Ship detection in remote sensing images plays a crucial role in various applications and has drawn increasing attention in recent years. However, existing arbitrary-oriented ship detection methods are generally developed on a set of predefined rotated anchor boxes. These predefined boxes not only lead to inaccurate angle predictions but also introduce extra hyperparameters and high computational cost. Moreover, the prior knowledge of ship size has not been fully exploited by existing methods, which hinders the improvement of their detection accuracy. Aiming at solving the above issues, in this article, we propose a center-head point extraction-based detector (CHPDet) to achieve arbitrary-oriented ship detection in remote sensing images. Our CHPDet formulates arbitrary-oriented ships as rotated boxes with head points that are used to determine the direction. Also, a rotated Gaussian kernel is used to map the annotations into target heatmaps. Keypoint estimation is performed to find the center of ships. Then, the size and head point of the ships are regressed. The orientation-invariant model (OIM) is also used to produce orientation-invariant feature maps. Finally, we use the target size as prior to fine-tune the results. Moreover, we introduce a new dataset for multiclass arbitrary-oriented ship detection in remote sensing images at a fixed ground sample distance (GSD) that is named FGSD2021. Experimental results on FGSD2021 and two other widely used datasets, i.e., HRSC2016 and UCAS-AOD, demonstrate that our CHPDet achieves the state-of-the-art performance and can well distinguish between bow and stern. Code and FGSD2021 dataset are available at https://github.com/zf020114/CHPDet .

Rotated object detection in aerial images has received increasing attention for a wide range of applications. However, it is also a challenging task due to the huge variations of scale, rotation, aspect ratio, and densely arranged targets. Most existing methods heavily rely on a large number of predefined anchors with different scales, angles, and aspect ratios, and are optimized with a distance loss. Therefore, these methods are sensitive to anchor hyperparameters and easily suffer from performance degradation caused by boundary discontinuity. To handle this problem, in this letter, we propose a dense anchor-free rotated object detector (DARDet) for rotated object detection in aerial images. Our DARDet directly predicts five parameters of rotated boxes at each foreground pixel of feature maps. We design a new alignment convolution module (ACM) to extract aligned features and introduce a pixels-intersection over union (PIoU) loss for precise and stable regression. Our method achieves state-of-the-art performance on three commonly used aerial objects datasets (i.e., DOTA, HRSC2016, and UCAS-AOD) while keeping high efficiency. Code is available at https://github.com/zf020114/DARDet .

Recently, impressive improvements have been achieved in general object detection. However, tiny object detection remains a very challenging problem since tiny objects only occupy a few pixels. Consequently, the label assignment strategies used in general object detectors are not suitable for tiny object detection, because these algorithms tend to assign few or even no positive samples for tiny objects. In this article, we propose a simple yet effective Gaussian assignment (GA) strategy to solve this problem. Specifically, we first model the bounding boxes as 2-D Gaussian distributions and then encode training samples with a threshold. This strategy can assign more high-quality positive samples for tiny objects and adjust the weight of positive samples to balance the contribution from different-size objects. Extensive experiments on four tiny object detection datasets show that the proposed strategy significantly and consistently improves the performance of single-stage tiny object detectors. In particular, with our strategy, we bridge the performance gap between single-stage and state-of-the-art multistage detectors on the AI-TOD dataset (24.2% versus 24.8% in mAP) while maintaining the inference speed. The code is available at https://github.com/zf020114/GaussianAssignment .

A structured light scanner is mainly used to collect three-dimensional (3D) data of an object; however, the uncertainty evaluation of a specific measurement task is not perfect. This study discusses the source of the uncertainty component of the measurement system of a structured blue-light scanner, including the instrument itself, data acquisition, data processing, and other factors. Using the surface profile as the evaluation object, PolyWorks software was used to perform the data processing of the point cloud denoising, splicing, and registration. An uncertainty evaluation is performed based on the black-box evaluation model. The experimental results show that the uncertainty components introduced by the point cloud stitching and point cloud registration are the main components of the entire measurement system. The bootstrap method does not need to consider the probability distribution type of the error sources, which improves computational efficiency. This method has several advantages when the distribution state is unknown. This work is conducive to improving the task-oriented uncertainty evaluation of 3D scanners.

Multi-object tracking (MOT) in satellite videos presents significant challenges, including small target sizes, dense distributions, and complex motion patterns. To address these issues, we propose OITrack, an improved tracking framework that integrates a Trajectory Completion Module (TCM), an Adaptive Kalman Filter (AKF), and an Iterative Expansion Intersection over Union Strategy (I-EIoU) strategy. Specifically, TCM enhances temporal continuity by compensating for missing trajectories, AKF improves tracking robustness by dynamically adjusting observation noise, and I-EIoU optimizes target association, leading to more accurate small-object matching. Experimental evaluations on the VIdeo Satellite Objects (VISO) dataset demonstrated that OITrack outperforms existing MOT methods across multiple key metrics, achieving a Multiple Object Tracking Accuracy (MOTA) of 57.0%, an Identity F1 Score (IDF1) of 67.5%, a reduction in False Negatives (FN) to 29,170, and a decrease in Identity Switches (ID switches) to 889. These results indicate that our method effectively improves tracking accuracy while minimizing identity mismatches, enhancing overall robustness.

Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks (i.e., target detection and tracking). We then propose a new non-dominated sorting genetic algorithm (NSGA) to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.