@inproceedings{d9618cea69c441afbaed74b111a7aeb9,
title = "High resolution satellite precipitation estimate using cluster ensemble cloud classification",
abstract = "The link-based cluster ensemble (LCE) method is applied to a high resolution satellite precipitation estimation (HSPE) algorithm, a modified form of the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. The HSPE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering and classification of cloud patches using cluster ensemble technique; and 4) dynamic application of brightness temperature (Tb) and rain rate relationships, derived using satellite observations. The LCE method combines multiple data partitions from different clustering in order to cluster the cloud patches. The results show that using the cluster ensemble increase the performance of rainfall estimates if compared to the HSPE algorithm using Self Organizing Map (SOM). The Heidke Skill Score (HSS) is improved 5\% to 7\% at medium and high level of rainfall thresholds.",
keywords = "Clustering method, feature extraction, image texture analysis, neural networks",
author = "Majid Mahrooghy and Younan, \{Nicolas H.\} and Anantharaj, \{Valentine G.\} and James Aanstoos",
year = "2011",
doi = "10.1109/IGARSS.2011.6049746",
language = "English",
isbn = "9781457710056",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2645--2648",
booktitle = "2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings",
note = "2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 ; Conference date: 24-07-2011 Through 29-07-2011",
}