Investigation of evolutionary fuzzy MPCM machine learning and probabilistic SVM models for Butea monosperma species mapping

Abstract

Accurate identification of plant species at an optimal level of precision remains a major challenge in ecological observations when using conventional classification methods. This study explores the potentiality of multi-temporal datasets with machine learning classifiers for the identification and distribution of Butea monosperma tree species, a native floral species grown in many countries of South and Southeast Asia. For identifying optimum combinations of temporal images the Euclidian distance-based separability analysis was employed on the multi-temporal GCI, MSAVI2 indices database (24 toatal temporal dates). This study uses the fuzzy Modified Possibilistic c-Mean (MPCM) classification method combined with the green chlorophyll (GCl), MSAVI2 temporal index to handle the complexity and uncertainty inherent in the phenological data. Owing to its lesser variance on the testing target species over the other classes, the 21 optimum temporal combinations of GCl images were chosen as a benchmark for comparison of the output with the Probabilistic Support Vector Machine (PSVM) with Radial Basis Function (RBF) kernel approach machine learning classifier which is well known for its ability to handle probabilistic information and high-dimensional data. In this study, a diverse dataset of tree species phenological observations has been employed to evaluate the performance of both classifiers. Key metrices such as overall accuracy and F1-score were utilized for the comparison of different models. The MPCM classifier achieved notable performance, with 92 % overall accuracy and an F1-score of 0.93 when utilizing the 21-temporal GCI database. In contrast, a single-date output resulted in only 65% overall accuracy and an F1-score of 0.74. When compared to PSVM model, which exhibits an F-score of 0.88 and an overall accuracy of 82 %, the utilization of MPCM with combined 21 temporal GCI indices demonstrated superior classification performance. Additionally, this research provides insights into how various evolutionary strategies and algorithms can enhance the classifiers’ adaptability to changing data distributions.