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The development of deep learning and remote sensing makes it possible to obtain information about oceanic internal waves from massive data. Oceanic internal waves play an important role in ocean activities. To obtain the light and dark stripes from synthetic aperture radar (SAR) images, a stripe segmentation algorithm for oceanic internal waves is proposed based on Mask RCNN. The research results show that the proposed method can not only identify whether there are oceanic internal waves, but also obtain the respective locations of the light and dark stripes in the images. The width and the direction angle of each light and dark stripe can be obtained efficiently by a separation and matching method with the sector region (SMMSR). Moreover, based on the identified relative locations of the light and dark stripes, it is possible to further determine the time when the oceanic internal waves undergo polarity conversion. [ABSTRACT FROM AUTHOR]

Precise and regularly updated maps of surface water extent are essential for wetland management. Since it is often challenging to obtain water extent information through ground surveys due to accessibility, satellite remote sensing has become a critical and cost-effective tool for acquiring this information in a temporal context. The methods that are commonly used include supervised and unsupervised multispectral classification, density slicing of a single band and spectral indices. However, optical data are substantially affected by cloud cover and solar illumination, thus, radar data comprise an ideal option. This article presents a methodological framework for rapid delineation of waterbodies' boundaries. For that purpose, an object-based approach is implemented. Sentinel-1 data were obtained and the Mean-Shift segmentation algorithm was employed. The proposed methodology produced promising results achieving an overall accuracy of 98%, a producer's accuracy for waterbodies of 90% and a user's accuracy for waterbodies of 95%. [ABSTRACT FROM AUTHOR]