Our results show that RF achieved higher overall accuracy (88.61%) than SVM having an overall accuracy of 81.86%. This study demonstrated that utilizing the Sentinel-2 NDVI time-series with RF and SVM successfully classified sugarcane crop fields.
Nov 10, 2020
Our results show that RF achieved higher overall accuracy (88.61%) than SVM having an overall accuracy of 81.86%. This study demonstrated that utilizing the ...
This study demonstrated that utilizing the Sentinel-2 NDVI time-series with RF and SVM successfully classified sugarcane crop fields. Sentinel-2 optical ...
Performance Evaluation of RF and SVM for Sugarcane Classification Using Sentinel-2 NDVI Time-Series. https://doi.org/10.1007/978-981-15-6353-9_15.
Our results show that RF achieved higher overall accuracy (88.61%) than SVM having an overall accuracy of 81.86%. This study demonstrated that utilizing the ...
Our results show that the RF achieved higher overall accuracy (88.61 %) than the SVM having an overall accuracy of 81.86%.s This study demonstrated that ...
Our results show that RF achieved higher overall accuracy (88.61%) than SVM having an overall accuracy of 81.86%. This study demonstrated that utilizing the ...
Get details about the chapter of Performance Evaluation of RF and SVM for Sugarcane Classification Using Sentinel-2 NDVI Time-Series from book Progress in ...
This paper demonstrates and compares the utilization and the combinatorial use of three different sets of object-based predictors for sugarcane yield ...
Missing: SVM | Show results with:SVM
TL;DR: This study demonstrated that utilizing the Sentinel-2 NDVI time-series with RF and SVM successfully classified sugarcane crop fields. ...read more read ...
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