A Multitree Genetic Programming-Based Feature Construction Approach to Crop Classification Using Hyperspectral Images

Feature construction has shown promise in improving the accuracy of crop classification by constructing high-level features. However, current feature construction methods often rely on domain knowledge and have a limited interpretability of the solutions. To address this, this study proposes a new genetic programming (GP) approach to automatically evolve solutions with high interpretability that can construct high-level features for crop classification from hyperspectral images. A flexible representation of multiple trees is proposed in the proposed GP approach to construct various types of high-level features from the original ones, simultaneously. To improve the search ability, a new offspring generation method is developed to dynamically guide the evolution of the population while improving the diversity of the population. The new approach wraps with three classification algorithms, i.e., support vector machine (SVM), naive Bayes (NB), and k-nearest neighbor (KNN), for crop classification on three datasets with different difficulties and tasks. The results demonstrate that the features constructed by the new approach can effectively distinguish different crop categories. The new approach achieves better performance than the compared GP-based method, classic methods, and deep learning methods in crop classification using hyperspectral images. Importantly, the proposed approach shows the high interpretability of the constructed features.