The Sensitivity Feature Analysis for Tree Species Based on Image Statistical Properties

While the statistical properties of images are vital in forestry engineering, the usefulness of these properties in various forestry tasks may vary, and certain image properties might not be enough to adequately describe a particular tree species. To address this problem, we propose a novel method to comprehensively analyze the relationship between various image statistical properties and images of different tree species, and to determine the subset of features that best describe each individual tree species. In this study, we employed various image statistical properties to quantify images of five distinct tree species from diverse places. Multiple feature-filtering methods were used to find the feature subset with the greatest correlation with the tree species category variable. Support Vector Machines (SVM) were employed to determine the number of features with the greatest correlation with the tree species, and a grid search was used to optimize the model. For each type of tree species image, we obtained the important ranking of all features in this type of tree species, and the sensitive feature subset of various tree species according to the order of features was determined by adding them to the Deep Support Vector Data Description (Deep SVDD). Finally, the feasibility of using a sensitive subset of the tree species was confirmed. The experimental results revealed that by utilizing the filtering method in conjunction with SVM, a total of eight feature subsets with the highest correlation with tree species categories were identified. Additionally, the sensitive feature subsets of different tree species exhibited significant differences. Remarkably, employing the sensitive feature subset of each tree species resulted in F1-score higher than 0.7 for all tree species. These experimental results demonstrate that the sensitive feature subset of tree species based on image statistical properties can serve as a potential representation of a specific tree species, while features that are less strongly associated with tree species may be significant in related areas, such as forestry protection and other related fields.