After the revival of deep learning in computer vision in 2012, SAR ship detection comes into
the deep learning era too. The deep learning-based computer vision algorithms can work in …

Ship detection and classification pose significant challenges in remote sensing. The potent
feature extraction capabilities of deep learning algorithms render them pivotal for these …

Deep learning has offered new ideas in SAR ship target recognition. Although many
methods improve the recognition performance through the improvement of loss function and …

Synthetic-aperture radar (SAR) image target detection is widely used in military, civilian and
other fields. However, existing detection methods have low accuracy due to the limitations …

Synthetic aperture radar (SAR) is playing an important role in maritime domain awareness.
As a fundamental ocean mission, SAR ship detection can offer high-quality services for …

Current synthetic aperture radar (SAR) ship classifiers using convolutional neural networks
(CNNs) offer state-of-the-art performance. Yet, they still have two defects potentially …

Synthetic Aperture Radar (SAR) object detection has gained significant attention recently
due to its irreplaceable all-weather imaging capabilities. However, this research field suffers …

Most of the existing synthetic aperture radar (SAR) ship instance segmentation models do
not achieve mask interaction or offer limited interaction performance. Besides, their …

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 …

This letter proposes a squeeze-and-excitation Laplacian pyramid network with dual-
polarization feature fusion (SE-LPN-DPFF) for ship classification in synthetic aperture radar …