Convolutional neural networks (CNNs) have achieved milestones in object detection of
synthetic aperture radar (SAR) images. Recently, vision transformers and their variants have …

In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it
is urgent to develop methods with higher accuracy and faster speed for ship detection in …

Deep learning has attracted increasing attention across a number of disciplines in recent
years. In the field of remote sensing, ship detection based on deep learning for synthetic …

With the booming of Convolutional Neural Networks (CNNs), CNNs such as VGG-16 and
ResNet-50 widely serve as backbone in SAR ship detection. However, CNN based …

Deep learning-based ship detection from SAR data is one of the challenging problems in the
remote sensing area. Also, SAR ship detection is precise object detection and pattern …

In the face of synthetic aperture radar (SAR) image object detection with different
distributions of training and test data, traditional supervised learning methods cannot …

Recently, deep learning-based methods for synthetic aperture radar (SAR) ship detection
have made remarkable advancements. However, most existing methods primarily focus on …

Synthetic aperture radar ship detection has recently received significant attention from
scholars. However, accurately distinguishing between ships is challenging due to the …

Recently, convolutional neural network based methods have been studied for ship detection
in optical remote sensing images. However, it is challenging to apply them to microwave …

Deep learning methods have made significant progress in ship detection in synthetic
aperture radar (SAR) images. The pretraining technique is usually adopted to support deep …