Classic multireceiver synthetic aperture sonar (SAS) reconstruction algorithms relying upon
Loffeld's bistatic formula (LBF) should first utilize the data segmentation approach to remove …

Aiming at the low observable target detection under sea clutter backgrounds, this paper
emphasizes the exploration of distinguishable full-polarization features between target and …

Most state-of-the-art synthetic aperture radar (SAR) ship detection methods based on deep
learning require large amounts of labeled data for network training. However, the annotation …

In the last decades, how to detect ships with polarimetric synthetic aperture radar (PolSAR)
has become one hot topic. Unfortunately, most of the existing ship detection methods cannot …

Detection of group targets in synthetic aperture radar (SAR) images is of great importance in
civil and military applications. However, most of the existing methods focus on the detection …

It is of great significance to accurately detect ships on the ocean. To obtain higher detection
performance, many researchers use deep learning to identify ships from images instead of …

In the field of maritime surveillance with satellite imagery, ship detection with Synthetic
Aperture Radar (SAR) is largely successful with the exception of those cases where the …

The high-resolution image obtained by ultrawideband synthetic aperture radar (UWB SAR)
includes rich features such as shape and scattering features, which can be utilized for …

Passive bistatic radar is a novel radar technology that passively detects targets without
actively emitting signals. Since passive bistatic radar entails larger data volumes and …

As the number of unauthorized operations of unmanned aerial vehicles increases, the
protection of public spaces, as well as critical infrastructures against malicious actions has …