Recently, deep-learning methods have been successfully applied to the ship detection in the
synthetic aperture radar (SAR) images. It is still a great challenge to detect multiscale SAR …

In recent years, deep learning has been widely used in remote sensing, especially in the
field of synthetic aperture radar (SAR) image target detection. However, all of these deep …

Quantities of multi-temporal remote sensing (RS) images create favorable conditions for
exploring the urban change in the long term. However, diverse multi-source features and …

Ship detection in synthetic aperture radar (SAR) images is a significant and challenging
task. Recently, deep convolutional neural networks have been applied to solve the detection …

To date, although numerous methods of Change detection (CD) in remote sensing images
have been proposed, accurately identifying changes is still a great challenge, due to the …

At present, few-shot object detection research in the field of optical remote sensing images
has been conducted, but few-shot object detection in the field of synthetic aperture radar …

Automatic change detection is an interesting research area in remote sensing (RS)
technology aims to detect the changes in synthetic aperture radar (SAR) and multi-temporal …

Optimization problems are ubiquitous in every field, and they are becoming more and more
complex, which greatly challenges the effectiveness of existing optimization methods. To …

Synthetic aperture radar (SAR) target detectors based on deep learning have difficulty
finding a good balance between accuracy and speed. Current pruning methods are usually …

In recent times, the realm of remote sensing has witnessed a remarkable surge in the area of
deep learning, specifically in the domain of target recognition within synthetic aperture radar …