Cloud detection from a sequence of SST images☆
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CSDFormer: A cloud and shadow detection method for landsat images based on transformer
2024, International Journal of Applied Earth Observation and GeoinformationSurface eddy kinetic energy variability of the Western North Atlantic slope sea 1993–2016
2024, Continental Shelf ResearchCloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects
2022, ISPRS Journal of Photogrammetry and Remote SensingCitation Excerpt :Gómez-Chova et al. (2017) performed nonlinear kernel ridge regression based on selected multiple cloud-free images to synthesize the clear-sky reference images of the target date, applied cluster analysis on the differential images of cloud-covered images and clear-sky reference images, determined whether each cluster is a cloud region, and eventually achieved highly accurate multi-temporal cloud detection. In general, multi-temporal based CCS detection methods usually outperform mono-temporal based methods (Cayula and Cornillon, 1996; Ricciardelli et al., 2008; Zhu and Woodcock, 2014b). Benefiting from the use of multi-temporal information, multi-temporal based methods can alleviate the common problems of thin cloud omission and high-bright objects commission.
Objective delineation of persistent SST fronts based on global satellite observations
2022, Remote Sensing of EnvironmentCitation Excerpt :In the interest of brevity, we summarize the principles underlying the CCA. For a detailed description of the algorithm, the reader is referred to the original papers detailing it (Cayula and Cornillon, 1992, 1995, 1996), in addition to a more recent paper (Chang and Cornillon, 2015). The algorithm, as originally conceived, relies on three levels of processing of the SST fields: the image level, the window level and the pixel level.
Distribution and variability of sea surface temperature fronts in the south China sea
2020, Estuarine, Coastal and Shelf ScienceGRADHIST - A method for detection and analysis of oceanic fronts from remote sensing data
2016, Remote Sensing of EnvironmentCitation Excerpt :The strength of the gradient algorithms is that they enable the detection of any front regardless of its strength, as shown by Castelao et al. (2006), or by Belkin and O'Reilly (2009); however, gradient algorithms are unable to recognise and discard false fronts caused by noise. On the other hand, histogram algorithms are able to detect weak fronts in the presence of high background noise as shown by Cayula and Cornillon (1992, 1995, 1996), Diehl, Budd, Ullman, and Cayula (2002) and, Ullman and Cornillon (1999, 2000, 2001). In the present study, a new approach was developed which combines and modifies the gradient algorithm of Canny (1986) and the histogram algorithm of Cayula and Cornillon (1992) in order to improve the front detection related to the suitability for the climatology of the North Sea.
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This research was performed with support from the National Aeronautics and Space Administration through Grant No. NAGW-3009, the Jet Propulsion Laboratory as part of the NSCAT Project through Grant No. 957627, and the Global Ocean Monitoring and Prediction (GOMAP) project via the Strategic Environmental Research and Developmemnt Progam (SERDP). Salary support for P. Cornillon was provided by the State of Rhode Island and Providence Plantations. The satellite data processing software used to preprocess the imagery was developed by R. Evans, O. Brown, J. Brown, and A. Li of the University of Miami. Their continued support is greatly appreciated.