Unsupervised hyperspectral image classification algorithm by integrating spatial-spectral information

Belkacem Baassou, Mingyi He, Shaohui Mei, Yifan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

An integrated spatial-spectral information algorithm for hyper spectral image classification is proposed, which uses spatial pixel association (SPA)by exploiting spectral information divergence (SID), and spectral clustering to reduce regions number and improve classification accuracy. Moreover, a class boundary correction method is also developed to minimize the misclassified pixels at the edge of each class and to solve the problem of merged classes. Experiments with hyper spectral data demonstrate the effectiveness and advantages of the proposed frame work over some traditional methods in term of classification accuracy.

Original languageEnglish
Title of host publicationICALIP 2012 - 2012 International Conference on Audio, Language and Image Processing, Proceedings
Pages610-615
Number of pages6
DOIs
StatePublished - 2012
Event2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012 - Shanghai, China
Duration: 16 Jul 201218 Jul 2012

Publication series

NameICALIP 2012 - 2012 International Conference on Audio, Language and Image Processing, Proceedings

Conference

Conference2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012
Country/TerritoryChina
CityShanghai
Period16/07/1218/07/12

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