Comparative Analysis of Different Classifiers on Fusion of Sentinel Datasets for Agriculture Land

Image fusion could adopt the optical and microwave images model to obtain more precise agricultural land cover information features by using the optical and microwave remote sensing sensors characteristics. The source of data has decided to take into account Sentinel-2 L2A and Sentinel-1 VH (Vertical-transmit and Horizontal-receive) datasets for testing image fusion classifiers used in the experiments. Different kinds of classifiers were used in this research. Among them, MDC stands for Minimum Distance Classifier, and Maximum Likelihood Classifier represents MLC. Lastly, a Support Vector Machine (SVM) was used during the fusion of the images to classify Land Use and determine the validity of these algorithms. A summary of the outputs will give an upper hand to the classifiers based on Support Vector Machines, with the accuracy reaching 95.67% and a kappa of 0.9191. The SVM classification algorithm during the study showed the dense vegetation, deciduous vegetation, water, and urban zones with shallow errors. MDC performed the task well in detecting the land use category with an accuracy of 91.70<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and kappa of 0.8469. The opposite of the MDC classifier was that the accuracy was not the top level, as, in some ways, some water bodies were wrongly assigned as urban regions. MLC has the least promising result among other approaches and is in the last place in terms of accuracy, with 89.52% and a kappa value of 0.8281.