Towards the development of next generation remote sensing technology - Erdas imagine incorporates a higher order feature extraction technoque based on ICA

American Society for Photogrammetry and Remote Sensing - ASPRS Annual Conference 2007: Identifying Geospatial Solutions

Volumn 2, Issue , 2007, Pages 666-677

  Shah, Chintan A ^a   Anderson, Ian ^b   Gou, Zaiyong ^b   Hao, Steven ^b   Leason, Andrew ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Leica Geosystems AG   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS TECHNIQUES; ANOMALY DETECTION; DATA DIMENSIONALITY; DATA SPACE; DISCRIMINABILITY; FEATURE EXTRACTION TECHNIQUES; FEATURE SPACE; FUNDAMENTAL LIMITATIONS; IMAGE DATA; INFORMATION CONTENTS; INTER-BAND CORRELATION; LEICA GEOSYSTEMS; MATHEMATICAL FRAMEWORKS; MINIMUM NOISE FRACTION; MULTIVARIATE GAUSSIAN DISTRIBUTIONS; OPTIMAL SUBSETS; POTENTIAL BENEFITS; REMOTE SENSING DATA; REMOTE SENSING IMAGERY; REMOTE SENSING TECHNOLOGY; SENSING FEATURES; SPECTRAL CHARACTERISTICS; SPECTRAL UNMIXING; STATISTICAL CHARACTERISTICS; STATISTICAL PATTERN RECOGNITION; SURFACE MATERIALS; THEORETICAL FORMULATION;

DATA REDUCTION; FEATURE EXTRACTION; INDEPENDENT COMPONENT ANALYSIS; PARAMETER ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING;

SPACE OPTICS;

EID: 84868612652     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (5)

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3. Hughes, G. F. (1968). On the mean accuracy of statistical pattern recognition. IEEE Trans. Inform. Theory, 14: 55- 63.
4. Jia, X. and J. A. Richards (1999). Remote Sensing Digital Image Analysis: An Introduction. 3rd ed., Berlin, NY: Springer Verlag.
5. Shah, C. A. (2003). ICA Mixture Model Algorithm For Unsupervised Classification of Multi/Hyperspectral Imagery. Masters Thesis, Syracuse University, Syracuse, NY.