Remote sensing image object detection and instance segmentation are widely valued research fields. A convolutional neural network (CNN) has shown defects in the object detection of remote sensing images. In recent years, the number of studies on transformer-based models increased, and these studies achieved good results. However, transformers still suffer from poor small object detection and unsatisfactory edge detail segmentation. In order to solve these problems, we improved the Swin transformer based on the advantages of transformers and CNNs, and designed a local perception Swin transformer (LPSW) backbone to enhance the local perception of the network and to improve the detection accuracy of small-scale objects. We also designed a spatial attention interleaved execution cascade (SAIEC) network framework, which helped to strengthen the segmentation accuracy of the network. Due to the lack of remote sensing mask datasets, the MRS-1800 remote sensing mask dataset was created. Finally, we combined the proposed backbone with the new network framework and conducted experiments on this MRS-1800 dataset. Compared with the Swin transformer, the proposed model improved the mask AP by 1.7%, mask AP_S by 3.6%, AP by 1.1% and AP_S by 4.6%, demonstrating its effectiveness and feasibility.

In recent years, remote sensing images has become one of the most popular directions in image processing. A small feature gap exists between satellite and natural images. Therefore, deep learning algorithms could be applied to recognize remote sensing images. We propose an improved Mask R-CNN model, called SCMask R-CNN, to enhance the detection effect in the high-resolution remote sensing images which contain the dense targets and complex background. Our model can perform object recognition and segmentation in parallel. This model uses a modified SC-conv based on the ResNet101 backbone network to obtain more discriminative feature information and adds a set of dilated convolutions with a specific size to improve the instance segmentation effect. We construct WFA-1400 based on the DOTA dataset because of the shortage of remote sensing mask datasets. We compare the improved algorithm with other state-of-the-art algorithms. The object detection AP₅₀ and AP increased by 1–2% and 1%, respectively, objectively proving the effectiveness and the feasibility of the improved model.

The wide range, complex background, and small target size of aerial remote sensing images results in the low detection accuracy of remote sensing target detection algorithms. Traditional detection algorithms have low accuracy and slow speed, making it difficult to achieve the precise positioning of small targets. This paper proposes an improved algorithm based on You Only Look Once (YOLO)-v3 for target detection of remote sensing images. Due to the difficulty in obtaining the datasets, research on small targets for complex images, such as airplanes and ships, is the focus of research. To make up for the problem of insufficient data, we screen specific types of training samples from the DOTA (Dataset of Object Detection in Aerial Images) dataset and select small targets in two different complex backgrounds of airplanes and ships to jointly evaluate the optimization degree of the improved network. We compare the improved algorithm with other state-of-the-art target detection algorithms. The results show that the performance indexes of both datasets are ameliorated by 1–3%, effectively verifying the superiority of the improved algorithm.

The You Only Look Once (YOLO) series has been widely adopted across various domains. With the increasing prevalence of continuous satellite observation, the resulting video streams can be subjected to intelligent analysis for various applications, such as traffic flow statistics, military operations, and other fields. Nevertheless, the signal-to-noise ratio of objects in satellite videos is considerably low, and their size is often smaller, ranging from tens to one percent, when compared to those taken by drones and other equipment. Consequently, the original YOLO algorithm’s performance is inadequate when detecting tiny objects in satellite videos. Hence, we propose an improved framework, named HB-YOLO. To enable the backbone to extract features, we replaced the universal convolution with an improved HorNet that enables higher-order spatial interactions. We replaced all Extended Efficient Layer Aggregation Networks (ELANs) with the BoTNet attention mechanism to make the features fully fused. In addition, anchors were re-adjusted, and image segmentation was integrated to achieve detection results, which are tracked using the BoT-SORT algorithm. The experimental results indicate that the original algorithm failed to learn using the satellite video dataset, whereas our proposed approach yielded improved recall and precision. Specifically, the F1-score and mean average precision increased to 0.58 and 0.53, respectively, and the object-tracking performance was enhanced by incorporating the image segmentation method.

In remote sensing images, small target size and diverse background cause difficulty in locating targets accurately and quickly. To address the lack of accuracy and inefficient real-time performance of existing tracking algorithms, a multi-object tracking (MOT) algorithm for ships using deep learning was proposed in this study. The feature extraction capability of target detectors determines the performance of MOT algorithms. Therefore, you only look once (YOLO)-v3 model, which has better accuracy and speed than other algorithms, was selected as the target detection framework. The high similarity of ship targets will cause poor tracking results; therefore, we used the multiple granularity network (MGN) to extract richer target appearance information to improve the generalization ability of similar images. We compared the proposed algorithm with other state-of-the-art multi-object tracking algorithms. Results show that the tracking accuracy is improved by 2.23%, while the average running speed is close to 21 frames per second, meeting the needs of real-time tracking.

This study explored the impact of atmospheric turbulence on partially coherent light propagation. Atmospheric turbulence causes random modulation of the intensity and phase of light, resulting in a speckle pattern in the far field. This study focused on partially coherent Gaussian Schell model beams and derived an analytical expression of the cross-spectral density function for their transmission through atmospheric turbulence, based on the generalized Huygens–Fresnel principle and the Tatarski spectrum model. Numerical simulations were used to investigate the effects of the source parameters and turbulence strength on the intensity distribution, beam width, and coherence length of partially coherent light in horizontal atmospheric transmission. The results demonstrate that diffraction-induced broadening primarily affects the intensity distribution of light in free-space transmission. Short transmission distances in atmospheric turbulence have comparable characteristics to those in a vacuum; however, as the turbulence intensity and transmission distance increase, the beam broadening effect amplifies, and the coherence length is reduced. The findings are relevant to the design of acquisition, pointing, and tracking systems for wireless laser communication systems and offer insights into the optimization of optical systems for atmospheric conditions.