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Linear mixture modelling applied to ikonos data for mangrove mapping

Mixed pixels problem in remotely sensed satellite data often results in poor classification accuracy. However, high spatial resolution sensors such as IKONOS and QuickBird, are expected to classify mangrove species more accurately than conventional coarse spatial resolution satellite images. Neverth...

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Main Authors: Kanniah, Kasturi Devi, Ng, Su Wai, Lau, Alvin Meng Shin, Rasib, Abd. Wahid
Format: Conference or Workshop Item
Language:English
Published: 2005
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/4860/1/FRR2-2.pdf
http://eprints.utm.my/id/eprint/4860/
http://www.proceedings.com/00856.html
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spelling my.utm.48602017-08-28T01:44:09Z http://eprints.utm.my/id/eprint/4860/ Linear mixture modelling applied to ikonos data for mangrove mapping Kanniah, Kasturi Devi Ng, Su Wai Lau, Alvin Meng Shin Rasib, Abd. Wahid TA Engineering (General). Civil engineering (General) Mixed pixels problem in remotely sensed satellite data often results in poor classification accuracy. However, high spatial resolution sensors such as IKONOS and QuickBird, are expected to classify mangrove species more accurately than conventional coarse spatial resolution satellite images. Nevertheless, conventional classification technique such as maximum likelihood could not improve the classification accuracy when such high-resolution images are applied. In order to improve the classification accuracy Linear Mixture Model (LMM) was applied in this study to classify the mangrove forest at Sungai Belungkor, Johor, Malaysia. High spatial resolution satellite imagery (IKONOS) was used to map three different mangrove species in the area. The application of LMM to mangrove classification involved image preprocessing, endmember selection, inversion of LMM and finally the accuracy assessment. Accuracy assessment was carried out between the fraction of pixels estimated from LMM and the fraction measured in the field. The results of accuracy assessment gave a correlation coefficient value of ~0.8 for endmember bakau minyak and ~0.6 for bakau kurap and “others” type of mangrove species. An error image was also created to compare the best fitting spectrum produced by the inversion of LMM to the original observed spectrum where the maximum RMS error was only 5%. The accuracy of LMM was also assessed based on a generalized area based confusion matrix. Areas that were correctly classified according to reference data and classified data were 63 % and 73%, for bakau minyak, 62 % and 60% for bakau kurap and 58% and 52% for “others” type of mangrove respectively. These accuracies obtained from LMM were higher in comparison to the classification results derived from maximum likelihood with the inclusion of texture information or minimum distance to mean classifier 2005-11 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/4860/1/FRR2-2.pdf Kanniah, Kasturi Devi and Ng, Su Wai and Lau, Alvin Meng Shin and Rasib, Abd. Wahid (2005) Linear mixture modelling applied to ikonos data for mangrove mapping. In: Proceedings of the 26th Asian Conference on Remote Sensing, ACRS 2005, 7-11 November 2005, Hanoi, Vietnam. http://www.proceedings.com/00856.html
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/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Kanniah, Kasturi Devi
Ng, Su Wai
Lau, Alvin Meng Shin
Rasib, Abd. Wahid
Linear mixture modelling applied to ikonos data for mangrove mapping
description Mixed pixels problem in remotely sensed satellite data often results in poor classification accuracy. However, high spatial resolution sensors such as IKONOS and QuickBird, are expected to classify mangrove species more accurately than conventional coarse spatial resolution satellite images. Nevertheless, conventional classification technique such as maximum likelihood could not improve the classification accuracy when such high-resolution images are applied. In order to improve the classification accuracy Linear Mixture Model (LMM) was applied in this study to classify the mangrove forest at Sungai Belungkor, Johor, Malaysia. High spatial resolution satellite imagery (IKONOS) was used to map three different mangrove species in the area. The application of LMM to mangrove classification involved image preprocessing, endmember selection, inversion of LMM and finally the accuracy assessment. Accuracy assessment was carried out between the fraction of pixels estimated from LMM and the fraction measured in the field. The results of accuracy assessment gave a correlation coefficient value of ~0.8 for endmember bakau minyak and ~0.6 for bakau kurap and “others” type of mangrove species. An error image was also created to compare the best fitting spectrum produced by the inversion of LMM to the original observed spectrum where the maximum RMS error was only 5%. The accuracy of LMM was also assessed based on a generalized area based confusion matrix. Areas that were correctly classified according to reference data and classified data were 63 % and 73%, for bakau minyak, 62 % and 60% for bakau kurap and 58% and 52% for “others” type of mangrove respectively. These accuracies obtained from LMM were higher in comparison to the classification results derived from maximum likelihood with the inclusion of texture information or minimum distance to mean classifier
format Conference or Workshop Item
author Kanniah, Kasturi Devi
Ng, Su Wai
Lau, Alvin Meng Shin
Rasib, Abd. Wahid
author_facet Kanniah, Kasturi Devi
Ng, Su Wai
Lau, Alvin Meng Shin
Rasib, Abd. Wahid
author_sort Kanniah, Kasturi Devi
title Linear mixture modelling applied to ikonos data for mangrove mapping
title_short Linear mixture modelling applied to ikonos data for mangrove mapping
title_full Linear mixture modelling applied to ikonos data for mangrove mapping
title_fullStr Linear mixture modelling applied to ikonos data for mangrove mapping
title_full_unstemmed Linear mixture modelling applied to ikonos data for mangrove mapping
title_sort linear mixture modelling applied to ikonos data for mangrove mapping
publishDate 2005
url http://eprints.utm.my/id/eprint/4860/1/FRR2-2.pdf
http://eprints.utm.my/id/eprint/4860/
http://www.proceedings.com/00856.html
_version_ 1643644167826440192
score 13.211869

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