Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS

AbstractThe 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...

[1]  Peter Vogt,et al.  A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors , 2011 .

[2]  J. Coakley,et al.  Retrieval of cloud cover from satellite imagery data: a statistical approach , 1985 .

[3]  Z. Janjic.  The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes , 1994 .

[4]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[5]  J. Cihlar,et al.  An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images , 2002 .

[6]  Chris Kidd,et al.  Satellite Rainfall Estimation Using a Combined Pasive Microwave and Infrared Algorithm. , 2003 .

[7]  Mekonnen,et al.  Satellite Rainfall Applications for Surface Hydrology || Extreme Precipitation Estimation Using Satellite-Based PERSIANN-CCS Algorithm , 2010 .

[8]  Qingmao Hu,et al.  A marker-based watershed method for X-ray image segmentation , 2014, Comput. Methods Programs Biomed..

[9]  Valliappa Lakshmanan,et al.  An Efficient , General-Purpose Technique to Identify Storm Cells in Geospatial Images , 2010 .

[10]  J. Curry,et al.  Confronting Models with Data: The Gewex Cloud Systems Study , 2003 .

[11]  Serge Beucher,et al.  Segmentation tools in mathematical morphology , 1990, Optics & Photonics.

[12]  Kuolin Hsu,et al.  Bias adjustment of infrared‐based rainfall estimation using Passive Microwave satellite rainfall data , 2017 .

[13]  Demin Wang,et al.  A multiscale gradient algorithm for image segmentation using watershelds , 1997, Pattern Recognit..

[14]  C. Bishop,et al.  Cloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter , 2009 .

[15]  Silas C. Michaelides,et al.  Multifeature texture analysis for the classification of clouds in satellite imagery , 2003, IEEE Trans. Geosci. Remote. Sens..

[16]  H. Sembhi,et al.  Towards cloud detection and cloud frequency distributions from MIPAS infra-red observations , 2005 .

[17]  Dong L. Wu,et al.  Cloud ice: A climate model challenge with signs and expectations of progress , 2007 .

[18]  A. Aghakouchak,et al.  A near real-time satellite-based global drought climate data record , 2012 .

[19]  Hirohiko Masunaga,et al.  Improving a spectral bin microphysical scheme using TRMM satellite observations , 2010 .

[20]  Larry L. Stowe,et al.  Scientific basis and initial evaluation of the CLAVR-1 global clear cloud classification algorithm f , 1999 .

[21]  Mahmood R. Azimi-Sadjadi,et al.  A study of cloud classification with neural networks using spectral and textural features , 1999, IEEE Trans. Neural Networks.

[22]  Yan Du,et al.  A Robust but Spurious Pattern of Climate Change in Model Projections over the Tropical Indian Ocean , 2016 .

[23]  W. Rossow,et al.  The International Satellite Cloud Climatology Project (ISCCP): The First Project of the World Climate Research Programme , 1983 .

[24]  C. Long,et al.  Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data , 1991 .

[25]  Fuqing Zhang,et al.  Assimilation of All-Sky Infrared Radiances from Himawari-8 and Impacts of Moisture and Hydrometer Initialization on Convection-Permitting Tropical Cyclone Prediction , 2018, Monthly Weather Review.

[26]  G. Gesell,et al.  An algorithm for snow and ice detection using AVHRR data An extension to the APOLLO software package , 1989 .

[27]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  R. Horton,et al.  Reducing spread in climate model projections of a September ice-free Arctic , 2013, Proceedings of the National Academy of Sciences.

[29]  Huifang Li,et al.  A Variational Gradient-based Fusion Method for Visible and SWIR Imagery , 2012 .

[30]  K. Parvati,et al.  Image Segmentation Using Gray-Scale Morphology and Marker-Controlled Watershed Transformation , 2008 .

[31]  Edward J. Delp,et al.  Morphological operations for color image processing , 1999, J. Electronic Imaging.

[32]  James J. Simpson,et al.  Application of neural networks to AVHRR cloud segmentation , 1995, IEEE Trans. Geosci. Remote. Sens..

[33]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[34]  S. Sorooshian,et al.  Evaluation of PERSIANN system satellite-based estimates of tropical rainfall , 2000 .

[35]  K. T. Kriebel,et al.  The cloud analysis tool APOLLO: Improvements and validations , 2003 .

[36]  W. Collins,et al.  Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models , 2008 .

[37]  Y. Weng,et al.  Performance of convection‐permitting hurricane initialization and prediction during 2008–2010 with ensemble data assimilation of inner‐core airborne Doppler radar observations , 2011 .

[38]  Y. Hong,et al.  Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System , 2004 .

[39]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[40]  Frank J. LaFontaine,et al.  Spatial and Temporal Varying Thresholds for Cloud Detection in GOES Imagery , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[41]  R. Saunders,et al.  An improved method for detecting clear sky and cloudy radiances from AVHRR data , 1988 .

[42]  Kuolin Hsu,et al.  Short-term quantitative precipitation forecasting using an object-based approach , 2013 .

[43]  Kuolin Hsu,et al.  Extreme Precipitation Estimation Using Satellite-Based PERSIANN-CCS Algorithm , 2010 .

[44]  Kuolin Hsu,et al.  Evaluating the Utility of Multispectral Information in Delineating the Areal Extent of Precipitation , 2009 .

[45]  Bruce A. Wielicki,et al.  Statistical Analyses of Satellite Cloud Object Data from CERES. Part I: Methodology and Preliminary Results of the 1998 El Niño/2000 La Niña , 2005 .

[46]  Sylvie Le Hégarat-Mascle,et al.  Use of Markov Random Fields for automatic cloud/shadow detection on high resolution optical images , 2009 .

[47]  K. Mitchell,et al.  Impact of Atmospheric Surface-layer Parameterizations in the new Land-surface Scheme of the NCEP Mesoscale Eta Model , 1997 .

[48]  Johannes Schmetz,et al.  Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation , 2001 .

[49]  O. Kärner.  A multi-dimensional histogram technique for cloud classification , 2000 .

[50]  Eric A. Smith,et al.  ISCCP Cloud Algorithm Intercomparison. , 1985 .

[51]  J. Otkin,et al.  Comparison of WRF Model-Simulated and MODIS-Derived Cloud Data , 2008 .

[52]  Bo Yi Lin,et al.  Radiometric normalization and cloud detection of optical satellite images using invariant pixels , 2015 .

[53]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[54]  Lin Sun,et al.  A Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a prior surface reflectance database , 2016 .

[55]  François Anctil,et al.  Neural network application for cloud detection in SPOT VEGETATION images , 2006 .

[56]  Kuolin Hsu,et al.  On an Enhanced PERSIANN-CCS Algorithm for Precipitation Estimation , 2012 .

[57]  S. Sorooshian,et al.  An Artificial Neural Network Model to Reduce False Alarms in Satellite Precipitation Products Using MODIS andCloudSatObservations , 2013 .

[58]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[59]  Fuzhong Weng,et al.  JCSDA Community Radiative Transfer Model (CRTM) : version 1 , 2006 .

[60]  W. Paul Menzel,et al.  INTRODUCING THE NEXT-GENERATION ADVANCED BASELINE IMAGER ON GOES-R , 2005 .

[61]  Bert Guindon,et al.  Quantitative assessment of a haze suppression methodology for satellite imagery: effect on land cover classification performance , 2003, IEEE Trans. Geosci. Remote. Sens..

[62]  Kuolin Hsu,et al.  Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation , 1999 .

[63]  Gérard Dedieu,et al.  A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images , 2010 .

[64]  Thomas Blaschke,et al.  Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[65]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

[66]  W. Rossow,et al.  Cloud Detection Using Satellite Measurements of Infrared and Visible Radiances for ISCCP , 1993 .

[67]  G. Thompson,et al.  Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization , 2008 .

[68]  Jonathan Muñoz,et al.  Watershed image segmentation and cloud classification from multispectral MSG–SEVIRI imagery , 2012 .

[69]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[70]  Robert J. Kuligowski,et al.  A Self-Calibrating Real-Time GOES Rainfall Algorithm for Short-Term Rainfall Estimates , 2002 .

[71]  Guy Rochard,et al.  Unsupervised segmentation of low clouds from infrared METEOSAT images based on a contextual spatio-temporal labeling approach , 2002, IEEE Trans. Geosci. Remote. Sens..

[72]  Kuolin Hsu,et al.  Satellite‐based precipitation estimation using watershed segmentation and growing hierarchical self‐organizing map , 2006 .

[73]  Matematiksel Morfoloji,et al.  MORPHOLOGICAL IMAGE PROCESSİNG WITH FUZZY LOGIC , 2006 .

[74]  David J. Stensrud,et al.  Assimilating All-Sky Infrared Radiances from GOES-16 ABI Using an Ensemble Kalman Filter for Convection-Allowing Severe Thunderstorms Prediction , 2018, Monthly Weather Review.

[75]  Pierre Soille,et al.  Advances in mathematical morphology applied to geoscience and remote sensing , 2002, IEEE Trans. Geosci. Remote. Sens..

[76]  Kuolin Hsu,et al.  PERSIANN-MSA: A Precipitation Estimation Method from Satellite-Based Multispectral Analysis , 2009 .

[77]  A. Ihler,et al.  A Deep Neural Network Modeling Framework to Reduce Bias in Satellite Precipitation Products , 2016 .

[78]  Jörg Bendix,et al.  Cloud detection in the Tropics--a suitable tool for climate-ecological studies in the high mountains of Ecuador , 2004 .

[79]  J. Mecikalski,et al.  Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery , 2004 .

[80]  W. Paul Menzel,et al.  Cloud Detection of MODIS Multispectral Images , 2014 .