Oil spill detection and mapping using deep learning (OSDMDL) is crucial for assessing its
impact on coastal and marine ecosystems. A novel approach was employed in this study to …

Intelligent recognition of maritime ship targets from synthetic aperture radar (SAR) imagery is
a hot research issue. However, interferences such as the strong sea clutter, sidelobe, small …

The increasingly frequent marine oil spill disasters have great harm to the marine
ecosystem. As an essential means of remote sensing monitoring, synthetic aperture radar …

In the domain of real-world concept learning, feature selection plays a crucial role in
accelerating learning processes and enhancing the quality of classification concepts …

With the advent of the era of high-resolution remote sensing, semantic segmentation
methods for solving pixel-level classification have been widely studied. Deep learning has …

Nowadays, the developed deep neural networks (DNNs) have been widely applied to
synthetic aperture radar (SAR) image interpretation, such as target classification and …

On the remote sensing images of marine synthetic aperture radar (SAR), ship targets often
occupy only a small part of an image, and the rest are all sea and coastal backgrounds …

The accurate acquisition of farmland boundary information is an important means for
agricultural production data statistics. Due to the extreme imbalance of categories in …

One-shot neural architecture search (NAS) has achieved impressive results in the field of
synthetic aperture radar (SAR) ship detection. However, it is a challenge to balance …

The unmanned aerial vehicle (UAV) is usually flexible and frequently low-altitude flying
without the influence of clouds and severe weather, and it is widely used for port oil spill …