Implementation of U-Net for Paddy Field Mapping Using Very High-Resolution Satellite Imagery

Mapping rice fields using remote sensing is one method that can be used to determine the number of rice fields, especially in Indonesia. Using this method can increase effectiveness in agricultural resource management. This research uses Pleiades optical satellite image data with very high resolution which is capable of displaying data information on a larger scale. The rice field classification model in this study uses U-net to classifier between rice fields and non-rice fields. The performance of applying this model for the classification of paddy fields and non-rice fields is 96%. These results show that the U-net model is capable of classifying small rice fields with high accuracy

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Publication Details

Journal
International Journal of Electrical Computer and Biomedical Engineering
Published
2024-09-30
DOI
https://doi.org/10.62146/ijecbe.v2i3.57
Citations
1
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
0.38

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article

Implementation of U-Net for Paddy Field Mapping Using Very High-Resolution Satellite Imagery

Dodi Sudiana, Mia Rizkinia, Faiz Khairul Isbat
1 citations
International Journal of Electrical Computer and Biomedical Engineering
Remote Sensing and Land Use
0.38
article

Implementation of U-Net for Paddy Field Mapping Using Very High-Resolution Satellite Imagery

Dodi Sudiana, Mia Rizkinia, Faiz Khairul Isbat
article en
1 citations

Abstract

Mapping rice fields using remote sensing is one method that can be used to determine the number of rice fields, especially in Indonesia. Using this method can increase effectiveness in agricultural resource management. This research uses Pleiades optical satellite image data with very high resolution which is capable of displaying data information on a larger scale. The rice field classification model in this study uses U-net to classifier between rice fields and non-rice fields. The performance of applying this model for the classification of paddy fields and non-rice fields is 96%. These results show that the U-net model is capable of classifying small rice fields with high accuracy

International Journal of Electrical Computer and Biomedical EngineeringVol. 2(3)
University of Indonesia (ID)
Badan Riset dan Inovasi Nasional
Openalex Percentile: Top 40%
Remote Sensing and Land Use
0.38
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