NDLI: Subspace clustering based on decision fusion strategy for hyperspectral imagery

Content Provider	IEEE Xplore Digital Library
Author	Jiao Hongzan Zhong Yanfei Zhang Liangpei Li Pingxiang
Copyright Year	2013
Description	Author affiliation: Sch. of Urban Design, Wuhan Univ., Wuhan, China (Jiao Hongzan) \|\| State Key Lab. of Inf. Eng. in Surveying, Mapping, & Remote Sensing, Wuhan Univ., Wuhan, China (Zhong Yanfei; Zhang Liangpei; Li Pingxiang)
Abstract	In this paper, a novel hyperspectral subspace clustering algorithm based on decision fusion strategy (SCDFS) is proposed. Because the different clusters are contained in different subspace of the same hyper-dimensional data, the clustering processing in different subspace is conducted by genetic K-means algorithm (KGA). The clustering results from different subspace can be combined into decision string. The proposed subspace clustering based on decision fusion strategy is conducted on decision string. Considering the selection of subspace, the decision results may be inaccurate. So by the majority voting processing for different subspace, the steady subspace combination can be determined. Finally, the weighted strategy is introduced into SCDFS algorithm to evaluate the distance of different decision string, and determine the fusion clustering result.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	1485
Ending Page	1488
File Size	581012
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723067
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Clustering algorithms Roads Soil Classification algorithms Accuracy Majority voting processing Hyperspectral subspace clustering Decision fusion strategy
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

Weighted decision fusion for supervised and unsupervised hyperspectral image classification

Decision fusion for supervised and unsupervised hyperspectral image classification

A multiclassifier and decision fusion system for hyperspectral image classification

Data dependent adaptation for improved classification of hyperspectral imagery

Spatial probability membership value based ensemble: An improved tool for vegetation mapping

Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques

Optimizing Subspace SVM Ensemble for Hyperspectral Imagery Classification

Clustering based band selection for hyperspectral images

A random measure approach for context estimation in hyperspectral imagery

Subspace clustering based on decision fusion strategy for hyperspectral imagery

Similar Documents

Weighted decision fusion for supervised and unsupervised hyperspectral image classification

Decision fusion for supervised and unsupervised hyperspectral image classification

A multiclassifier and decision fusion system for hyperspectral image classification

Data dependent adaptation for improved classification of hyperspectral imagery

Spatial probability membership value based ensemble: An improved tool for vegetation mapping

Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques

Optimizing Subspace SVM Ensemble for Hyperspectral Imagery Classification

Clustering based band selection for hyperspectral images

A random measure approach for context estimation in hyperspectral imagery

Subspace clustering based on decision fusion strategy for hyperspectral imagery