Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)

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Evaluation of the Multispectral Satellites with Object-Based Classifiers for Land Use and Land Cover Classification

Authors
Eman A. Alshari1, 2, *, Bharti W. Gawali2
1Computer Science and Information Technology Department, Thamar University, Dhamar, Yemen
2Dr. Babasaheb Ambedkar, Marathwada University, Aurangabad, 431004, India
*Corresponding author. Email: em.alshari3@gmail.com
Corresponding Author
Eman A. Alshari
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-196-8_46How to use a DOI?
Keywords
Multispectral Satellites; Land Use & Land Cover Classification (LULC C); Sentinel-2A satellite (10m); Landsat8 Satellite (30m); Random Forest (RF) Classifier; K-Nearest Neighbour (KNN); Sana'a City
Abstract

This research aimed to evaluate traditional machine learning to achieve high-resolution LULC classification with multispectral satellites and object-based classifiers. Multispectral satellites have high importance in getting and downloading images of observation land. This article describes the comparative analysis of Sentinel-2A (10 m resolution) and Landsat8 (30 m resolution)Satellites with two classifiers from object-based machine learning methods, Random Forest (RF) and K Nearest Neighbor (KNN), to experiment with the classification of five years (2015, 2016, 2017, 2018, and 2019) with 95 images downloaded, 60 images with sentinal2A, and 35 images with landsat8. Area of Sana'a region. This Study indicated that Random Forest proved efficient for Sentinel 2A and Landsat8. Whereas KNN worked well with Landsat8 and provided higher accuracy than RF. The interpretation of these results may be due to the RF classifier requiring many features for good accuracy. At the same time, KNN works well with a small number of input feature variables and gives good accuracy.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
Series
Advances in Intelligent Systems Research
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-196-8_46
ISSN
1951-6851
DOI
10.2991/978-94-6463-196-8_46How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Eman A. Alshari
AU  - Bharti W. Gawali
PY  - 2023
DA  - 2023/08/10
TI  - Evaluation of the Multispectral Satellites with Object-Based Classifiers for Land Use and Land Cover Classification
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 602
EP  - 625
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_46
DO  - 10.2991/978-94-6463-196-8_46
ID  - Alshari2023
ER  -