NDLI: Contextual Image Classification Based on Spatial Boosting

Content Provider	IEEE Xplore Digital Library
Author	Nishii, R.
Copyright Year	2006
Description	Author affiliation: Fac. of Math., Kyushu Univ., Fukuoka (Nishii, R.)
Abstract	Spatial AdaBoost proposed by Nishii and Eguchi (TGRS, 2005) is a supervised image classification method. It is a voting machine based on log posterior probabilities at a test pixel and its neighbors. The method can be obtained by less computation effort with respect to a classifier based on Markov random fields, but still shows a similar excellent performance. Further, the method was modified for applying various settings. This paper considers another extension of Spatial Boost. Consider supervised image classification of geospatial data. Suppose that separated training regions with a single land-cover class are given. In this case, the original Spatial Boost does not work because it incorporates spatial information of the training data. The aim of the paper is to make Spatial Boost applicable to the case. We propose a classifier given by a linear combination of log posteriors whose coefficients are determined by spatial information of test data only. By numerical examples, it shows an excellent performance.
Starting Page	2137
Ending Page	2140
File Size	206903
Page Count	4
File Format	PDF
ISBN	0780395107
DOI	10.1109/IGARSS.2006.553
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image classification Boosting Training data Voting Markov random fields Mathematics Benchmark testing Robustness Spatiotemporal phenomena Pixel
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

Contextual Unmixing of Geospatial Data based on Gaussian Mixture Models and Markov Random Fields

Contextual unmixing of geospatial data based on Markov random fields and conditional random fields

Texture classification based on spatial dependence features using co-occurrence matrices and Markov random fields

Semi-supervised contextual classification and unmixing of hyperspectral data based on mixture distributions

A nonparametric contextual classification based on Markov random fields

Spatio-temporal contextual image classification based on Spatial AdaBoost

Fuzzy Super Resolution Mapping Based on Markov Random Fields

Fusion of contextual classification and the existing classification result

Texture classification using nonparametric Markov random fields

Contextual Image Classification Based on Spatial Boosting

Similar Documents

Contextual Unmixing of Geospatial Data based on Gaussian Mixture Models and Markov Random Fields

Contextual unmixing of geospatial data based on Markov random fields and conditional random fields

Texture classification based on spatial dependence features using co-occurrence matrices and Markov random fields

Semi-supervised contextual classification and unmixing of hyperspectral data based on mixture distributions

A nonparametric contextual classification based on Markov random fields

Spatio-temporal contextual image classification based on Spatial AdaBoost

Fuzzy Super Resolution Mapping Based on Markov Random Fields

Fusion of contextual classification and the existing classification result

Texture classification using nonparametric Markov random fields

Contextual Image Classification Based on Spatial Boosting