Evaluating the Influence of Pseudo Tree Crown (PTC) Input Alternatives for Machine Learning and Deep Learning Models on Individual Tree Classification Performance.
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| Title: | Evaluating the Influence of Pseudo Tree Crown (PTC) Input Alternatives for Machine Learning and Deep Learning Models on Individual Tree Classification Performance. |
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| Authors: | Yan, Tong1 (AUTHOR), Zhang, Kongwen2 (AUTHOR) frank.zhang@ufv.ca, Cheng, Wuxue1,3 (AUTHOR), Liu, Jane1,3 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1848. 24p. |
| Subjects: | Machine learning, Data transformations (Statistics), Deep learning, Crowns (Botany), Random forest algorithms, Principal components analysis |
| Abstract: | Highlights: What are the main findings? The Pseudo Tree Crown (PTC) data transformation method consistently improves the individual tree classification accuracy by at least 5% across various models and data variations. Expanding the original to the green-band PTC implementation, the PTC framework exhibits robust, consistent behavior and high reliability across different data forms and transformations (including GRVI and PCA). What are the implications of the main findings? By proving that PTC works beyond the green band, the framework can now be applied far beyond green-leaf species, unlocking its use for multiseasonal, senescent, or non-green canopy data, showcasing the advantages of PTC in terms of robustness, effectiveness, resilience, reliability, and flexibility. PTC serves as the bridging of 2D top-down nadir imagery and true 3D structures. This injects geometric physical meaning and understanding back into classification processing, instead of strictly relying on the models' "auto-pilot". Individual tree classification has a long history of diverse development, with recent trends focusing on the adoption of machine learning and deep learning approaches. It is a simple and powerful approach that allows the model to auto-pilot while reducing the need for physical characteristic understanding. Over more than a decade of research, we have focused on establishing a direct representation of individual trees that bridges 2D top-down imagery and true 3D models. In this study, we investigated the fundamental question of the influence of the input data on these ML/DL models. In 2024, we introduced a novel data transformation method, the Pseudo Tree Crown (PTC), which provides a pseudo-3D pixel-value perspective that enhances the informational richness of images and significantly improves classification performance. Our original implementation was successfully tested on urban and deciduous trees in 2024 and was later extended to Canadian natural conifer species under snow conditions in 2025. However, the original PTC relied on the green band, limiting its applicability to green-leaf species. In this study, we analyzed and compared the performance of different data variations and transformations, such as the Green–Red Vegetation Index (GRVI) and principal component analysis (PCA), as direct input and used their PTC forms. Classifications were conducted using Random Forest (RF), ResNet50, YOLOv10 and Segment Anything (SA). The results confirmed the effectiveness of the PTC, which consistently improves the classification accuracy by at least 5% without introducing additional computational time or complexity. Furthermore, PTC exhibits robust, consistent behavior across all data forms, demonstrating its strong resilience and reliability. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The Pseudo Tree Crown (PTC) data transformation method consistently improves the individual tree classification accuracy by at least 5% across various models and data variations. Expanding the original to the green-band PTC implementation, the PTC framework exhibits robust, consistent behavior and high reliability across different data forms and transformations (including GRVI and PCA). What are the implications of the main findings? By proving that PTC works beyond the green band, the framework can now be applied far beyond green-leaf species, unlocking its use for multiseasonal, senescent, or non-green canopy data, showcasing the advantages of PTC in terms of robustness, effectiveness, resilience, reliability, and flexibility. PTC serves as the bridging of 2D top-down nadir imagery and true 3D structures. This injects geometric physical meaning and understanding back into classification processing, instead of strictly relying on the models' "auto-pilot". Individual tree classification has a long history of diverse development, with recent trends focusing on the adoption of machine learning and deep learning approaches. It is a simple and powerful approach that allows the model to auto-pilot while reducing the need for physical characteristic understanding. Over more than a decade of research, we have focused on establishing a direct representation of individual trees that bridges 2D top-down imagery and true 3D models. In this study, we investigated the fundamental question of the influence of the input data on these ML/DL models. In 2024, we introduced a novel data transformation method, the Pseudo Tree Crown (PTC), which provides a pseudo-3D pixel-value perspective that enhances the informational richness of images and significantly improves classification performance. Our original implementation was successfully tested on urban and deciduous trees in 2024 and was later extended to Canadian natural conifer species under snow conditions in 2025. However, the original PTC relied on the green band, limiting its applicability to green-leaf species. In this study, we analyzed and compared the performance of different data variations and transformations, such as the Green–Red Vegetation Index (GRVI) and principal component analysis (PCA), as direct input and used their PTC forms. Classifications were conducted using Random Forest (RF), ResNet50, YOLOv10 and Segment Anything (SA). The results confirmed the effectiveness of the PTC, which consistently improves the classification accuracy by at least 5% without introducing additional computational time or complexity. Furthermore, PTC exhibits robust, consistent behavior across all data forms, demonstrating its strong resilience and reliability. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111848 |
