It is very difficult to detect multi-scale synthetic aperture radar (SAR) ships, especially under
complex backgrounds. Traditional constant false alarm rate methods are cumbersome in …

The problem of multiscale ship detection in synthetic aperture radar (SAR) images has
received much attention with the development of deep convolutional neural networks …

Convolution neural network (CNN)-based detectors have shown great performance on ship
detections of synthetic aperture radar (SAR) images. However, the performance of current …

Synthetic aperture radar (SAR) ship detection is widely used in cutting-edge applications
such as environmental protection, traffic monitoring, search, and rescue. Lightweight …

Thanks to the excellent feature representation capabilities of neural networks, target
detection methods based on deep learning are now widely applied in synthetic aperture …

In light of recent advances in deep learning and Synthetic Aperture Radar (SAR) technology,
there has been a growing adoption of ship detection models that are based on deep …

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 …

Deep learning algorithms have been widely used in ship detection with synthetic aperture
radar (SAR). However, the complex background, clutter noise, and large span of ship sizes …

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 …

Synthetic aperture radar (SAR) is an active microwave imaging sensor with the capability of
working in all-weather, all-day to provide high-resolution SAR images. Recently, SAR …