Synthetic aperture radar (SAR) ship detection is of significant importance in military and
commercial applications. However, a high similarity in intensity and spatial distribution of …

Model-based decomposition methods are widely used in full-polarization synthetic aperture
radar (SAR), for the inversion and interpretation of ground features and constitute an …

Synthetic aperture radar (SAR) is an advanced microwave sensor, which has been widely
used in ocean surveillance, and its operation is not affected by light and weather. SAR ship …

As radar sensors are being miniaturized, there is a growing interest for using them in indoor
sensing applications such as indoor drone obstacle avoidance. In those novel scenarios …

Flood monitoring is of crucial importance for protecting lives and properties. Change
detection (CD) methods on multisource remote sensing images have been widely used for …

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 …

Data, in deep learning (DL), are crucial to detect ships in synthetic aperture radar (SAR)
images. However, SAR image annotation limitations hinder DL-based SAR ship detection. A …

The problem of multiscale ship detection in synthetic aperture radar (SAR) images has
received much attention with the development of deep convolutional neural networks …

The constant false alarm rate (CFAR) detectors are well studied for ship detection in
synthetic aperture radar (SAR) images, which suffer performance degradation due to the …

In recent years, deep learning has been successfully applied in the field of synthetic
aperture radar (SAR) image object detection. However, unlike ships and tanks targets, the …