Unsupervised segmentation of hyperspectral images

Sangwook Lee, Chulhee Lee

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

3 Citations (Scopus)

Abstract

In this paper, we propose a new unsupervised segmentation method for hyperspectral images using edge fusion. We first remove noisy spectral band images by examining the correlations between the spectral bands. Then, the Canny algorithm is applied to the retained images. This procedure produces a number of edge images. To combine these edge images, we compute an average edge image and then apply a thresholding operation to obtain a binary edge image. By applying dilation and region filling procedures to the binary edge image, we finally obtain a segmented image. Experimental results show that the proposed algorithm produced satisfactory segmentation results without requiring user input.

Original languageEnglish
Title of host publicationSatellite Data Compression, Communication, and Processing IV
DOIs
Publication statusPublished - 2008 Nov 21
EventSatellite Data Compression, Communication, and Processing IV - San Diego, CA, United States
Duration: 2008 Aug 102008 Aug 11

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7084
ISSN (Print)0277-786X

Other

OtherSatellite Data Compression, Communication, and Processing IV
CountryUnited States
CitySan Diego, CA
Period08/8/1008/8/11

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Lee, S., & Lee, C. (2008). Unsupervised segmentation of hyperspectral images. In Satellite Data Compression, Communication, and Processing IV [70840B] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 7084). https://doi.org/10.1117/12.795807