Improving classification accuracy for separation of area under crops based on feature selection from multi-temporal images and machine learning algorithms

Abstract

Timeliness, accurate, and frequent monitoring of area under crops are necessary for achieving food security. Therefore, the present study was conducted to improve the classification accuracy for the area under crops based on feature selection (FS) technique from multi-temporal Sentinel-1 and Sentinel-2 images in Shush County, Iran. To this end, the required pre-processing was performed on the images, and then features were extracted and categorized into four types of datasets with 78, 53, 65 features, for dataset 1 to 3 respectively. Dataset 4 is the result of integrating datasets 1, 2, and 3 with 194 features. The Sequential Forward Selection (SFS) method was used for the optimal FS, and Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for the image classification. The results of FS by SFS method and SVM algorithm for datasets 1, 2, 3, and 4 showed that 15 features were optimal. The SFS method and RF algorithm determined the optimum feature number as 18, 19, 11, and 14 for datasets 1, 2, 3, and 4, respectively. The results of the RF algorithm performed on dataset 4 showed the highest Overall Accuracy (OA) and Kappa coefficient of 91.23% and 0.9, respectively. The results from this algorithm indicated that the type of input features affects the classification accuracy, so dataset 4 (compared to dataset 2) improved the classification with a difference in OA and kappa coefficient of approximately 2.5% and 0.03, respectively. According to the findings, it can be concluded that selecting the optimal number of features using the SFS method in the separation of area under crops is of high importance and improves the computational efficiency of the models and classification accuracy. The SFS result indicated that the higher accuracy could be achieved when less than 20 features recruited in classification.