NDLI: Multi-class SVM based remote sensing image classification and its semi-supervised improvement scheme

Content Provider	IEEE Xplore Digital Library
Author	Heng-Nian Qi Jian-Gang Yang Yi-Wen Zhong Chao Deng
Copyright Year	2004
Description	Author affiliation: Inst. of Artificial Intelligence, Zhejiang Univ., China (Heng-Nian Qi; Jian-Gang Yang; Yi-Wen Zhong; Chao Deng)
Abstract	Support vector machine (SVM), which is based on statistical learning theory (SLT), has shown much better performance than most other existing machine learning methods, which are based on the traditional statistics. The original SVM was developed to solve the dichotomy classification problem. Various approaches have been presented to solve multi-class problems. Using multi-class SVM classifier we have obtained high class rate of 95.4% in remote sensing image classification. However for the class number of remote sensing image is much great, manually obtaining of training samples is a much time-consuming work. Hence, we present a multi-class SVM based semi-supervised approach. We choose the initial cluster centers manually first, then label the samples as the training ones automatically with fuzzy C-means clustering algorithm. It is believed that this method upgrades the classification efficiency greatly with practicable class rate.
Starting Page	3146
Ending Page	3151
File Size	553790
Page Count	6
File Format	PDF
ISBN	0780384032
DOI	10.1109/ICMLC.2004.1378575
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Remote sensing Image classification Clustering algorithms Risk management Chaos Artificial intelligence Forestry Educational institutions
Content Type	Text
Resource Type	Article

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