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Geostatistical classification for remote sensing:an introduction

Atkinson, Peter M. and Lewis, P. (2000) Geostatistical classification for remote sensing:an introduction. Computers and Geosciences, 26 (4). pp. 361-371. ISSN 0098-3004

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Abstract

Traditional spectral classification of remotely sensed images applied on a pixel-by-pixel basis ignores the potentially useful spatial information between the values of proximate pixels. For some 30 years the spatial information inherent in remotely sensed images has been employed, albeit by a limited number of researchers, to enhance spectral classification. This has been achieved primarily by filtering the original imagery to (i) derive texture ‘wavebands’ for subsequent use in classification or (ii) smooth the imagery prior to (or after) classification. Recently, the variogram has been used to represent formally the spatial dependence in remotely sensed images and used in texture classification in place of simple variance filters. However, the variogram has also been employed in soil survey as a smoothing function for unsupervised classification. In this review paper, various methods of incorporating spatial information into the classification of remotely sensed images are considered. The focus of the paper is on the variogram in classification both as a measure of texture and as a guide to choice of smoothing function. In the latter case, the paper focuses on the technique developed for soil survey and considers the modification that would be necessary for the remote sensing case.

Item Type: Article
Journal or Publication Title: Computers and Geosciences
Additional Information: M1 - 4
Uncontrolled Keywords: Variogram ; Classification ; Geostatistics ; Smoothing ; Texture
Subjects:
Departments: Faculty of Science and Technology
ID Code: 77164
Deposited By: ep_importer_pure
Deposited On: 15 Dec 2015 16:08
Refereed?: Yes
Published?: Published
Last Modified: 21 Sep 2017 01:19
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/77164

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