Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS Imagery
The accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning m...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2275 |
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| author | Jiawei Li Huihui Zhang David Barnard |
| author_facet | Jiawei Li Huihui Zhang David Barnard |
| author_sort | Jiawei Li |
| collection | DOAJ |
| description | The accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. This study employed high-resolution imagery captured by unmanned aircraft systems (UASs) throughout the shrub growing season and explored the effectiveness of transfer learning for both semantic segmentation (Attention U-Net) and instance segmentation (Mask R-CNN). It utilized pre-trained model weights from two previous studies that originally focused on tree crown delineation to improve shrub crown segmentation in non-forested areas. Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. However, fine-tuning the pre-trained models by unfreezing additional layers improved segmentation accuracy by around 30%. Fine-tuned pre-trained models show limited sensitivity to shrubs in the early growing season (April to June) and improved performance when shrub crowns become more spectrally unique in late summer (July to September). These findings highlight the value of combining pre-trained models with targeted fine-tuning to enhance model adaptability in complex remote sensing environments. The proposed framework demonstrates a scalable solution for ecological monitoring in data-scarce regions, supporting informed land management decisions and advancing the use of deep learning for long-term environmental monitoring. |
| format | Article |
| id | doaj-art-9f934876db0b4ecea8e980cba0fa92ab |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9f934876db0b4ecea8e980cba0fa92ab2025-08-20T03:50:16ZengMDPI AGRemote Sensing2072-42922025-07-011713227510.3390/rs17132275Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS ImageryJiawei Li0Huihui Zhang1David Barnard2Water Management and Systems Research Unit, United States Department of Agriculture, Agricultural Research Service, 2150 Centre Avenue, Building D, Fort Collins, CO 80526, USAWater Management and Systems Research Unit, United States Department of Agriculture, Agricultural Research Service, 2150 Centre Avenue, Building D, Fort Collins, CO 80526, USAWater Management and Systems Research Unit, United States Department of Agriculture, Agricultural Research Service, 2150 Centre Avenue, Building D, Fort Collins, CO 80526, USAThe accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. This study employed high-resolution imagery captured by unmanned aircraft systems (UASs) throughout the shrub growing season and explored the effectiveness of transfer learning for both semantic segmentation (Attention U-Net) and instance segmentation (Mask R-CNN). It utilized pre-trained model weights from two previous studies that originally focused on tree crown delineation to improve shrub crown segmentation in non-forested areas. Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. However, fine-tuning the pre-trained models by unfreezing additional layers improved segmentation accuracy by around 30%. Fine-tuned pre-trained models show limited sensitivity to shrubs in the early growing season (April to June) and improved performance when shrub crowns become more spectrally unique in late summer (July to September). These findings highlight the value of combining pre-trained models with targeted fine-tuning to enhance model adaptability in complex remote sensing environments. The proposed framework demonstrates a scalable solution for ecological monitoring in data-scarce regions, supporting informed land management decisions and advancing the use of deep learning for long-term environmental monitoring.https://www.mdpi.com/2072-4292/17/13/2275deep learningshrub segmentationUASattention U-NetMask R-CNN |
| spellingShingle | Jiawei Li Huihui Zhang David Barnard Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS Imagery Remote Sensing deep learning shrub segmentation UAS attention U-Net Mask R-CNN |
| title | Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS Imagery |
| title_full | Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS Imagery |
| title_fullStr | Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS Imagery |
| title_full_unstemmed | Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS Imagery |
| title_short | Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS Imagery |
| title_sort | transfer learning based accurate detection of shrub crown boundaries using uas imagery |
| topic | deep learning shrub segmentation UAS attention U-Net Mask R-CNN |
| url | https://www.mdpi.com/2072-4292/17/13/2275 |
| work_keys_str_mv | AT jiaweili transferlearningbasedaccuratedetectionofshrubcrownboundariesusinguasimagery AT huihuizhang transferlearningbasedaccuratedetectionofshrubcrownboundariesusinguasimagery AT davidbarnard transferlearningbasedaccuratedetectionofshrubcrownboundariesusinguasimagery |