Knowledge-based semi-supervised satellite image classification

Bilal Al Momani, Philip Morrow, Sally McClean

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

3 Citations (Scopus)

Abstract

Spectral information on its own has proven to be insufficient for classification of remotely sensed images. In general, it is difficult to distinguish between types of land-cover classes that have similar or identical spectral signatures from remotely sensed data. Contextual data can be dasiafusedpsila with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster-Shafer theory of evidence to fuse the output of a semi-supervised classification (SSC) technique with contextual data in the form of a digital elevation model. The final classification accuracy is shown to improve when using this approach.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages1-4
Number of pages4
DOIs
Publication statusPublished - Feb 2007
Event9th International Symposium on Signal Processing and Its Applications, 2007 (ISSPA 2007) -
Duration: 1 Feb 2007 → …

Conference

Conference9th International Symposium on Signal Processing and Its Applications, 2007 (ISSPA 2007)
Period1/02/07 → …

Fingerprint

image classification
digital elevation model
land cover
satellite image

Cite this

@inproceedings{f2280629a3e84efdb9b7394fbbbb7ddd,
title = "Knowledge-based semi-supervised satellite image classification",
abstract = "Spectral information on its own has proven to be insufficient for classification of remotely sensed images. In general, it is difficult to distinguish between types of land-cover classes that have similar or identical spectral signatures from remotely sensed data. Contextual data can be dasiafusedpsila with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster-Shafer theory of evidence to fuse the output of a semi-supervised classification (SSC) technique with contextual data in the form of a digital elevation model. The final classification accuracy is shown to improve when using this approach.",
author = "{Al Momani}, Bilal and Philip Morrow and Sally McClean",
year = "2007",
month = "2",
doi = "10.1109/ISSPA.2007.4555340",
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Al Momani, B, Morrow, P & McClean, S 2007, Knowledge-based semi-supervised satellite image classification. in Unknown Host Publication. pp. 1-4, 9th International Symposium on Signal Processing and Its Applications, 2007 (ISSPA 2007), 1/02/07. https://doi.org/10.1109/ISSPA.2007.4555340

Knowledge-based semi-supervised satellite image classification. / Al Momani, Bilal; Morrow, Philip; McClean, Sally.

Unknown Host Publication. 2007. p. 1-4.

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

TY - GEN

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AB - Spectral information on its own has proven to be insufficient for classification of remotely sensed images. In general, it is difficult to distinguish between types of land-cover classes that have similar or identical spectral signatures from remotely sensed data. Contextual data can be dasiafusedpsila with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster-Shafer theory of evidence to fuse the output of a semi-supervised classification (SSC) technique with contextual data in the form of a digital elevation model. The final classification accuracy is shown to improve when using this approach.

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