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DOI: 10.14569/IJACSA.2022.0131070
PDF

Evaluation of Land Use/Land Cover Classification based on Different Bands of Sentinel-2 Satellite Imagery using Neural Networks

Author 1: Pallavi M
Author 2: Thivakaran T K
Author 3: Chandankeri Ganapathi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

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Abstract: Spatial data analytics is an emerging technology. Artificial neural network techniques play a major role in analysing any critical dataset. Integrating remote sensing data with deep neural networks has led a way to several research problems. This paper aims at producing land use land cover map of Bangalore region, Karnataka, India with various band combinations of sentinel satellite imagery obtained from google earth engine. LULC map classes include water, urban, forest, vegetation and openland. Band combinations of satellite images represent different characteristics of spatial data. Hence, several band combinations are used to build LULC maps. Also, classified maps are generated using different neural networks with pixel-based classification approach. Appropriate performance metrics were identified to evaluate the classification results such as Accuracy, Precision, Recall, F1-score and Confusion Matrix. Among neural networks, Convolutional Neural Network technique outperformed with 98.1 % of accuracy and less error rates in confusion matrix considering RGBNIR (4328) band combination of satellite imagery.

Keywords: Sentinel-2; neural networks; convolutional neural networks; remote sensing data; land use land cover maps

Pallavi M, Thivakaran T K and Chandankeri Ganapathi, “Evaluation of Land Use/Land Cover Classification based on Different Bands of Sentinel-2 Satellite Imagery using Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131070

@article{M2022,
title = {Evaluation of Land Use/Land Cover Classification based on Different Bands of Sentinel-2 Satellite Imagery using Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131070},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131070},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Pallavi M and Thivakaran T K and Chandankeri Ganapathi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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