Mixture model for multiple instance regression and applications in remote sensing

IEEE Transactions on Geoscience and Remote Sensing

Volumn 50, Issue 6, 2012, Pages 2226-2237

  Wang, Zhuang ^a,b   Lan, Liang ^a   Vucetic, Slobodan ^a  

^a Temple University   (United States)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

Aerosol retrieval; expectation maximization; mixture model; Moderate Resolution Imaging Spectroradiometer (MODIS); Multi angle Imaging pectroRadiometer (MISR); multiple instance learning (MIL); multiple instance regression (MIR); neural networks; remote sensing

Indexed keywords

AEROSOL RETRIEVAL; EXPECTATION MAXIMIZATION; MIXTURE MODEL; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER; MULTI-ANGLE; MULTIPLE INSTANCES; MULTIPLE-INSTANCE LEARNING;

AGRICULTURE; ALGORITHMS; ATMOSPHERIC AEROSOLS; IMAGE SEGMENTATION; NEURAL NETWORKS; RADIOMETERS; REGRESSION ANALYSIS; REMOTE SENSING; SATELLITE IMAGERY;

MIXTURES;

ACCURACY ASSESSMENT; AEROSOL; ALGORITHM; ARTIFICIAL NEURAL NETWORK; CROP YIELD; DATA SET; MODIS; NUMERICAL MODEL; REGRESSION ANALYSIS; REMOTE SENSING;

EID: 84861341692     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2011.2171691     Document Type: Article

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