Object detection has made great progress. However, due to the unique imaging method of optical satellite remote sensing, the detection of slender targets is still insufficient. Specifically, the perspective of optical satellites is small, and the characteristics of slender targets are severely lost during imaging, resulting in insufficient detection task information; at the same time, the appearance of slender targets in the image is greatly affected by the satellite perspective, which is likely to cause insufficient generalization capabilities of conventional detection models. In response to these two points, we have made some improvements. First, in this paper, we introduce the shadow as auxiliary information to complement the trunk features of the target lost in imaging. Second, to reduce the impact of satellite perspective on imaging, in this paper, we use the characteristic that shadow information is not affected by satellite perspective to design STC-Det. STC-Det treats the shadow and the target as two different types of targets and uses the shadow information to assist the detection, reducing the impact of the satellite perspective on detection. Among them, in order to improve the performance of STC-Det, we propose an automatic matching method (AMM) of shadow and target and a feature fusion method (FFM). Finally, this paper proposes a new method to calculate the heatmaps of detectors, which verifies the effectiveness of the proposed network in a visual way. Experiments show that when the satellite perspective is variable, the precision of STC-Det is increased by 1.7%, and when the satellite perspective is small, the precision of STC-Det is increased by 5.2%.

Optical satellite remote sensing has become an important means of large-scale targets detection. However, due to the small perspective of satellite remote sensing, most of the structural information of slender targets, such as power transmission towers, is compressed during the imaging process. Experiments have found that the existing network does not work well in this situation. In order to tackle this problem, we proposed a shadow information-based slender targets detection (SI-STD) method. First, the shadow of the tower is used to compensate for the structure information lost during imaging. Second, the candidate regions containing towers and shadows are proposed by a modified regional proposal network (MRPN), which also classifies these regions at the same time. Third, the tower targets are regressed from candidate regions by TowerHead. Finally, by fusing the results of MRPN and TowerHead, we get the final results. The framework can comprehensively use the tower and its shadow to complete the detection task, and it has also shown excellent performance in the test.

The existing slender target detection methods based on optical satellite images are greatly affected by the satellite perspective and the solar perspective. Due to limited data sources, it is difficult to implement a fully data-driven approach. This work introduces the imaging parameters of optical satellite images, which greatly reduces the influence of the satellite perspectives and the solar perspectives, and reduces the demand for the amount of data. We improve the oriented bounding box (OBB) detector based on faster R-CNN (region convolutional neural networks) and propose an imaging parameters-considered detector (IPC-Det) which is more suitable for our task. Specifically, in the first stage, the umbra and the shadow are extracted by horizontal bounding box (HBB), respectively, and then the matching of the umbra and the shadow is realized according to the imaging parameters. In the second stage, the paired umbra and shadow features are used to complete the classification and regression, and the target is obtained by OBB. In experiments, after introducing imaging parameters, our detection accuracy is improved by 3.9% (up to 87.5%), proving that this work is a successful attempt to introduce imaging parameters for slender target detection.

Fine-Grained Few-Shot Classification (FG-FSC) in remote sensing has become a critical task, as the scarcity of high-quality annotated data severely restricts the performance of deep learning models in fine-grained classification. Although Contrastive Language-Image Pre-Training (CLIP) exhibits strong generalization ability in few-shot learning, it fails to generate discriminative text and image features when adapted to remote sensing tasks. In this paper, a framework is proposed to adapt CLIP to remote sensing FG-FSC from both visual and text aspects. First, we introduce a Distribution-AWare Adapter (DAWA) that adaptively fuses instance-level visual knowledge from few-shot samples with distribution-aware representations derived from Gaussian Discriminant Analysis based on the original CLIP zero-shot knowledge, leading to stable visual feature representations under various few-shot settings. A hybrid loss function that incorporates transductive and contrastive regularization is employed to further prevent overfitting and improve the discriminability of features. Furthermore, we generate category-level fine-grained text captions, optimizing the image–text alignment when extremely few training images are available. Experiments on multiple remote sensing and natural image datasets verify that the proposed framework achieves state-of-the-art few-shot fine-grained classification performance with a modest training cost, providing a practical solution for few-shot remote sensing image analysis.

A reduced-order Linear Active Disturbance Rejection Control (LADRC) controller for the load frequency control (LFC) system is studied in this paper. The power system is inevitably disturbed by various factors such as temperature and pressure, which may cause instability of the frequency and bring safety problems. The ADRC controller has the characteristic of estimating the disturbance and then eliminating it to achieve the suppression of disturbance. Therefore, the ADRC controller, theoretically speaking, can be used to solve the LFC problems. We utilize the reduced-order LADRC controller to control a three-area interconnected power system, and the simulation results show that the performance of the reduced-order ADRC controller is better than that of the traditional PID controller. Finally, we verified the robustness of the designed controller by changing the parameters of the controlled object.