NDLI: The analysis about factors influencing the supervised classification accuracy for vegetation hyperspectral remote sensing imagery

Content Provider	IEEE Xplore Digital Library
Author	Wang Meng Zhang Lianpeng Chen Shichen Ma Weiwei Guo Yangyang
Copyright Year	2011
Description	Author affiliation: School of Geodesy and Geomatics, Xuzhou Normal University, China (Wang Meng; Zhang Lianpeng; Chen Shichen; Ma Weiwei; Guo Yangyang)
Abstract	Classification of hyperspectral imagery has drawn much attention in recent years since the development of hyperspectral sensor. The hyperspectral sensor may offer hundreds of contiguous and very narrow spectral channels which may detect more detailed classes and improve the classification accuracy. However, with the increasing dimensionalities of classification data space, the parameters in the supervised classification models are also increasing quickly, which will need much more numbers of training samples to ensure the parameters estimation accuracy. Most recent researches focus on developing novel classification algorithms to improve the hyperspectral imagery processing performance. There are still lack of systematic researches about the factors (such as numbers and distributions of training samples, classification algorithms, dimensionalities of feature space) influencing classification accuracy for vegetation hyperspectral imagery so far. To analyze the factors, firstly, two methods for dimensionality reduction are proposed based on correlation coefficient and spectrum curve inflection points; On this basis, we use region of interest (ROI) constructed by different training numbers of pixels and different distributions to classify OMIS aerial hyperspectral image of ZaoYuan Town of Yanan City by several often used supervised classification algorithms; then analyze the relationships among classifiers and training samples and feature space dimensionality. This research shows that the classifiers based on Mahal distance and Maximum Likelihood (ML) that use secondary moment are superior to Euclid distance (ED), parallelepiped (PP) and spectral angle mapper (SAM); when the numbers of training samples are large enough, dimensionality reduction makes little influence on classification accuracy; when the numbers of training samples are limited, the influence is distinct on Mahal distance and ML but not distinct on Euclidean distance, PP and SAM.
Starting Page	1685
Ending Page	1689
File Size	354675
Page Count	5
File Format	PDF
ISBN	9781424493043
e-ISBN	9781424493067
DOI	10.1109/CISP.2011.6100493
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training demensionality reduction hyperspectral remote sensing Accuracy Correlation Image color analysis feature space supervised classification Hyperspectral imaging
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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