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Volumn 5, Issue 1, 2008, Pages
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Overcoming the small sample size problem in hyperspectral classification and detection tasks
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Author keywords
Feature extraction; Hyperspectral; Pattern classification; Target recognition
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Indexed keywords
CLASS SEPARATION;
CLASSIFICATION PERFORMANCE;
CLASSIFICATION SYSTEM;
DECISION BOUNDARY;
DECISION FUSION;
DETECTION TASKS;
DIMENSIONALITY REDUCTION TECHNIQUES;
FALSE ALARM RATE;
GENERALIZATION CAPACITY;
GRAY SCALE;
HIGH-DIMENSIONAL FEATURE SPACE;
HYPER-SPECTRAL CLASSIFICATION;
HYPERSPECTRAL;
HYPERSPECTRAL DATA;
HYPERSPECTRAL SIGNATURES;
MULTI-CLASSIFIER;
MULTI-SPECTRAL IMAGERY;
OVERFITTING;
PATTERN CLASSIFICATION;
REFLECTANCE VALUES;
REGULARIZED LDA;
SMALL SAMPLE SIZE PROBLEMS;
SPATIAL RELATIONS;
SPECIFIC INFORMATION;
SPECTRAL BAND;
SPECTRAL INFORMATION;
TARGET RECOGNITION;
TRAINING SETS;
ALARM SYSTEMS;
CLASSIFIERS;
FACE RECOGNITION;
LEARNING SYSTEMS;
REFLECTION;
REMOTE SENSING;
TARGETS;
FEATURE EXTRACTION;
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EID: 67649805072
PISSN: None
EISSN: None
Source Type: Conference Proceeding
DOI: 10.1109/IGARSS.2008.4780108 Document Type: Conference Paper |
Times cited : (21)
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References (8)
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