Machine learning methods for synthetic aperture radar (SAR) image automatic target
recognition (ATR) can be divided into two main types: traditional methods and deep learning …

SAR ship detection and recognition are important components of the application of SAR
data interpretation, allowing for the continuous, reliable, and efficient monitoring of maritime …

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 …

Environmental perception is crucial for autonomous ships realizing autonomous navigation,
in particular, the high-precision and low-latency detection of small objects on the sea surface …

As an active microwave device, synthetic aperture radar (SAR) uses the backscatter of
objects for imaging. SAR image ship targets are characterized by unclear contour …

One of the current research areas in the synthetic aperture radar (SAR) processing fields is
deep learning-based ship detection in SAR imagery. Recently, ship detection in SAR …

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 …

Detecting ships in synthetic aperture radar (SAR) images poses a formidable challenge,
primarily attributed to limited observation samples and complex environments. To address …

Current synthetic aperture radar (SAR) ship detectors achieve excellent performance with
sufficient samples while encountering degraded results when the sensors and imaging …

Most of the existing deep learning-based synthetic aperture radar (SAR) target detection
algorithms rely on manual experience to repeatedly adjust structures and parameters to …