Hybrid hierarchical classifiers for hyperspectral data analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5519 LNCS, Issue , 2009, Pages 42-51

  Jun, Goo ^a   Ghosh, Joydeep ^a  

^a The University of Texas at Austin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFICATION; BINARY CLASSIFIERS; BINARY HIERARCHICAL CLASSIFIERS; CLASS HIERARCHIES; CLASSIFICATION ACCURACY; DATA SETS; HIERARCHICAL CLASSIFIERS; HIGH DIMENSIONAL SPACES; HYBRID ALGORITHMS; HYPERSPECTRAL; HYPERSPECTRAL DATA ANALYSIS; MULTI-CLASS PROBLEMS; SAMPLE SIZES; TREE ALGORITHMS;

BINARY TREES; CLASSIFIERS;

LEARNING SYSTEMS;

EID: 70349312673     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02326-2_5     Document Type: Conference Paper

References (10)

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10. Ham, J., Chen, Y., Crawford, M.M., Ghosh, J.: Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans. Geosci. and Remote Sens. 43(3), 492-501 (2005)