Knowledge-based semi-supervised satellite image classification

Bilal Al Momani; Philip Morrow; Sally McClean

doi:10.1109/ISSPA.2007.4555340

Knowledge-based semi-supervised satellite image classification

Bilal Al Momani, Philip Morrow, Sally McClean

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 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.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE Computer Society
Pages	1-4
Number of pages	4
DOIs	https://doi.org/10.1109/ISSPA.2007.4555340
Publication status	Published (in print/issue) - Feb 2007
Event	9th International Symposium on Signal Processing and Its Applications, 2007 (ISSPA 2007) - Duration: 1 Feb 2007 → …

Conference

Conference	9th International Symposium on Signal Processing and Its Applications, 2007 (ISSPA 2007)
Period	1/02/07 → …

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10.1109/ISSPA.2007.4555340

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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.",

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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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DO - 10.1109/ISSPA.2007.4555340

M3 - Conference contribution

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T2 - 9th International Symposium on Signal Processing and Its Applications, 2007 (ISSPA 2007)

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ER -

Knowledge-based semi-supervised satellite image classification

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