Sentinel SAR-optical fusion for improving in-season wheat crop mapping at a large scale using machine learning and the Google Earth engine platform

Zoungrana, Louis Evence; Barbouchi, Meriem; Toukabri, Wael; Babasy, Mohamedou Ould; Khatra, Nabil Ben; Annabi, Mohamed; Bahri, Haithem

doi:10.1007/s12518-023-00545-4

Cited by 2 publications

(1 citation statement)

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“…2). RF has shown its outstanding performance in a variety of regions and application elds (Zoungrana et al, 2024). To classify an input vector, each tree classi er provides one unit vote for the class that is most common in the tree (Adam et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of SVM and RF Machine Learning Algorithms in Land Use/Land Cover Change Assessment: Tessa Watershed Case Study (Northwest of Tunisia)

BACCARI,

HAMZA,

SLAMA

et al. 2024

Preprint

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This study aims to highlight the changes in LULC (land use and Land cover) in the Tessa watershed (Northwest of Tunisia) between 1993 and 2023. Remote sensing coupled with geographic information systems (GIS) and statistical analysis, are used. Accuracy metrics make it possible to evaluate the performance of the image classification method, using the calculation of the producer’s accuracy, the user’s accuracy, overall accuracy, and the Kappa coefficient. Two Machine Learning (ML) algorithms related to the supervised classification are used for two Landsat images related to 1993 and 2023: the Support Vector Machine (SVM) and the Random Forest (RF). These algorithms are integrated into the SCP plugin of the QGIS software used in this study. The overall accuracy achieved by applying the SVM algorithm to the Landsat 5 TM image from 1993 is 88.24% with a Kappa value of 0.8, whereas the overall accuracy obtained for the Landsat 8 OLI image from 2023 is 99.4% with a Kappa value of 0.99. By applying the RF algorithm, the overall accuracy obtained for the 1993 Landsat 5 TM image is 86% with a Kappa value of 0.8, while for the 2023 Landsat 8 OLI image, the overall accuracy obtained is 81% with a Kappa value of 0.77. Using the transition matrix, it was possible to detect LULC changes spatiotemporally. A comparison of the classification results obtained from SVM and RF algorithms with ground truth showed that the SVM classifier was more accurate in the study area.

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Section: Introductionmentioning

confidence: 99%