Title
Multiple-kernel learning-based unmixing algorithm for estimation of cloud fractions with MODIS and CloudSat data
Abstract
Detection of clouds in satellite-generated radiance images, including those from MODIS, is an important first step in many applications of these data. In this paper we apply spectral unmixing to this problem with the aim of estimating subpixel cloud fractions, as opposed to identification only of whether or not a pixel radiance contains cloud contributions. We formulate the spectral unmixing approach in terms of multiple-kernel learning (MKL). To this end we propose a MKL-based unmixing algorithm that drives a multiple-kernel description of cloud, enabling estimation of sub-pixel cloud fractions. This approach is based on supervised learning. We generate training and testing samples by using CloudSat and CALIPSO data to compute cloud fractions within individual MODIS pixels. Results of our study on limited data (1875 training and testing MODIS pixels along with their CloudSat and CALIPSO based sub-pixel cloud fractions) show that the proposed algorithm can effectively estimate sub-pixel MODIS cloud fraction and outperforms support vector machine (SVM) in terms of estimation performance.
Year
DOI
Venue
2012
10.1109/IGARSS.2012.6351167
IGARSS
Keywords
DocType
Volume
remote sensing,cloud detection,modis,learning (artificial intelligence),spectral unmixing algorithm,spectral unmixing,mkl based unmixing algorithm,multiple-kernel learning (mkl),multiple kernel learning,clouds,calipso data,modis data,geophysical image processing,cloudsat data,object detection,spectral unmixing approach,subpixel cloud fraction estimation,satellite generated radiance images,support vector machines,kernel,learning artificial intelligence,estimation,vectors
Conference
null
Issue
ISSN
ISBN
null
2153-6996 E-ISBN : 978-1-4673-1158-8
978-1-4673-1158-8
Citations 
PageRank 
References 
3
0.51
5
Authors
6
Name
Order
Citations
PageRank
Yanfeng Gu174255.56
Shizhe Wang2863.29
Tao Shi330.51
Yinghui Lu4283.16
Eugene E. Clothiaux5193.69
Bin Yu61984241.03