As a significant and fundamental task in the remote sensing field, object detection has received increasing attention and research studies. However, geospatial object detection is still a challenge owing to the dramatic variation in object scales, intraclass differences, and interclass similarity from multiscale and multiclass objects. To deal with these problems, an end-to-end feature-reflowing pyramid network (FRPNet) is proposed in this letter. FRPNet has two advantages that contribute to improve object detection accuracy. First, we embed a nonlocal block into the backbone in order to get the relevancy between different regions of the geospatial image for obtaining discriminative features. Furthermore, a feature-reflowing pyramid structure is proposed to generate high-quality feature presentation for each scale through fusing fine-grained features from the adjacent lower level, which improves the detection capability for multiscale and multiclass objects. Experiments on a public remote sensing data set DIOR illustrate that FRPNet can significantly improve the performance when compared to several state-of-the-art detection approaches in terms of mean average precision (mAP).

With practical applications such as environment surveillance, agricultural production, and disaster assessment, accurate object detection in remote sensing images is in high demand. Precise detection of object instances in remote sensing images remains considerably challenging due to dense instance stacking, large-scale variations, and complex backgrounds. To solve the mentioned issues, a novel global context-weaving network (GCWNet) is developed for object detection in remote sensing images. We propose two novel modules for feature extraction and refinement, which include the global context aggregation module (GCAM) and the feature refinement module (FRM). GCAM assembles a global context with high-level and low-level features through feature weaving, which facilitates dense object detection. Meanwhile, FRM convolves multiple receptive fields by combining different branches, thereby further refining the features and improving the feature distinction at different scales. Furthermore, we design to alleviate the sample imbalanced problem during training using focal loss and balanced L1 loss to improve object classification and regression, respectively. The experimental results indicate that GCWNet achieves superior performance in object classification and localization on the DOTA-v1.5 dataset, which illustrates the superiority of GCWNet.

Unlike object detection in natural images that usually achieved great success, remote sensing imagery has its own challenges to detect and localize multiclass objects, such as large-scale change, uncertain direction, and high density. The context information of the objects is very worthwhile for solving these challenges in remote sensing images. In this letter, we propose a context-driven detection network (CDD-Net) to improve the accuracy of multiclass object detection in remote sensing images. For capturing the local neighboring objects and features, a local context feature network (LCFN) is proposed to learn the local context of the region of interest. Meanwhile, a hybrid attention pyramid network (HAPN) is designed, which can steer the focus to more valuable features. The HAPN inserts a squeeze and excitation block (SEB) and three asymmetric convolution blocks (ACBs) in the feature pyramid network (FPN). The experimental results over the DOTA-v1.5 data set demonstrate that the proposed CDD-Net yields promising results.

Accurate object detection in remote sensing images is an essential part of automatic extraction, analysis, and understanding of image information, which potentially plays a significant role in a number of practical applications. However, the scale diversity in remote sensing images presents a substantial challenge for object detection, regarded as one of the crucial problems to be solved. To extract multiscale feature representations and sufficiently exploit semantic context information, this letter proposes a semantic context-aware network (SCANet) model for multiscale object detection. We propose two novel modules, called receptive field-enhancement module (RFEM) and semantic context fusion module (SCFM), to enhance the performance of SCANet. The RFEM dedicates to more robust multiscale feature extraction by paying attention to distinct receptive fields through multibranch different convolutions. For the purpose of utilizing the semantic context information contained in the scene to guide the network to better detection accuracy, the SCFM integrates the semantic context features from the upper level with the lower level features and delivers them hierarchically. Experiments demonstrate that, compared with the state-of-the-art approaches, the SCANet yields superior detection results on the DOTA-v1.5 data set.

Compared with general optical images, remote sensing images (RSIs) capture large areas from high altitudes with a bird’s eye view, which is responsible for the many categories and scale variations of objects in the images, as well as the abundant scene information. Although the complexity of the RSIs presents a significant challenge to the object detection task, the complexity presents opportunities as well. The RSIs contain plenty of object-related context information, which is valuable for boosting the object detection performance. To address the existing issue of poor context utilization in RSIs, we propose a hierarchical context embedding network (HCENet) in this letter. First, we construct a semantic feature pyramid, in which the semantic context aggregation module (SFAM) integrates the semantic contexts included in the adjacent layers of features with a novel feature fusion mechanism. Furthermore, the scene-level context embedding module (SLCEM) extracts the scene context of the overall image by a simple design and is utilized to guide feature classification. Finally, we outperform the popular object detectors on the publicly available DOTA-v1.5 dataset, achieving superior performance.

Traditional celestial navigation mainly utilized the sextant to measure the attitude and the position contour method to calculate and resolve the vessel’s positioning problem, but these methods are not rigorous, having major deficiencies in the positioning accuracy. Currently, the small field-of-view star sensor is becoming the main attitude measurement equipment on vessels, and its measurement accuracy directly affects the vessel positioning results. Aiming at this problem, this research provides a model of small field-of-view star sensor positioning accuracy based on the two-star combination method, and numerical solutions are given. In addition, it focuses on the influence of the measurement error of the star sensor, especially the elevation angle error, on the positioning accuracy of the vessel and gives the star selection strategy for practical application. In particular, the star selection strategy is also applicable to other two-star positioning methods. The results show that the analytical solution is computationally simple and real-time, and the effect of measurement errors on positioning can be minimized by the star selection strategy. This study reveals the error influence mechanism based on the dual-star combination approach, which has significant implications for practical vessel navigation using small-field-of-view star sensors.