Issues such as insufficient feature representation and background confusion make detection tasks for small object in remote sensing arduous. Particularly when the algorithm will be deployed on board for real-time processing, which requires extensive optimization of accuracy and speed under limited computing resources. To tackle these problems, an efficient detector called FFCA-YOLO(Feature enhancement, Fusion and Context Aware YOLO) is proposed in this paper. FFCA-YOLO includes three innovative lightweight and plug-and-play modules: feature enhancement module(FEM), feature fusion module(FFM) and spatial context aware module(SCAM). These three modules improve the network capabilities of local area awareness, multi-scale feature fusion and global association cross channels and space, respectively, while trying to avoid increasing complexity as possible. Thus the weak feature representations of small objects are enhanced and the confusable backgrounds are suppressed. Two public remote sensing datasets(VEDAI and AI-TOD) for small object detection and one self-built dataset(USOD) are used to validate the effectiveness of FFCA-YOLO. The accuracy of FFCA-YOLO reaches 0.748, 0.617 and 0.909(in terms of mAP ₅₀ ) that exceeds several benchmark models and state-of-the-art methods. Meanwhile, the robustness of FFCA-YOLO is also validated under different simulated degradation conditions. Moreover, to further reduce computational resource consumption while ensuring efficiency, a lite version of FFCA-YOLO(L-FFCA-YOLO) is optimized by reconstructing the backbone and neck of FFCA-YOLO based on partial convolution. L-FFCA-YOLO has faster speed, smaller parameter scale, lower computing power requirement but little accuracy loss compared with FFCA-YOLO. The source code will be available at https://github.com/yemu1138178251/FFCA-YOLO.

Multimodal remote sensing images provide complementary information, enhancing the effectiveness of object detection tasks in open-world scenarios. To address the imbalance of information richness between modalities in multimodal object detection, we propose a simple yet effective multi-source image object detection method (DKDNet). Our contributions are twofold: (a) we introduce diffusion deformation convolution (DDConv), which combines deformation convolution with adaptive long-range receptive fields to further enhance the ability to perceive object pose variations and capture distant information. (b) We propose the bidirectional feature distillation and information complementary fusion network (BDFusion), where different modalities exchange information through a knowledge distillation strategy, explicitly enhancing the information interaction between modalities. Finally, we adaptively build spatial domain complementarity between different modalities via self-correction, revealing implicit correlations. Experimental results on the publicly available vehicle detection in aerial imagery (VEDAI) dataset and the optical and SAR ship detection dataset (OSSDD), collected in the Suez Canal region, demonstrate that our proposed method achieves superior performance with acceptable inference time, making it suitable for various realworld scenarios.

Since polarimetric synthetic aperture radar (PolSAR) terrain segmentation is a dense prediction task, the disadvantage of inadequate labeled samples greatly limits its performance. In this paper, we present a semi-supervised segmentation network called SemiPSCN to reduce the data reliance on label annotation, which integrates semi-supervised learning paradigm and the characteristics of PolSAR data into a unified architecture. First, considering the unreliability of pseudo-labels caused by noise interference in PolSAR data, a pseudo-label error localization (PEL) module is designed. By mapping the pixels that have mispredictions in pseudo-labels, PEL can greatly enhance the confidence of pseudo-labels. Then, SemiPSCN introduces a category representation constraint (CRC) module to explicitly boost the category consistency between labeled and unlabeled PolSAR data. Via explicit intra-category and inter-category constraints, CRC can guarantee the invariant representations on the same category region between labeled and unlabeled data. Furthermore, a region consistency constraint (RCC) module is designed to enhance the regional consistency in PolSAR data. RCC leverages the conception of graph to model the understanding of spatial relations among terrain targets, thereby facilitating consistent spatial region expression in semi-supervised process. Finally, we build a challenging large-scale dataset called LSPolSAR-Seg and conduct abundant experiments on LSPolSAR-Seg. SemiPSCN exhibits superior performance when compared to other advanced approaches, especially improving mIoU by 3.44% 12.77% under 20% split setting, which promotes the performance to a state-of-the-art level.

In this paper, the dual spacecraft pursuit-evasion game problem under incomplete information is investigated, and a strategy-solving method for the incomplete information pursuit-evasion game based on particle swarm optimization and unscented particle filter (PSO-UPF) estimation is proposed. The completeness of the information available about the target’s cost function, which is determined by the weighting information, has a significant impact on the success of the pursuing strategy. For the cost function is unknown in incomplete information scenarios, a research framework of the pursuit-evasion game based on following observation and one-sided pursuit two stages is established. Besides, to describe the more accurate motion of the spacecraft, a Schweighart-Sedwick (SS) dynamic model is introduced that considers the effect of $J_{2}$ perturbation. Firstly, an equilibrium strategy for the SS model-based pursuit-evasion problem is derived under complete information. Next, for the incomplete information scenarios, an estimation method based on PSO-UPF of weight matrix information is established, which allows the cost function to be determined by the estimation method in the observation stage. Then, the pursuit strategy is re-designed in the one-sided pursuit stage based on the estimated cost function. Finally, the performance of the proposed method is validated by simulation. The results demonstrate that the approach can achieve good performance by efficiently estimating the weight information in the opponent’s cost function.