions of this paper are threefold: (1) the design of the multi‐frequency attention downsampling module, which integrates the ADown module with Haar wavelet transforms and pixel attention; (2) the proposal of the adaptive attention network (DMAA) module, an enhanced multiscale feature extractor based on the multiscale feature extraction attention mechanism; (3) the integration of both modules into the YOLOv8 backbone to achieve superior performance in remote sensing image detection. Based on the DOTA‐1.0 dataset for training, experimental results show that the MD‐YOLOv8 network achieves improvements in precision, recall rate, and mAP@0.5, reaching 82.69%, 78.28%, and 82.05%, respectively; these represent increases of 3.76%, 3.43%, and 4.37% compared to the original model. In practical image detection, MD‐YOLOv8 demonstrates higher recognition quality and can flexibly respond to various target types. The MD‐YOLOv8 network effectively meets the accuracy requirements for target detection in remote sensing satellite images.

Remote sensing image target recognition plays an important role in environmental monitoring, disaster management, urban planning, and traffic systems. To address issues such as high missing detection rates of dense small targets and insufficient multi‐scale feature interaction in remote sensing image target detection, an improved PAP‐YOLOv8 network architecture was proposed in this study based on YOLOv8. This framework included three core innovations: (1) the pixel dynamic convolution attention module (PDCA), which enhanced small target detection capabilities in complex backgrounds through direction‐sensitive convolution and a dual‐stage attention mechanism; (2) the adaptive channel attention network (ACAN), which optimized multi‐scale feature fusion and suppressed background interference via dynamic weight allocation and channel recalibration; (3) the pyramidal wavelet attention fusion block (PWAFB), which synergistically integrated wavelet multi‐resolution analysis with composite attention mechanisms to model spatial details and channel relationships in remote sensing images through a collaborative decomposition and reconstruction workflow. Experiments on the Detection in Optical Remote Sensing (DIOR) dataset showed that the mAP@0.5 of PAP‐YOLOv8 reached 86.9%, an increase of 5.3% over the baseline YOLOv8, while demonstrating enhanced detection capability for small and multi‐scale targets. Compared with other detection models, PAP‐YOLOv8 achieves a balanced trade‐off between network size and detection capability, demonstrating superior overall performance with an F1 score of 83.1%. This study confirms the potential of PAP‐YOLOv8 for satellite monitoring applications and provides a valuable reference for improving target detection performance in optical remote sensing satellite imagery.

This study addresses key challenges in obtaining reliable infrared data for maritime ship observation and limitations of existing models, such as simplified reflectance assumptions and incomplete multi-band coverage. To improve modeling accuracy and computational efficiency, a high-precision Bidirectional Reflectance and Pseudo-random Vector Enhanced Reverse Monte Carlo Method (BP-ERMCM) is developed. By combining the Bidirectional Reflectance Distribution Function (BRDF), pseudo-random vector approaches, and improved ray-tracking algorithms with precomputed thermal radiation and MODTRAN’s atmospheric transfer model, BP-ERMCM provides multi-view infrared characteristic simulations across 3–5 μm and 8–12 μm bands. Simulations using a 3D ship model with 191 viewpoints reveal seasonal sensitivity, with summer peak intensity at 9.8 μm being 39.3% higher than in winter, and viewpoint dependency showing oblique overhead radiation 5.65 times greater than that from bow angles. Long-wave contours enhance target distinction, while mid-wave regions are dominated by reflection, increasing intensity at 3.8 μm by 56.1–85.7%. These findings highlight BP-ERMCM’s potential to inform infrared signature database construction, detector optimization, and maritime observation strategies. The findings underscore BP-ERMCM’s capability to enhance efficiency and accuracy, providing valuable insights for infrared databases, sensor selection, and maritime observation strategies, thereby advancing infrared signature analysis in maritime applications.

Unmanned surface vehicle (USV) swarms operating in communication-denied maritime environments face degraded formation control when inter-agent state exchange is disrupted. This paper presents a three-layer control architecture integrating (1) LLM-assisted strategic mission planning with formal safety verification, (2) predictive tactical coordination combining physics-based motion extrapolation with online-learned neighbor behavior models, and (3) DMPC + ADMM execution for constrained formation control. The predictive coordination module in simulation-based evaluation across five representative scenarios reduces formation error by 76.0% (under simulation conditions) during 60 s communication outages compared to zero-hold prediction (Cohen d = 0.88, p < 0.001, DMPC + ADMM validated). The adaptive topology manager dynamically selects among star, mesh, and tree configurations via a utility function balancing communication quality, threat exposure, and overhead, reducing communication overhead by 71.7% (for the particular cases investigated) under the evaluated conditions while maintaining formation accuracy. Stability analysis using multiple Lyapunov functions and average dwell time theory guarantees global uniform exponential stability under topology switching, with explicit error bounds under communication denial derived via Gronwall-type arguments. The framework is validated through 1250 simulation trials across five scenarios with rigorous statistical analysis. The three-layer temporal decoupling architecture provides a practical template for safely integrating LLM-assisted planning with real-time multi-agent control in contested environments.

Unmanned Surface Vehicle (USV) clusters operating in maritime environments face dynamic communication conditions, including varying sea states, electromagnetic interference, and satellite denial, that render static communication topologies suboptimal. Existing approaches assess link quality through single indicators, typically the SNR, and lack mechanisms for automatic topology adaptation. This paper presents a multi-layer adaptive communication framework that achieves a mean communication quality score of 0.72 (vs. 0.51–0.66 for baselines), a message delivery rate of 94.1% under benign conditions, and a failure recovery time of 3.2 s (vs. 5.8–8.4 s for baselines) across five communication failure scenarios. The framework integrates three layers: a weighted multi-indicator communication quality metric fusing the SNR, packet loss rate, latency, and link stability into a unified score; a topology utility function that selects among centralized, distributed, and hierarchical topologies by optimizing a quality–threat–overhead objective; and a multi-modal backup communication manager with physics-based underwater acoustic, optical line-of-sight, and multi-hop relay fallback modes. Simulation results demonstrate consistent improvements over single-indicator and static-topology baselines, with particularly strong performance under satellite denial and jamming scenarios where multi-modal backup communication sustains delivery rates above 85% under simulated conditions. In summary, the framework demonstrates consistent improvements across all metrics (communication quality, delivery rate, recovery time) relative to four baselines, with the largest gains observed under the most challenging conditions (satellite denial and jamming). We emphasize that the framework adaptively selects among pre-defined canonical topologies (star, mesh, tree) based on real-time conditions rather than synthesizing optimal topologies de novo—a distinction between topology management and topology optimization.