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Processing and classification of landsat and sentinel images for oil palm plantation detection

The increasing demand for remote sensing, along with the advancement of technology, has led to the development of robust, sensible, and user-friendly products that can utilise remotely captured images. Remote sensing in agriculture has gained a lot of interest recently, especially in plantation m...

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Main Authors:	Mohd Ibrahim, Azhar, Asming, Muhammad Anwar Azizan, Abir, Intiaz Mohammad
Format:	Article
Language:	English English
Published:	Elsevier 2022
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/97696/7/97696_Processing%20and%20classification%20of%20landsat_SCOPUS.pdf http://irep.iium.edu.my/97696/13/97696_Processing%20and%20classification%20of%20landsat.pdf http://irep.iium.edu.my/97696/ https://www.sciencedirect.com/science/article/abs/pii/S2352938522000556
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spelling	my.iium.irep.976962022-04-22T07:52:37Z http://irep.iium.edu.my/97696/ Processing and classification of landsat and sentinel images for oil palm plantation detection Mohd Ibrahim, Azhar Asming, Muhammad Anwar Azizan Abir, Intiaz Mohammad T Technology (General) The increasing demand for remote sensing, along with the advancement of technology, has led to the development of robust, sensible, and user-friendly products that can utilise remotely captured images. Remote sensing in agriculture has gained a lot of interest recently, especially in plantation management. This technology is useful for controlling and monitoring various aspects of the plantations. One of the capabilities of remote sensing is the detection of oil palm plantations. Therefore, this paper attempts to determine the best methods for image classification, especially for land cover classification of oil palm plantations. It first focuses on the correction algorithm needed to estimate the true surface reflectance value of the satellite image data before the image is filtered to reduce any noise. The process includes the analysis of both supervised and unsupervised modules in terms of their contrast visual and reflectance spectral curve to find the best method of extracting the images’ features. In distinguishing oil palm trees, optimisation of the pre-processing of the images enables the extraction of useful information based on its spectral signature, before they are utilised as an input for the soft computing method. The results show that Artificial Neural Network (ANN) performed the best image classification with the highest overall accuracy and kappa coefficient compared to other supervised classifications. The parameters for ANN were later adjusted to identify the best ANN classification, resulting in an overall accuracy of 98.2857% and 0.9792 of kappa coefficient, and manages to effectively detect oil palm trees from the background. Elsevier 2022-04-08 Article PeerReviewed application/pdf en http://irep.iium.edu.my/97696/7/97696_Processing%20and%20classification%20of%20landsat_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/97696/13/97696_Processing%20and%20classification%20of%20landsat.pdf Mohd Ibrahim, Azhar and Asming, Muhammad Anwar Azizan and Abir, Intiaz Mohammad (2022) Processing and classification of landsat and sentinel images for oil palm plantation detection. Remote Sensing Applications: Society and Environment, 26. https://www.sciencedirect.com/science/article/abs/pii/S2352938522000556 10.1016/j.rsase.2022.100747
institution	Universiti Islam Antarabangsa Malaysia
building	IIUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	International Islamic University Malaysia
content_source	IIUM Repository (IREP)
url_provider	http://irep.iium.edu.my/
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Mohd Ibrahim, Azhar Asming, Muhammad Anwar Azizan Abir, Intiaz Mohammad Processing and classification of landsat and sentinel images for oil palm plantation detection
description	The increasing demand for remote sensing, along with the advancement of technology, has led to the development of robust, sensible, and user-friendly products that can utilise remotely captured images. Remote sensing in agriculture has gained a lot of interest recently, especially in plantation management. This technology is useful for controlling and monitoring various aspects of the plantations. One of the capabilities of remote sensing is the detection of oil palm plantations. Therefore, this paper attempts to determine the best methods for image classification, especially for land cover classification of oil palm plantations. It first focuses on the correction algorithm needed to estimate the true surface reflectance value of the satellite image data before the image is filtered to reduce any noise. The process includes the analysis of both supervised and unsupervised modules in terms of their contrast visual and reflectance spectral curve to find the best method of extracting the images’ features. In distinguishing oil palm trees, optimisation of the pre-processing of the images enables the extraction of useful information based on its spectral signature, before they are utilised as an input for the soft computing method. The results show that Artificial Neural Network (ANN) performed the best image classification with the highest overall accuracy and kappa coefficient compared to other supervised classifications. The parameters for ANN were later adjusted to identify the best ANN classification, resulting in an overall accuracy of 98.2857% and 0.9792 of kappa coefficient, and manages to effectively detect oil palm trees from the background.
format	Article
author	Mohd Ibrahim, Azhar Asming, Muhammad Anwar Azizan Abir, Intiaz Mohammad
author_facet	Mohd Ibrahim, Azhar Asming, Muhammad Anwar Azizan Abir, Intiaz Mohammad
author_sort	Mohd Ibrahim, Azhar
title	Processing and classification of landsat and sentinel images for oil palm plantation detection
title_short	Processing and classification of landsat and sentinel images for oil palm plantation detection
title_full	Processing and classification of landsat and sentinel images for oil palm plantation detection
title_fullStr	Processing and classification of landsat and sentinel images for oil palm plantation detection
title_full_unstemmed	Processing and classification of landsat and sentinel images for oil palm plantation detection
title_sort	processing and classification of landsat and sentinel images for oil palm plantation detection
publisher	Elsevier
publishDate	2022
url	http://irep.iium.edu.my/97696/7/97696_Processing%20and%20classification%20of%20landsat_SCOPUS.pdf http://irep.iium.edu.my/97696/13/97696_Processing%20and%20classification%20of%20landsat.pdf http://irep.iium.edu.my/97696/ https://www.sciencedirect.com/science/article/abs/pii/S2352938522000556
_version_	1731225385122660352
score	13.211869

Processing and classification of landsat and sentinel images for oil palm plantation detection

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