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The approaches for oasis desert vegetation information abstraction based on medium - Resolution Lansat TM image: A case study in desert wadi Hadramut Yemen

This paper present two issues namely; firest is oasis desert brightness inversion correction, and secondly, the classifying method of oasis desert vegetation through remote sensing image data., Oasis desert brightness inversion is known reduce the classification accuracy in medium-resolution images....

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Main Authors: Almhab, Ayoub, Busu, Ibrahim
Format: Book Section
Published: Institute of Electrical and Electronics Engineers 2008
Subjects:
GE Environmental Sciences
Online Access:http://eprints.utm.my/id/eprint/12776/
http://dx.doi.org/10.1109/AMS.2008.143
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spelling my.utm.127762011-06-29T07:57:36Z http://eprints.utm.my/id/eprint/12776/ The approaches for oasis desert vegetation information abstraction based on medium - Resolution Lansat TM image: A case study in desert wadi Hadramut Yemen Almhab, Ayoub Busu, Ibrahim GE Environmental Sciences This paper present two issues namely; firest is oasis desert brightness inversion correction, and secondly, the classifying method of oasis desert vegetation through remote sensing image data., Oasis desert brightness inversion is known reduce the classification accuracy in medium-resolution images. In this study, the radiation correction and the brightness inversion adjustment models was analysis. The model's parameters were obtained from the image pixel values. The result of brightness inversion correction shows that the model can correct oasis desert brightness inversion. After brightness inversion correction, the vegetation's pixel value in brightness inversion area is similar with the pixel value of vegetation in other area. Brightness inversion correction increases classification accuracy. In the second part of this study, three methods are studied to derive oasis desert vegetations information, including vegetation index method, back propagation neural network method, and texture method. Three methods' classification accuracies are calculated and appraised. And a conclusion is drawn, which is the texture classification method is a good classification method. The accuracy of texture classification method can reach up to 82.31%. Institute of Electrical and Electronics Engineers 2008 Book Section PeerReviewed Almhab, Ayoub and Busu, Ibrahim (2008) The approaches for oasis desert vegetation information abstraction based on medium - Resolution Lansat TM image: A case study in desert wadi Hadramut Yemen. In: Proceedings - 2nd Asia International Conference on Modelling and Simulation, AMS 2008. Institute of Electrical and Electronics Engineers, New York, 356 -360. ISBN 978-076953136-6 http://dx.doi.org/10.1109/AMS.2008.143 doi:10.1109/AMS.2008.143
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic GE Environmental Sciences
spellingShingle GE Environmental Sciences
Almhab, Ayoub
Busu, Ibrahim
The approaches for oasis desert vegetation information abstraction based on medium - Resolution Lansat TM image: A case study in desert wadi Hadramut Yemen
description This paper present two issues namely; firest is oasis desert brightness inversion correction, and secondly, the classifying method of oasis desert vegetation through remote sensing image data., Oasis desert brightness inversion is known reduce the classification accuracy in medium-resolution images. In this study, the radiation correction and the brightness inversion adjustment models was analysis. The model's parameters were obtained from the image pixel values. The result of brightness inversion correction shows that the model can correct oasis desert brightness inversion. After brightness inversion correction, the vegetation's pixel value in brightness inversion area is similar with the pixel value of vegetation in other area. Brightness inversion correction increases classification accuracy. In the second part of this study, three methods are studied to derive oasis desert vegetations information, including vegetation index method, back propagation neural network method, and texture method. Three methods' classification accuracies are calculated and appraised. And a conclusion is drawn, which is the texture classification method is a good classification method. The accuracy of texture classification method can reach up to 82.31%.
format Book Section
author Almhab, Ayoub
Busu, Ibrahim
author_facet Almhab, Ayoub
Busu, Ibrahim
author_sort Almhab, Ayoub
title The approaches for oasis desert vegetation information abstraction based on medium - Resolution Lansat TM image: A case study in desert wadi Hadramut Yemen
title_short The approaches for oasis desert vegetation information abstraction based on medium - Resolution Lansat TM image: A case study in desert wadi Hadramut Yemen
title_full The approaches for oasis desert vegetation information abstraction based on medium - Resolution Lansat TM image: A case study in desert wadi Hadramut Yemen
title_fullStr The approaches for oasis desert vegetation information abstraction based on medium - Resolution Lansat TM image: A case study in desert wadi Hadramut Yemen
title_full_unstemmed The approaches for oasis desert vegetation information abstraction based on medium - Resolution Lansat TM image: A case study in desert wadi Hadramut Yemen
title_sort approaches for oasis desert vegetation information abstraction based on medium - resolution lansat tm image: a case study in desert wadi hadramut yemen
publisher Institute of Electrical and Electronics Engineers
publishDate 2008
url http://eprints.utm.my/id/eprint/12776/
http://dx.doi.org/10.1109/AMS.2008.143
_version_ 1643646038907551744
score 13.211869

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