Remote sensing technologies and machine learning (ML) algorithms play an increasingly
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …

Polarization can provide information largely uncorrelated with the spectrum and intensity.
Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields …

To collect data of distributed sensors located at different areas in challenging scenarios
through artificial way is obviously inefficient, due to the numerous labor and time. Unmanned …

Oil spill (OS), as one of the main pollutions in the ocean, is a serious threat to the marine
environment. Thus, timely and accurate OS detection (OSD) is necessary for ocean …

Monitoring of aquatic ecosystems has been historically accomplished by intensive
campaigns of direct measurements (by probes and other boat instruments) and indirect …

Abstract Synthetic Aperture Radar (SAR) imagery can be beneficial for segmenting oil spills,
which are a common environmental hazard. Oil spill detection in SAR imagery faces several …

The identification and counting of grassland degradation indicator ground objects is an
important component of grassland ecological monitoring. These steps are also an important …

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 …

River width is an essential parameter for studying the river's hydrological process and has
been widely used to estimate the river discharge. The existing approaches to measuring …

Oil spill accidents are one of the major problems causing marine pollution, and thus such
accidents require rapid detection for early response. In recent years, deep learning …