Cover Image

REMOTE SENSING TECHNIQUES FOR OIL PALM AGE CLASSIFICATION USING LANDSAT-5 TM SATELLITE

This paper demonstrates the procedure to classify the age of oil palm trees using Landsat-5 TM (thematic mapper) remote sensing data. The study was conducted in two phases: phase I focuses on the the land cover classification, and phase II involves the oil palm age classification. Firstly,the region...

Full description

Saved in:
Bibliographic Details
Main Authors: Vadivelu, Shamala, Asmala, A., Yun-Huoy, C.
Format: Article
Language:English
Published: Publications International 2014
Subjects:
Q Science (General)
S Agriculture (General)
Online Access:http://eprints.utem.edu.my/id/eprint/13736/1/695845022PID_61--Shamala--1547-1551Doc1.pdf
http://eprints.utem.edu.my/id/eprint/13736/
http://www.sci-int.com/pdf/695845022PID%2061--Shamala--1547-1551Doc1.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utem.eprints.13736
record_format eprints
spelling my.utem.eprints.137362015-05-28T04:33:50Z http://eprints.utem.edu.my/id/eprint/13736/ REMOTE SENSING TECHNIQUES FOR OIL PALM AGE CLASSIFICATION USING LANDSAT-5 TM SATELLITE Vadivelu, Shamala Asmala, A. Yun-Huoy, C. Q Science (General) S Agriculture (General) This paper demonstrates the procedure to classify the age of oil palm trees using Landsat-5 TM (thematic mapper) remote sensing data. The study was conducted in two phases: phase I focuses on the the land cover classification, and phase II involves the oil palm age classification. Firstly,the region of interest (ROI) was identified and drawn in order to supply the training and testing pixels for the supervised classification. Maximum likelihood (ML) classifier was used for land cover classification. The land cover classification using the ML produces a good result with an overall accuracy of 85.51% and kappa coefficient of 0.8208. Meanwhile, three classifiers were used to investigate the age of oil palm classification, which are the 1) Maximum likelihood (ML), 2) Neural Network (NN) and, 3) Support Vector Machine (SVM). The accuracy of the classifications was then assessed by comparing the classifications with a reference set using a confusion matrix technique. Among the three classifiers, SVM performs the best with the highest overall accuracy of 54.18% and kappa coefficient of 0.39. Publications International 2014 Article PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/13736/1/695845022PID_61--Shamala--1547-1551Doc1.pdf Vadivelu, Shamala and Asmala, A. and Yun-Huoy, C. (2014) REMOTE SENSING TECHNIQUES FOR OIL PALM AGE CLASSIFICATION USING LANDSAT-5 TM SATELLITE. Science International, 26 (4). pp. 1547-1551. ISSN 1013-5316 http://www.sci-int.com/pdf/695845022PID%2061--Shamala--1547-1551Doc1.pdf
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic Q Science (General)
S Agriculture (General)
spellingShingle Q Science (General)
S Agriculture (General)
Vadivelu, Shamala
Asmala, A.
Yun-Huoy, C.
REMOTE SENSING TECHNIQUES FOR OIL PALM AGE CLASSIFICATION USING LANDSAT-5 TM SATELLITE
description This paper demonstrates the procedure to classify the age of oil palm trees using Landsat-5 TM (thematic mapper) remote sensing data. The study was conducted in two phases: phase I focuses on the the land cover classification, and phase II involves the oil palm age classification. Firstly,the region of interest (ROI) was identified and drawn in order to supply the training and testing pixels for the supervised classification. Maximum likelihood (ML) classifier was used for land cover classification. The land cover classification using the ML produces a good result with an overall accuracy of 85.51% and kappa coefficient of 0.8208. Meanwhile, three classifiers were used to investigate the age of oil palm classification, which are the 1) Maximum likelihood (ML), 2) Neural Network (NN) and, 3) Support Vector Machine (SVM). The accuracy of the classifications was then assessed by comparing the classifications with a reference set using a confusion matrix technique. Among the three classifiers, SVM performs the best with the highest overall accuracy of 54.18% and kappa coefficient of 0.39.
format Article
author Vadivelu, Shamala
Asmala, A.
Yun-Huoy, C.
author_facet Vadivelu, Shamala
Asmala, A.
Yun-Huoy, C.
author_sort Vadivelu, Shamala
title REMOTE SENSING TECHNIQUES FOR OIL PALM AGE CLASSIFICATION USING LANDSAT-5 TM SATELLITE
title_short REMOTE SENSING TECHNIQUES FOR OIL PALM AGE CLASSIFICATION USING LANDSAT-5 TM SATELLITE
title_full REMOTE SENSING TECHNIQUES FOR OIL PALM AGE CLASSIFICATION USING LANDSAT-5 TM SATELLITE
title_fullStr REMOTE SENSING TECHNIQUES FOR OIL PALM AGE CLASSIFICATION USING LANDSAT-5 TM SATELLITE
title_full_unstemmed REMOTE SENSING TECHNIQUES FOR OIL PALM AGE CLASSIFICATION USING LANDSAT-5 TM SATELLITE
title_sort remote sensing techniques for oil palm age classification using landsat-5 tm satellite
publisher Publications International
publishDate 2014
url http://eprints.utem.edu.my/id/eprint/13736/1/695845022PID_61--Shamala--1547-1551Doc1.pdf
http://eprints.utem.edu.my/id/eprint/13736/
http://www.sci-int.com/pdf/695845022PID%2061--Shamala--1547-1551Doc1.pdf
_version_ 1665905560161091584
score 13.211869

Similar Items