Combinations of Feature Selection and Machine Learning Algorithms for Object-Oriented Betel Palms and Mango Plantations Classification Based on Gaofen-2 Imagery

Abstract

Betel palms and mango plantations are two crucial commercial crops in tropical agricultural areas. Accurate spatial distributions of these two crops are essential in tropical agricultural regional planning and management. However, the characteristics of small patches, scattering, and perennation make it challenging to map betel palms and mango plantations in complex tropical agricultural regions. Furthermore, the excessive features of very-high-resolution (VHR) imaging might lead to a reduction in classification accuracy and an increase in computation times. To address these challenges, we selected five feature selection (FS) methods (random forest means a decrease in accuracy (RFMDA), ReliefF, random forest-recursive feature elimination (RFE), aggregated boosted tree (ABT), and logistic regression (LR)) and four machine learning algorithms (random forest (RF), support vector machine (SVM), classification and regression tree (CART), and adaptive boosting (AdaBoost)). Then, the optimal combinations of FS and machine learning algorithms suited for object-oriented classification of betel palms and mango plantations were explored using VHR Gaofen-2 imagery. In terms of overall accuracy, all optimal classification schemes exceeded 80%, and the classifiers using selected features increased the overall accuracy between 1% and 4% compared with classification without FS methods. Specifically, LR was appropriate to RF and SVM classifiers, which produced the highest classification accuracy (89.1% and 89.88% for RF and SVM, respectively). In contrast, ABT and ReliefF were found to be suitable FS methods for CART and AdaBoost classifiers, respectively. Overall, all four optimal combinations of FS methods and classifiers could precisely recognize mango plantations, whereas betel palms were best depicted by using the RF-LR method with 26 features. The results indicated that combination of feature selection and machine learning algorithms contributed to the object-oriented classification of complex tropical crops using Gaofen-2 imagery, which provide a useful methodological reference for precisely recognizing small tropical agricultural patterns.