Due to the diversity of aircraft target scale and interference of background strong scattering in synthetic aperture radar (SAR) images, it is a challenge for target detection tasks. In response to these problems, this paper proposes a new SAR image aircraft target detection model named EST-YOLOv5s. The proposed model integrates the Efficient Channel Attention (ECA) mechanism into the C3 module of the backbone network, which enhances the scattering features of aircraft targets and suppresses irrelevant background information without increasing the number of parameters. Secondly, replace the bottleneck module in the last C3 module in the backbone network with the Swin Transformer Block. By using the shifted window partitioning approach to obtain the global perception ability, the problem of missed detection of small objects is improved. Finally, the Task-Specific Context Decoupling (TSCODE) head is used to balance the relationship between classification and regression so that different contextual details can be better utilized. In this paper, the SAR Aircraft Detection Dataset (SADD) is used as the experimental data set to compare with the baseline model YOLOv5s. The experimental outcomes indicate that the recall of the EST-YOLOv5s model reached 94.2%, the precision reached 97.3%, and the mAP@50 reached 97.8%, which were 2.3%, 1.7%, and 1.7% higher than YOLOv5s respectively. Furthermore, our model also meets the real-time requirements in terms of speed and exhibits strong anti-interference ability.

Segmentation of high-resolution remote sensing images is one of the hottest topics in deep learning. Compared to ordinary images, high-resolution remote sensing images possess characteristics such as higher intra-class diversity and lower inter-class separability. Additionally, the objects in these images are complex and have smaller sizes. Aiming at the classical segmentation network in remote sensing images, there are some problems, such as inaccurate edge object segmentation, inconsistent segmentation of different types of objects, low detection accuracy, and a high false detection rate. This paper proposes a new hybrid attention model (S-CA), a new coordinate efficient channel attention module (C-ECA), and a new small-target feature extraction network (S-FE). The S-CA model enhances important spatial and channel features in shallow layers, allowing for more detailed feature extraction. The C-ECA model utilizes convolutional layers to capture complex dependencies between variations, thereby better capturing feature information at each position and reducing redundancy in feature channels. The S-FE network can capture the local feature information of different targets more effectively. It enhances the recognition and classification capabilities of various targets and improves the detection rate of small targets. The algorithm is used for segmentation in high-resolution remote sensing images. Experiments were conducted on the public dataset GID-15 based on Gaofen-2 satellite remote sensing images. The experimental results demonstrate that the improved DeepLabV3+ segmentation algorithm for remote sensing images achieved a mean intersection over union (mIoU), mean pixel accuracy (mPA), and mean precision (mP) of 91.6%, 96.1%, and 95.5%, respectively. The improved algorithm is more effective than current mainstream segmentation networks.