Traditional and neural probabilistic multispectral image processing for the dust aerosol detection problem

Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

Volumn , Issue , 2010, Pages 169-172

  Rivas Perea, P ^a   Rosiles, J G ^a   Chacon M , M I ^b  

^a University of Texas at El Paso   (United States)

^b Chihuahua Institute of Technology   (Mexico)

Author keywords

Image processing; Maximum likelihood classification; Neural networks; Remote sensing

Indexed keywords

1KM RESOLUTION; ATMOSPHERIC COMPONENTS; CLASSIFICATION PERFORMANCE; DATA SETS; DUST AEROSOLS; GROUND TRUTH; MANUAL SEGMENTATION; MAXIMUM LIKELIHOOD CLASSIFICATION; MAXIMUM LIKELIHOOD CLASSIFICATIONS; MAXIMUM LIKELIHOOD CLASSIFIERS; MULTI-SPECTRAL; MULTI-SPECTRAL IMAGE ANALYSIS; MULTISPECTRAL IMAGE PROCESSING; NASA TERRA SATELLITE; PROBABILISTIC NEURAL NETWORKS; REALTIME PROCESSING;

ALGORITHMS; ATMOSPHERIC AEROSOLS; CLASSIFIERS; DUST; IMAGE ANALYSIS; IMAGING SYSTEMS; MAXIMUM LIKELIHOOD ESTIMATION; NASA; QUADRATURE PHASE SHIFT KEYING; REMOTE SENSING;

NEURAL NETWORKS;

EID: 77954804738     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSIAI.2010.5483890     Document Type: Conference Paper

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