NDLI: Hyperspectral Image Classification Using Functional Data Analysis

Content Provider	IEEE Xplore Digital Library
Author	Hong Li Guangrun Xiao Tian Xia Tang, Y.Y. Luoqing Li
Copyright Year	2013
Abstract	The large number of spectral bands acquired by hyperspectral imaging sensors allows us to better distinguish many subtle objects and materials. Unlike other classical hyperspectral image classification methods in the multivariate analysis framework, in this paper, a novel method using functional data analysis (FDA) for accurate classification of hyperspectral images has been proposed. The central idea of FDA is to treat multivariate data as continuous functions. From this perspective, the spectral curve of each pixel in the hyperspectral images is naturally viewed as a function. This can be beneficial for making full use of the abundant spectral information. The relevance between adjacent pixel elements in the hyperspectral images can also be utilized reasonably. Functional principal component analysis is applied to solve the classification problem of these functions. Experimental results on three hyperspectral images show that the proposed method can achieve higher classification accuracies in comparison to some state-of-the-art hyperspectral image classification methods.
Page Count	12
File Size	18025580
Starting Page	1544
Ending Page	1555
File Format	PDF
ISSN	21682267
Volume Number	44
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Support vector machines Splines (mathematics) Feature extraction Kernel Principal component analysis support vector machines (SVM) Functional data analysis (FDA) functional data representation functional principal component analysis (FPCA) hyperspectral image classification
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Kernel Association for Classification and Prediction: A Survey

Region-Kernel-Based Support Vector Machines for Hyperspectral Image Classification

Using FCMC, FVS, and PCA techniques for feature extraction of multispectral images

Kernel PCA feature extraction and the SVM classification algorithm for multiple-status, through-wall, human being detection

Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification

Semisupervised Dimensionality Reduction With Pairwise Constraints for Hyperspectral Image Classification

Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification

A Composite Semisupervised SVM for Classification of Hyperspectral Images

Remote Sensing Feature Selection by Kernel Dependence Measures

Hyperspectral Image Classification Using Functional Data Analysis

Similar Documents

Kernel Association for Classification and Prediction: A Survey

Region-Kernel-Based Support Vector Machines for Hyperspectral Image Classification

Using FCMC, FVS, and PCA techniques for feature extraction of multispectral images

Kernel PCA feature extraction and the SVM classification algorithm for multiple-status, through-wall, human being detection

Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification

Semisupervised Dimensionality Reduction With Pairwise Constraints for Hyperspectral Image Classification

Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification

A Composite Semisupervised SVM for Classification of Hyperspectral Images

Remote Sensing Feature Selection by Kernel Dependence Measures

Hyperspectral Image Classification Using Functional Data Analysis