NDLI: Dynamic classifier system for hyperspectral image classification

Content Provider	IEEE Xplore Digital Library
Author	Bhushan, D.B. Nidamanuri, R.R.
Copyright Year	2013
Description	Author affiliation: Dept. of Earth & Space Sci., Indian Inst. of Space Sci. & Technol., Thiruvananthapuram, India (Bhushan, D.B.; Nidamanuri, R.R.)
Abstract	Multiple classifier system (MCS) is one of the effective strategies for hyperspectral image classification. Deploying different dimensionality reduction methods as the input data source to the MCS creates diversity among the base classifiers. The performance of the MCS is guaranteed when the base classifiers are accurate and diverse. Moreover the presence of the bad classifiers may negatively influence the performance of the MCS. In order to form a strong MCS, which are accurate as well as diverse, in this work the dynamic classifier system is developed. The dynamic classifier system selects the adaptive classifier from a pool of classifier for each dimensionality reduction method. The selected classifier relative to each dimensionality reduction method is further combined by different combination functions. Our experimental results on five multi-site hyperspectral images show the potential of dynamic classifier system to increase the classification accuracy significantly.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	1039
Ending Page	1042
File Size	1571419
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6721341
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Abstracts Integrated optics Optical imaging Hyperspectral imaging Accuracy Support vector machines dimensionality reduction methods Hyperspectral image classification multiple classifier system dynamic classifier system
Content Type	Text
Resource Type	Article

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