NDLI: Fusion of spectral and spatial information by a novel SVM classification technique

Content Provider	IEEE Xplore Digital Library
Author	Bruzzone, L. Marconcini, M. Persello, C.
Copyright Year	2007
Description	Author affiliation: Dept. of Inf. & Commun. Technol., Trento (Bruzzone, L.; Marconcini, M.; Persello, C.)
Abstract	A novel context-sensitive semisupervised classification technique based on support vector machines is proposed. This technique aims at exploiting the SVM method for image classification by properly fusing spectral information with spatial- context information. This results in: i) an increased robustness to noisy training sets in the learning phase of the classifier; ii) a higher and more stable classification accuracy with respect to the specific patterns included in the training set; and iii) a regularized classification map. The main property of the proposed context sensitive semisupervised SVM $(CS^{4}VM)$ is to adaptively exploit the contextual information in the training phase of the classifier, without any critical assumption on the expected labels of the pixels included in the same neighborhood system. This is done by defining a novel context-sensitive term in the objective function used in the learning of the classifier. In addition, the proposed $CS^{4}VM$ can be integrated with a Markov random field (MRF) approach for exploiting the contextual information also to regularize the classification map. Experiments carried out on very high geometrical resolution images confirmed the effectiveness of the proposed technique.
Starting Page	4838
Ending Page	4841
File Size	504111
Page Count	4
File Format	PDF
ISBN	9781424412112
DOI	10.1109/IGARSS.2007.4423944
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Image classification Robustness Remote sensing Pixel Phase noise Markov random fields Image analysis Cost function remote sensing image classification context-sensitive classification semisupervised classification support vector machines
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