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Modelling-based feature selection for classification of forest structure using very high resolution multispectral imagery
- Source :
- IEEE International Conference on Systems, Man and Cybernetics, 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct 2013, Manchester, United Kingdom. ⟨10.1109/SMC.2013.732⟩, SMC
- Publication Year :
- 2013
- Publisher :
- HAL CCSD, 2013.
-
Abstract
- This paper presents a new feature selection method which aims to effectively and efficiently map remote sensing data. An automated texture-based modelling procedure of forest structure variables is at the core of our approach. We show that texture features that are highly correlated to genuine physical parameters of forest structure have potential for building reliable classifiers. We demonstrate the effectiveness of our modelling-based texture feature selection method in performing mapping of very high resolution forest images. Our method outperforms Random Forest variable importance in terms of classification accuracy and computational complexity.
- Subjects :
- 010504 meteorology & atmospheric sciences
Computer science
Multispectral image
Feature extraction
0211 other engineering and technologies
Feature selection
02 engineering and technology
01 natural sciences
Texture (geology)
remote sensing
Image texture
image texture
ComputingMilieux_MISCELLANEOUS
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Contextual image classification
business.industry
feature extraction
forestry
Pattern recognition
15. Life on land
Random forest
geophysical image processing
Artificial intelligence
business
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
image classification
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- IEEE International Conference on Systems, Man and Cybernetics, 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct 2013, Manchester, United Kingdom. ⟨10.1109/SMC.2013.732⟩, SMC
- Accession number :
- edsair.doi.dedup.....35389e2f353c850c255182458816721e