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 …

Compared with large-scale ships, small-scale ships occupy few pixels and have low
contrast, so it poses a great challenge to detect multiscale ships in synthetic aperture radar …

The scales of different ships vary in synthetic aperture radar (SAR) images, especially for
small scale ships, which only occupy few pixels. So ship detection methods currently face …

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 …

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 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 …

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 …

With the development of synthetic aperture radar (SAR) imaging and deep learning, SAR
ship detection based on convolutional neural networks (CNNs) has been extensively …

Synthetic aperture radar (SAR) images have been widely used for ship monitoring. The
traditional methods of SAR ship detection are difficult to detect small scale ships and avoid …