Classification Performance of Land Use from Multispectral Remote Sensing Images using Decision Tree, K-Nearest Neighbor, Random Forest and Support Vector Machine Using EuroSAT Data
Keywords:
machine learning algorithm, land use/land cover (LULC), decision tree, k-nearest neighbor, support vector machine, random forestAbstract
Remote sensing is commonly used in remote sensing applications for land cover and land use classification using remotely sensed data. Different algorithms for LULC mapping need to be compared to determine which one is most accurate for further use of Earth observations. In this study, performance of four machine learning algorithms, specifically decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) was examined with the help of satellite images from the EuroSAT dataset. Accuracy assessment was performed using the training, testing, and validation methods. With the help of the confusion matrix, the classification output, the prediction test, and validation accuracy assessment were assessed to obtain the classifier with more accuracy. Validating the classification findings against actual data would reveal the optimal performance. According to the EuroSAT dataset, the overall classification accuracy was 98.57 percent, which is higher than the K-nearest neighbor classifier and more suitable for satellite image classification. Appropriate LULC maps can be produced by accurately classifying the data. This map can be used in variety of applications. Based on Sentinel-2 satellite photos, we provide a new dataset with 27,000 classified images from 13 spectral bands and 10 classifications. The suggested research's categorization approach opens the door to an extensive range of Earth observation applications. Maps may be improved by using a categorized system, which we illustrate here.
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