Extraction of Broad-Leaved Tree Crown Based on UAV Visible Images and OBIA-RF Model: A Case Study for Chinese Olive Trees

Abstract

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images.

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“…Moreover, in a related study by Yang et al [48], the effectiveness of Random Forest (RF) and Support Vector Machine (SVM) in land cover classification was emphasized. Yang et al [48] highlighted RF's robustness and SVM's ability to handle complex feature spaces, in line with our observations.…”

“…Moreover, in a related study by Yang et al [48], the effectiveness of Random Forest (RF) and Support Vector Machine (SVM) in land cover classification was emphasized. Yang et al [48] highlighted RF's robustness and SVM's ability to handle complex feature spaces, in line with our observations. They also pointed out that compared with Pixel-Based (PB) classification, the Object-Based image analysis (OBIA) method, as Yang et al [48] indicated, can extract features of each element of remote sensing images, providing certain advantages.…”

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“…Our research primarily centered on evaluating the impacts of various feature extraction methods and their combinations within the context of a single classifier. For example, Yang et al [50] conducted a comparison between the RF method and other widely-used ML classification algorithms, demonstrating that the RF method yielded superior classification results compared to the decision tree, support vector machine, and naive Bayesian methods. Reference [47] reported that the combination of FFT and the RF method exhibited exceptional pothole detection capabilities, achieving an accuracy rate as high as 96.5%, aligning with our study's findings.…”

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