Closely arranged inshore ship detection using a bi-directional attention feature pyramid network

The detection of inshore ships in Synthetic Aperture Radar (SAR) images is seriously disturbed by shore buildings, especially for closely arranged inshore ships whose appearance is similar when compared with detection of deep-sea ships. There are many interference factors such as speckle noise, cross sidelobes, and defocusing in SAR images. These factors can seriously interfere with feature extraction, and the traditional Fully Convolutional One-Stage (FCOS) network often cannot effectively distinguish small-scale ships from backgrounds. Additionally, for closely arranged inshore ships, missed detections and inaccurate positioning often occur. In this paper, a method of inshore ship detection based on Bi-directional Attention Feature Pyramid Network (BAFPN) is proposed. In order to improve the detection ability of small-scale ships, the BAFPN is based on the FCOS network, which connects a Convolutional Block Attention Module (CBAM) to each feature map of the pyramid and can extract rich semantic features. Then, the idea from Path-Aggregation Network (PANet) is adopted to splice a bottom-up pyramid structure behind the original pyramid structure, further highlighting the features of different scales and improving the ability of the network to accurately locate ships under complex backgrounds, thereby avoiding missed detections in closely arranged inshore ship detection. Finally, a weighted feature fusion method is proposed, which makes the feature information extracted from the feature map have different focuses and can improve the accuracy of ship detection. Experiments on SAR image ship datasets show that the mAP for the SSDD and HRSID reached 0.902 and 0.839 respectively. The proposed method can effectively improve the ship positioning accuracy while maintaining a fast detection speed, and achieves better results for ship detection under complex background

Image Simulation in Remote Sensing

Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers