A comparison of local variance, fractal dimension, and Moran's I as aids to multispectral image classification
The accuracy of traditional multispectral maximum-likelihood image classification is limited by the multi-modal statistical distributions of digital numbers from the complex, heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension...
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Published in | International journal of remote sensing Vol. 26; no. 8; pp. 1575 - 1588 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Abingdon
Taylor & Francis Group
01.04.2005
Taylor and Francis |
Subjects | |
Online Access | Get full text |
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Summary: | The accuracy of traditional multispectral maximum-likelihood image classification is limited by the multi-modal statistical distributions of digital numbers from the complex, heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery with the goal of improving urban land cover classification accuracy. Tools available in the ERDAS Imagine
TM
software package and the Image Characterization and Modeling System (ICAMS) were used to analyse Landsat ETM + imagery of Atlanta, Georgia. Images were created from the ETM + panchromatic band using the three texture indices. These texture images were added to the stack of multispectral bands and classified using a supervised, maximum likelihood technique. Although each texture band improved the classification accuracy over a multispectral only effort, the addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per-pixel spectral classification techniques. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0143-1161 1366-5901 |
DOI: | 10.1080/01431160512331326765 |