Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Acquisition System
2.2. Data Pre-Processing
2.3. Semantic Segmentation Algorithms and Hyperparameter Fine-Tuning
2.3.1. Decision Tree and Random Forest
2.3.2. Convolutional Neural Networks
2.3.3. Model Hyperparameters Fine-Tuning
2.4. Semantic Segmentation Performance Evaluation
3. Results
3.1. Semantic Segmentation Performance of Individual Algorithms with Images in Original Spatial Resolution
3.2. Semantic Segmentation Performance with Images in Down-Sampled Spatial Resolutions
3.3. Visualizations of the Forest Classification
4. Discussion
4.1. Opportunities and Challenges in ERC Early Detection with High Spatial Resolution Remote Sensing Imagery
4.2. CNN-Based Models Improve Data Utilization of Ultra-High Spatial Resolution Imagery for Multi-Species Classification
4.3. Trade-Off between Spatial Resolution and Coverage for Encroachment Species Detection and Mapping
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Layer | Filter Size | Stride | Number of Filters | Output Dimension |
---|---|---|---|---|
Convolutional layer 1 | 11 × 11 | 4 | 96 | 55 × 55 × 96 |
Max pooling layer 1 | 3 × 3 | 2 | / | 27 × 27 × 96 |
Convolutional layer 2 | 5 × 5 | 1 | 256 | 27 × 27 × 256 |
Max pooling layer 2 | 3 × 3 | 2 | / | 13 × 13 × 256 |
Convolutional layer 3 | 3 × 3 | 1 | 384 | 13 × 13 × 384 |
Convolutional layer 4 | 3 × 3 | 1 | 384 | 13 × 13 × 384 |
Convolutional layer 5 | 3 × 3 | 1 | 256 | 13 × 13 × 256 |
Max pooling layer 3 | 3 × 3 | 2 | / | 6 × 6 × 256 |
Fully connected layer 1 | / | / | / | 4096 |
Fully connected layer 2 | / | / | / | 4096 |
Fully connected layer 3 | / | / | / | 4 |
Layer | Filter Size | Stride | Number of Filters | Output Dimension |
---|---|---|---|---|
Convolutional layer 1 | 7 × 7 | 2 | 64 | 112 × 112 × 64 |
Max pooling layer 1 | 3 × 3 | 2 | / | 56 × 56 × 64 |
Convolutional layer 2 | × 3 | 56 × 56 × 256 | ||
Convolutional layer 3 | × 4 | 28 × 28 × 512 | ||
Convolutional layer 4 | × 6 | 14 × 14 × 1024 | ||
Convolutional layer 5 | × 3 | 7 × 7 × 2048 | ||
Average pooling layer 1 | 7 × 7 | 1 | / | 2048 |
Fully connected layer 1 | / | / | / | 4 |
Algorithms | Overall Accuracy | mIoU (%) | IoU of Redcedar (%) | IoU of Defoliation (%) | IoU of Pine (%) | IoU of Others (%) |
---|---|---|---|---|---|---|
Decision Tree | 0.610 | 44.7 | 61.3 | 31.7 | 41.2 | 44.8 |
Random Forest | 0.666 | 50.7 | 68.7 | 35.7 | 47.2 | 51.1 |
AlexNet | 0.878 | 78.2 | 79.5 | 72.8 | 81.5 | 79.1 |
ResNet | 0.918 | 85.0 | 86.3 | 80.1 | 90.6 | 82.8 |
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Wang, L.; Zhou, Y.; Hu, Q.; Tang, Z.; Ge, Y.; Smith, A.; Awada, T.; Shi, Y. Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms. Remote Sens. 2021, 13, 1975. https://doi.org/10.3390/rs13101975
Wang L, Zhou Y, Hu Q, Tang Z, Ge Y, Smith A, Awada T, Shi Y. Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms. Remote Sensing. 2021; 13(10):1975. https://doi.org/10.3390/rs13101975
Chicago/Turabian StyleWang, Lin, Yuzhen Zhou, Qiao Hu, Zhenghong Tang, Yufeng Ge, Adam Smith, Tala Awada, and Yeyin Shi. 2021. "Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms" Remote Sensing 13, no. 10: 1975. https://doi.org/10.3390/rs13101975