Landscape tree species recognition using RedEdge-MX: Suitability analysis of two different texture extraction forms under MLC and RF supervision

Abstract

The size of the texture extraction window impacts image tree species classification, and the determination of the optimal texture extraction window requires the supervision of a specific classifier for accuracy. Therefore, it is necessary to analyse which kind of classifier is more suitable and should be to choose. In this study, we extracted eight types of textures, namely mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation, changed the window size by gradient increase and used maximum likelihood classification (MLC) and random forest (RF) to supervise and determine their optimal extraction windows, respectively. Finally, the optimised time consumption and classification accuracy for tree species classification was identified. The time consumption of MLC was significantly less than that of RF; however, neither was very long; for most textures, the optimal texture extraction window determined by MLC supervision was larger than that determined by RF supervision; in the classification of most feature sets, the overall accuracy obtained by MLC was less than that of RF. Because the time consumption of the texture extraction was much greater than that of the image classification, the comprehensive trade-off indicates that using RF supervision to determine the optimal window for texture extraction was more conducive to tree species recognition.

Improving Artificial-Intelligence-Based Individual Tree Species Classification Using Pseudo Tree Crown Derived from Unmanned Aerial Vehicle Imagery

“…Out of the many well-established ML algorithms, one is the deep learning (DL) approach, with convolutional neural networks (CNNs), graph neural networks (GNNs), and their variations being among the most popular [6,7]. The results are usually comparable to more traditional ML approaches, such as random forest (RF) and support vector machine (SVM) [8][9][10].…”

AI-Based Tree Species Classification Using Pseudo Tree Crown Derived From UAV Imagery

“…[4] Recently, numerous advancements have emerged in the classification of urban tree species, encompassing various data sources such as hyperspectral images, LiDAR data, and high-resolution images from UAV using two primary classification approaches: 1) Random Forest (RF) and Support Vector Machine (SVM); 2) Artificial Neural Network (ANN) and its derivatives, including Convolutional Neural Networks (CNN) and Hierarchical Convolutional Neural Network (H-CNN). [5][6][7][8][9] Liu et al [10] conducted supervised data classification by extracting eight textures, including mean and variance, utilizing Maximum Likelihood Classification (MLC) and RF. Their findings indicated that MLC exhibited significantly lower time consumption compared to RF.…”