Unsupervised multispectral image classification

Shih Yu Chen, Chinsu Lin, Yen Chieh Ouyang, Chein I. Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a new approach to unsupervised classification for multispectral imagery. It first uses a Gaussian pyramid multi-resolution technique to reduce image size from which the pixel purity index (PPI) is implemented to find regions of interest (ROIs) with PPI counts greater than zero. These PPI-found samples are further used as support vectors for a support vector machine (SVM) to classify data. The resulting SVM-classified data samples are further processed by a new designed iterative Fisher's linear discriminate analysis (IFLDA) which implements FLDA in an iterative manner to refine classification results. The experimental results show the proposed approach has great promise in unsupervised classification.

Original languageEnglish
Title of host publication2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012
PublisherIEEE Computer Society
ISBN (Print)9781479934065
DOIs
Publication statusPublished - 2012
Event2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012 - Shanghai, China
Duration: 2012 Jun 42012 Jun 7

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
ISSN (Print)2158-6276

Conference

Conference2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012
Country/TerritoryChina
CityShanghai
Period12-06-0412-06-07

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing

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