Combined intensity and fractal information for neural classification of remote sensing imagery

Kun S. Chen, C. F. Chen, D. W. Tsay

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

Abstract

This paper presents the results of the terrain cover classification from satellite imagery from multispectral SPOT high resolution visible images and ERS-1 C-band SAR image. Fractal image was extracted using, from SAR, a wavelet transform as texture measure. The use of SAR fractal image to combine with SPOT data for terrain cover classification is proved to be effective and efficient, in that for SAR the despeckle process is avoided and thus naturally preserves its texture information. It was found that fractal information significantly improves the discrimination capability of the heterogeneous areas such as in urban regions, while it slightly degrades accuracy for homogeneous areas, such as open water. The overall classification performance is superior to results obtained using intensity image only.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsJacky Desachy
Pages225-232
Number of pages8
StatePublished - 1995
EventImage and Signal Processing for Remote Sensing II - Paris, Fr
Duration: 25 Sep 199527 Sep 1995

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume2579
ISSN (Print)0277-786X

Conference

ConferenceImage and Signal Processing for Remote Sensing II
CityParis, Fr
Period25/09/9527/09/95

Fingerprint

Dive into the research topics of 'Combined intensity and fractal information for neural classification of remote sensing imagery'. Together they form a unique fingerprint.

Cite this