Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery
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Title
Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery
Authors
Keywords
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Journal
Remote Sensing
Volume 10, Issue 4, Pages 570
Publisher
MDPI AG
Online
2018-04-11
DOI
10.3390/rs10040570
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- (2014) A. Hueni et al. APPLIED OPTICS
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- (2014) Fabian E. Fassnacht et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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- (2013) Aniruddha Ghosh et al. International Journal of Applied Earth Observation and Geoinformation
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- Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data
- (2010) Björn Waske et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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- Parameter determination of support vector machine and feature selection using simulated annealing approach
- (2007) Shih-Wei Lin et al. APPLIED SOFT COMPUTING
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