Significant advancements and progress made in recent computer vision research enable
more effective processing of various objects in high-resolution overhead imagery obtained …

This letter proposes a novel convolutional neural network (CNN) method for dual-polarized
synthetic aperture radar (SAR) ship grained classification. The network employs hybrid …

The method proposed in this letter for synthetic aperture radar (SAR) image classification
has two main stages. In the first stage, a convolutional neural network (CNN) is trained for …

There are still many challenges to be resolved in the task of ship classification in synthetic
aperture radar (SAR) images, such as limited number of labeled samples in SAR domain …

With the continuous development of earth observation technology, space-based synthetic
aperture radar (SAR) has become an important source of information for maritime …

In this paper, we propose a novel multi-manifold feature fusion (MMFF) based deep learning
framework for automatic target recognition (ATR) in complex-valued synthetic aperture radar …

Vessel monitoring technology involves the application of remote sensing technologies to
detect and identify vessels in various environments, which is critical for monitoring vessel …

Fine-grained ship classification in synthetic aperture radar (SAR) images is a challenging
task, since SAR images can only provide limited discriminative information due to the …

Synthetic aperture radar (SAR) ship recognition can obtain location and class information
from SAR scene images, which is important in military and civilian fields, and has turned into …

With the recent developments in the field of deep learning, various neural networks have
been proposed to increase the detection accuracy of targets in radar data and beyond. A …