Classification of hyperspectral data using support vector machine

IEEE International Conference on Image Processing

Volumn 1, Issue , 2001, Pages 882-885

  Zhang, J ^a   Zhang, Y ^a   Zhou, T ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; FEATURE EXTRACTION; IMAGE SEGMENTATION; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; STATISTICAL METHODS; VECTORS;

ADAPTIVE SUBSPACE DECOMPOSITION; AIRBORNE VISIBLE INFRARED IMAGING SPECTROMETER; HYPERSPECTRAL DATA CLASSIFICATION; KERNEL FUNCTION; SUPERVISED CLASSIFICATION METHODS; SUPPORT VECTOR MACHINE;

IMAGE PROCESSING;

EID: 0035163908     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. The airborne visible/infrared imaging spectrometer (AVIRIS)
2. Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification
3. Adaptive subspace decomposition and classification for hyperspectral images
4. Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach
5. Hyperspectral data analysis and supervised feature reduction via projection pursuit
6. Fusion classification of hyperspectral image based on adaptive subspace decomposition
7. Perceptual-based image fusion for hyperspectral data
8. A tutorial on support vector machines for pattern recognition
9. Comparing support vector machines with Gaussian kernels to radial basis function classifiers