Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty
Sábháilte in:
| Foilsithe in: | Remote Sensing vol. 17, no. 6 (2025), p. 1066 |
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| Príomhchruthaitheoir: | |
| Rannpháirtithe: | |
| Foilsithe / Cruthaithe: |
MDPI AG
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| Ábhair: | |
| Rochtain ar líne: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3182137532 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17061066 |2 doi | |
| 035 | |a 3182137532 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Joshi, Durga | |
| 245 | 1 | |a Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Forest health monitoring at scale requires high-spatial-resolution remote sensing images coupled with deep learning image analysis methods. However, high-quality large-scale datasets are costly to acquire. To address this challenge, we explored the potential of freely available National Agricultural Imagery Program (NAIP) imagery. By comparing the performance of traditional convolutional neural network (CNN) models (U-Net and DeepLabv3+) with a state-of-the-art Vision Transformer (SegFormer), we aimed to determine the optimal approach for detecting unhealthy tree crowns (UTC) using a publicly available data source. Additionally, we investigated the impact of different spectral band combinations on model performance to identify the most effective configuration without incurring additional data acquisition costs. We explored various band combinations, including RGB, color infrared (CIR), vegetation indices (VIs), principal components (PC) of texture features (PCA), and spectral band with PC (RGBPC). Furthermore, we analyzed the uncertainty associated with potential subjective crown annotation and its impact on model evaluation. Our results demonstrated that the Vision Transformer-based model, SegFormer, outperforms traditional CNN-based models, particularly when trained on RGB images yielding an F1-score of 0.85. In contrast, DeepLabv3+ achieved F1-score of 0.82. Notably, PCA-based inputs yield reduced performance across all models, with U-Net producing particularly poor results (F1-score as low as 0.03). The uncertainty analysis indicated that the Intersection over Union (IoU) could fluctuate between 14.81% and 57.41%, while F1-scores ranged from 8.57% to 47.14%, reflecting the significant sensitivity of model performance to inconsistencies in ground truth annotations. In summary, this study demonstrates the feasibility of using publicly available NAIP imagery and advanced deep learning techniques to accurately detect unhealthy tree canopies. These findings highlight SegFormer’s superior ability to capture complex spatial patterns, even in relatively low-resolution (60 cm) datasets. Our findings underline the considerable influence of human annotation errors on model performance, emphasizing the need for standardized annotation guidelines and quality control measures. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Data acquisition | ||
| 653 | |a Accuracy | ||
| 653 | |a Vision | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Quality control | ||
| 653 | |a Models | ||
| 653 | |a Color imagery | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Biodiversity | ||
| 653 | |a Remote sensing | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Image processing | ||
| 653 | |a Annotations | ||
| 653 | |a Feasibility studies | ||
| 653 | |a Uncertainty analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Image | ||
| 653 | |a Vegetation | ||
| 653 | |a Image analysis | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Vegetation index | ||
| 653 | |a Ground truth | ||
| 653 | |a Classification | ||
| 653 | |a Image acquisition | ||
| 653 | |a Image quality | ||
| 653 | |a Neural networks | ||
| 653 | |a Semantics | ||
| 700 | 1 | |a Witharana, Chandi | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 6 (2025), p. 1066 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3182137532/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3182137532/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3182137532/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |