HIGH RESOLUTION SATELLITE PRECIPITATION ESTIMATE USING CLUSTER ENSEMBLE CLOUD CLASSIFICATION

被引：2
|
作者：
Mahrooghy, Majid [1 ,2 ]
Younan, Nicolas H. [1 ,2 ]
Anantharaj, Valentine G. [3 ]
Aanstoos, James [2 ]
机构：
[1] Mississippi State Univ, Dept Elect Engn, Mississippi State, MS 39762 USA
[2] Mississippi State Univ, Geosyst Res Inst, Mississippi State, MS USA
[3] Natl Ctr Computat Sci, Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA
来源：
2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2011年
关键词：
Clustering method; neural networks; feature extraction; image texture analysis; CONSENSUS; PARTITIONS; MODELS; SYSTEM;
D O I：
10.1109/IGARSS.2011.6049746
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：2645 / 2648
页数：4
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