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Title: Effective Cloud Detection and Segmentation Using a Gradient-Based Algorithm for Satellite Imagery: Application to Improve PERSIANN-CCS

Abstract

The effective identification of clouds and monitoring of their evolution are important toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation algorithm is developed using image processing techniques. This method integrates morphological image gradient magnitudes to separate cloud systems and patches boundaries. A varying scale kernel is implemented to reduce the sensitivity of image segmentation to noise and to capture objects with various finenesses of the edges in remote sensing images. The proposed method is flexible and extendable from single to multispectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellite ( GOES-16) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potentials comparing to the conventional segmentation technique used in PERSIANN-CCS to improve rain detection and estimation skills with an accuracy rate of up to 98% in identifying cloud regions.

Authors:
 [1];  [1];  [1];  [2];  [2]
  1. Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
  2. Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania
Publication Date:
Research Org.:
Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE Office of International Affairs (IA); California Energy Commission; National Science Foundation (NSF); NOAA/NESDIS/NCDC; NASA Earth and Space Science Fellowship
OSTI Identifier:
1511900
Alternate Identifier(s):
OSTI ID: 1613793
Grant/Contract Number:  
IA0000018; 300-15-005; CCF-1331915; NA09NES4400006; 2009-1380-01; NNX15AN86H; NNX16AD84G; NNX12AJ79G
Resource Type:
Published Article
Journal Name:
Journal of Hydrometeorology
Additional Journal Information:
Journal Name: Journal of Hydrometeorology Journal Volume: 20 Journal Issue: 5; Journal ID: ISSN 1525-755X
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences; Clouds; Cloud cover; Precipitation; Rainfall; Cloud retrieval

Citation Formats

Hayatbini, Negin, Hsu, Kuo-lin, Sorooshian, Soroosh, Zhang, Yunji, and Zhang, Fuqing. Effective Cloud Detection and Segmentation Using a Gradient-Based Algorithm for Satellite Imagery: Application to Improve PERSIANN-CCS. United States: N. p., 2019. Web. doi:10.1175/JHM-D-18-0197.1.
Hayatbini, Negin, Hsu, Kuo-lin, Sorooshian, Soroosh, Zhang, Yunji, & Zhang, Fuqing. Effective Cloud Detection and Segmentation Using a Gradient-Based Algorithm for Satellite Imagery: Application to Improve PERSIANN-CCS. United States. doi:10.1175/JHM-D-18-0197.1.
Hayatbini, Negin, Hsu, Kuo-lin, Sorooshian, Soroosh, Zhang, Yunji, and Zhang, Fuqing. Wed . "Effective Cloud Detection and Segmentation Using a Gradient-Based Algorithm for Satellite Imagery: Application to Improve PERSIANN-CCS". United States. doi:10.1175/JHM-D-18-0197.1.
@article{osti_1511900,
title = {Effective Cloud Detection and Segmentation Using a Gradient-Based Algorithm for Satellite Imagery: Application to Improve PERSIANN-CCS},
author = {Hayatbini, Negin and Hsu, Kuo-lin and Sorooshian, Soroosh and Zhang, Yunji and Zhang, Fuqing},
abstractNote = {The effective identification of clouds and monitoring of their evolution are important toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation algorithm is developed using image processing techniques. This method integrates morphological image gradient magnitudes to separate cloud systems and patches boundaries. A varying scale kernel is implemented to reduce the sensitivity of image segmentation to noise and to capture objects with various finenesses of the edges in remote sensing images. The proposed method is flexible and extendable from single to multispectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellite ( GOES-16) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potentials comparing to the conventional segmentation technique used in PERSIANN-CCS to improve rain detection and estimation skills with an accuracy rate of up to 98% in identifying cloud regions.},
doi = {10.1175/JHM-D-18-0197.1},
journal = {Journal of Hydrometeorology},
number = 5,
volume = 20,
place = {United States},
year = {2019},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1175/JHM-D-18-0197.1

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Cited by: 3 works
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