On the use of a cluster ensemble cloud classification technique in satellite precipitation estimation

Majid Mahrooghy; Nicolas H. Younan; Valentine G. Anantharaj; James Aanstoos; Shantia Yarahmadian

doi:10.1109/JSTARS.2012.2201449

On the use of a cluster ensemble cloud classification technique in satellite precipitation estimation

Majid Mahrooghy, Nicolas H. Younan, Valentine G. Anantharaj, James Aanstoos, Shantia Yarahmadian

Data Lifecycle Technologies

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18 Scopus citations

Abstract

In this paper, the link-based cluster ensemble (LCE) method is utilized to improve cloud classification and satellite precipitation estimation. High resolution Satellite Precipitation Estimation (SPE) is based on the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. This modified SPE with the incorporation of LCE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering cloud patches using LCE; and 4) dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using satellite observations. In order to cluster the cloud patches, the LCE method combines multiple data partitions from different clustering methods. The results show that using the cluster ensemble increases the performance of rainfall estimates compared to the SPE algorithm using a Self Organizing Map (SOM) neural network. The false alarm ratio (FAR), probabilities of detection (POD), equitable threat score (ETS), and bias are used as quantitative measures to assess the performance of the algorithm. It is shown that both the ETS and bias provide improvement in the summer and winter seasons. Almost 5% ETS improvement is obtained at some threshold values for the winter season using the cluster ensemble.

Original language	English
Article number	6236228
Pages (from-to)	1356-1363
Number of pages	8
Journal	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume	5
Issue number	5
DOIs	https://doi.org/10.1109/JSTARS.2012.2201449
State	Published - 2012

Funding

Manuscript received November 15, 2011; revised April 17, 2012 and May 01, 2012; accepted May 17, 2012. Date of publication July 10, 2012; date of current version November 14, 2012. This work was supported by the NASA Applied Sciences Program under Grant NNS06AA98B and the NOAA Office of Atmospheric Research via Grant NA07OAR4170517. The work of V. Anantharaj was also supported by the Oak Ridge Leadership Computing Facility under the auspices of the Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy under Contract DE-AC05-00OR22725 and Contract DE-AC05-00OR22725 with UT-Battelle, LLC.

Funders	Funder number
NOAA Office of Atmospheric Research	NA07OAR4170517
Oak Ridge National Laboratory
U.S. Department of Energy	DE-AC05-00OR22725
National Aeronautics and Space Administration	NNS06AA98B
Office of Science
Advanced Scientific Computing Research

Keywords

Cluster ensemble
Feature extraction
Satellite precipitation estimation
Self organizing map

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10.1109/JSTARS.2012.2201449

Cite this

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title = "On the use of a cluster ensemble cloud classification technique in satellite precipitation estimation",

abstract = "In this paper, the link-based cluster ensemble (LCE) method is utilized to improve cloud classification and satellite precipitation estimation. High resolution Satellite Precipitation Estimation (SPE) is based on the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. This modified SPE with the incorporation of LCE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering cloud patches using LCE; and 4) dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using satellite observations. In order to cluster the cloud patches, the LCE method combines multiple data partitions from different clustering methods. The results show that using the cluster ensemble increases the performance of rainfall estimates compared to the SPE algorithm using a Self Organizing Map (SOM) neural network. The false alarm ratio (FAR), probabilities of detection (POD), equitable threat score (ETS), and bias are used as quantitative measures to assess the performance of the algorithm. It is shown that both the ETS and bias provide improvement in the summer and winter seasons. Almost 5% ETS improvement is obtained at some threshold values for the winter season using the cluster ensemble.",

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On the use of a cluster ensemble cloud classification technique in satellite precipitation estimation. / Mahrooghy, Majid; Younan, Nicolas H.; Anantharaj, Valentine G. et al.
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 5, No. 5, 6236228, 2012, p. 1356-1363.

Research output: Contribution to journal › Article › peer-review

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AU - Aanstoos, James

AU - Yarahmadian, Shantia

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AB - In this paper, the link-based cluster ensemble (LCE) method is utilized to improve cloud classification and satellite precipitation estimation. High resolution Satellite Precipitation Estimation (SPE) is based on the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. This modified SPE with the incorporation of LCE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering cloud patches using LCE; and 4) dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using satellite observations. In order to cluster the cloud patches, the LCE method combines multiple data partitions from different clustering methods. The results show that using the cluster ensemble increases the performance of rainfall estimates compared to the SPE algorithm using a Self Organizing Map (SOM) neural network. The false alarm ratio (FAR), probabilities of detection (POD), equitable threat score (ETS), and bias are used as quantitative measures to assess the performance of the algorithm. It is shown that both the ETS and bias provide improvement in the summer and winter seasons. Almost 5% ETS improvement is obtained at some threshold values for the winter season using the cluster ensemble.

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On the use of a cluster ensemble cloud classification technique in satellite precipitation estimation

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