Integrating Hyperspectral Images and LiDAR Data Using Vision Transformers for Enhanced Vegetation Classification
Abstract
:1. Introduction
2. Materials and Methods
2.1. Material
2.1.1. The Trento Dataset
2.1.2. The Houston 2013 Dataset
2.2. Methods
2.2.1. Overall Framework
2.2.2. Spectral–Spatial Extractor Module (SSE)
- 1.
- Feature HSI
- 2.
- Feature LiDAR
2.2.3. Multimodal Fusion Integrator (MFI)
2.2.4. Deep Feature Classifier (DFC)
- 1.
- Patch Embedding
- 2.
- Positional Encoding
- 3.
- Transformer Blocks
- 4.
- Classification
3. Experiments and Results
3.1. Experimental Setup
3.2. Implementation Details and Evaluation
3.2.1. Model Implementation and Training
- Optimizer and learning rate. The Adam optimizer was used for training with a learning rate of 0.01. A comparison among 0.001, 0.005, 0.01, and 0.05 indicated that 0.01 provided the best balance between convergence speed and accuracy. Betas and Epsilon were set to default values, supported by prior studies on ViT in RS.
- Loss function: Soft-Target Cross-Entropy was employed to enhance class separability and mitigate class imbalance, which is common in RS datasets.
- Batch size: A batch size of 32 was chosen, as it balances computational efficiency and generalization ability. Tests with 16, 32, and 64 showed that 32 provided stable gradient updates without excessive memory consumption.
- Number of training epochs: The model was trained for 100 epochs, determined through validation loss convergence analysis. Training with 50 epochs was insufficient, while exceeding 100 epochs led to diminishing returns and increased overfitting risk.
- Regularization techniques: To prevent overfitting, L2 regularization (weight decay = 1 × 10−4) and Dropout (0.3) were applied in the transformer layers, following best practices in DL.
3.2.2. Model Evaluation and Metrics
- Overall accuracy (OA): measures the proportion of correctly predicted observations to the total observations, providing a holistic view of model accuracy.
- Average accuracy (AA): represents the average of accuracies computed for each class, offering insights into class-wise performance.
- Kappa coefficient (): quantifies the agreement of prediction with the true labels, corrected by the randomness of agreement, serving as a robust statistic for classification accuracy.
3.3. Experimental Results
3.3.1. Performance on the Trento Dataset
3.3.2. Performance on the Houston 2013 Dataset
4. Discussion
4.1. Performance Comparison on the Trento Dataset
4.2. Performance Comparison on the Houston 2013 Dataset
4.3. Learning Curve Insights
4.4. Model Strengths and Advantages
- The integration of high-resolution HSI and LiDAR data enables PlantViT to effectively combine spectral and spatial features, which are essential for accurate vegetation and land cover classification. With its multimodal fusion capability, the model excels in detecting fine-scale landscape features, making it particularly advantageous for monitoring both urban and natural ecosystems.
- Advanced DL architecture: The incorporation of LightViT, a lightweight Vision Transformer, significantly enhances computational efficiency while preserving high classification accuracy. By selectively focusing on the most relevant features within the dataset, LightViT minimizes the need for overly complex network architectures and excessive computational resources, ensuring scalability for large-scale applications.
- Generalization across datasets: The consistent performance of PlantViT on both the Trento and Houston 2013 datasets highlights its robustness and adaptability across different geographical regions and land cover types. This strong generalization capability makes it well-suited for large-scale RS tasks, including vegetation analysis and land-use mapping.
4.5. Conclusion of Comparative Performance
5. Conclusions
- Cross-environment adaptability. By integrating HSI and LiDAR data, the model effectively enhances its generalization capability across diverse environmental conditions. Utilizing multiple datasets, including the Trento and Houston 2013 datasets, we demonstrated its adaptability across distinct geographical regions and plant communities. This advancement directly addresses a well-documented challenge in RS—ensuring classification accuracy across heterogeneous ecosystems.
- Overcoming CNN limitations in complex environments. Traditional convolutional neural networks (CNNs) often encounter difficulties in extracting fine-grained spatial–spectral features within intricate plant environments. To mitigate this issue, we incorporated involution-based operations, enabling the model to efficiently capture subtle feature variations while reducing the computational burden typically associated with CNN-based approaches. This enhancement is particularly advantageous for distinguishing morphologically similar plant species within highly heterogeneous landscapes.
- Cross-environment attention mechanism. The fusion of involution with LightViT introduces an innovative attention mechanism that dynamically adjusts to varying environmental contexts. This mechanism enables the model to selectively focus on the most relevant plant features, thereby improving classification robustness and accuracy across diverse ecosystems.
- Future work will involve evaluating the model on additional RS datasets to further assess its robustness and applicability to broader ecological and environmental monitoring tasks.
- Investigating the potential of transfer learning across diverse ecosystems could further enhance the model’s adaptability and generalization capabilities.
- Future research should explore the feasibility of deploying this model in real-time large-scale wetland monitoring. Additionally, incorporating supplementary data sources, such as UAV-based RGB imagery and satellite observations, could expand its practical applications and operational scalability.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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No. | Class Name | Training | Test | Samples |
---|---|---|---|---|
1 | Apple Trees | 129 | 3905 | 4034 |
2 | Buildings | 125 | 2778 | 2903 |
3 | Ground | 105 | 374 | 479 |
4 | Wood | 154 | 8969 | 9123 |
5 | Vineyard | 184 | 10,317 | 10,501 |
6 | Roads | 122 | 3052 | 3174 |
Total | 819 | 29,395 | 30,214 |
No. | Class Name | Training | Test | Samples |
---|---|---|---|---|
1 | Healthy Grass | 198 | 1053 | 1251 |
2 | Stressed Grass | 190 | 1064 | 1254 |
3 | Synthetic Grass | 192 | 505 | 697 |
4 | Tree | 188 | 1056 | 1244 |
5 | Soil | 186 | 1056 | 1242 |
6 | Water | 182 | 143 | 325 |
7 | Residential | 196 | 1072 | 1268 |
8 | Commercial | 191 | 1053 | 1244 |
9 | Road | 193 | 1059 | 1252 |
10 | Highway | 191 | 1036 | 1227 |
11 | Railway | 181 | 1054 | 1235 |
12 | Parking Lot 1 | 192 | 1041 | 1233 |
13 | Parking Lot 2 | 184 | 285 | 469 |
14 | Tennis Court | 181 | 247 | 428 |
15 | Running Track | 187 | 473 | 660 |
Total | 2832 | 12,197 | 15,029 |
No. | Class Name | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|---|
1 | Apple trees | 97.18 | 99.18 | 97.33 | 98.25 |
2 | Buildings | 97.92 | 98.23 | 97.92 | 98.07 |
3 | Ground | 97.96 | 95.68 | 97.96 | 96.81 |
4 | Wood | 100.00 | 99.99 | 99.99 | 99.99 |
5 | Vineyard | 99.36 | 99.35 | 99.35 | 99.35 |
6 | Roads | 98.22 | 96.82 | 97.46 | 97.14 |
OA | 99.00 | ||||
AA | 98.44 | ||||
98.66 |
No. | Class Name | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|---|
1 | Healthy Grass | 97.15 | 97.00 | 98.00 | 98.00 |
2 | Stressed Grass | 97.46 | 97.00 | 98.00 | 98.00 |
3 | Synthetic Grass | 99.80 | 99.00 | 99.00 | 99.00 |
4 | Trees | 95.55 | 100.00 | 98.00 | 97.00 |
5 | Soil | 100.00 | 100.00 | 100.00 | 100.00 |
6 | Water | 97.20 | 97.00 | 98.00 | 98.00 |
7 | Residential | 92.82 | 97.00 | 95.00 | 94.00 |
8 | Commercial | 96.39 | 96.00 | 96.00 | 95.00 |
9 | Road | 97.92 | 98.00 | 97.00 | 97.00 |
10 | Highway | 100.00 | 100.00 | 100.00 | 100.00 |
11 | Railway | 96.39 | 97.00 | 96.00 | 95.00 |
12 | Parking Lot 1 | 99.62 | 100.00 | 99.00 | 99.00 |
13 | Parking Lot 2 | 98.60 | 100.00 | 99.00 | 99.00 |
14 | Tennis Court | 100.00 | 100.00 | 100.00 | 100.00 |
15 | Running Track | 94.93 | 95.00 | 96.00 | 94.00 |
OA | 97.41 | ||||
AA | 97.59 | ||||
97.19 |
No. | Class Name | PlantViT | EndNet [33] | DCT [16] | MBF [34] | AM3Net [35] | HES2T [36] | BTRF [37] | TTCNN [38] |
---|---|---|---|---|---|---|---|---|---|
1 | Apple Trees | 97.18 | 88.19 | 99.13 | 98.71 | 97.12 | 99.46 | 95.92 | 98.07 |
2 | Buildings | 97.92 | 98.49 | 99.44 | 98.87 | 96.73 | 91.65 | 98.04 | 95.21 |
3 | Ground | 97.96 | 95.19 | 99.94 | 97.79 | 98.96 | 87.43 | 80.33 | 93.32 |
4 | Wood | 100.00 | 99.30 | 92.76 | 99.89 | 99.12 | 98.33 | 99.83 | 99.93 |
5 | Vineyard | 99.36 | 91.96 | 86.37 | 99.25 | 98.22 | 97.65 | 99.29 | 98.78 |
6 | Roads | 98.22 | 90.14 | 92.78 | 92.38 | 93.76 | 68.94 | 97.66 | 89.98 |
OA | 99.00 | 94.17 | 97.45 | 98.62 | 97.75 | 94.42 | 98.66 | 97.92 | |
AA | 98.44 | 93.88 | 95.07 | 98.14 | 97.32 | 90.58 | 95.30 | 96.19 | |
98.66 | 92.22 | 96.58 | 97.81 | 97.00 | 92.54 | 98.20 | 96.81 |
No. | Class Name | PlantViT | MSST [39] | mFormer [40] | S2CA [41] | RFFT [42] | MATA [43] | CSF [44] | SFE-FN [22] |
---|---|---|---|---|---|---|---|---|---|
1 | Healthy Grass | 97.15 | 89.12 | 82.37 | 84.62 | 87.96 | 91.64 | 82.72 | 95.06 |
2 | Stressed Grass | 97.46 | 92.85 | 85.03 | 95.68 | 95.90 | 83.83 | 82.24 | 96.52 |
3 | Synthetic Grass | 99.80 | 95.43 | 98.09 | 100.00 | 97.41 | 100.00 | 100.00 | 100.00 |
4 | Trees | 95.55 | 91.13 | 95.23 | 99.62 | 97.74 | 99.24 | 92.52 | 100.00 |
5 | Soil | 100.00 | 100.00 | 99.15 | 100.00 | 95.32 | 99.34 | 98.67 | 99.81 |
6 | Water | 97.20 | 86.31 | 98.14 | 100.00 | 97.84 | 100.00 | 95.10 | 97.20 |
7 | Residential | 92.82 | 72.35 | 89.61 | 87.03 | 92.57 | 85.91 | 80.97 | 93.00 |
8 | Commercial | 96.39 | 76.87 | 72.33 | 93.02 | 95.42 | 94.59 | 81.67 | 88.98 |
9 | Road | 97.92 | 79.40 | 88.86 | 89.71 | 91.74 | 91.78 | 86.87 | 87.44 |
10 | Highway | 100.00 | 99.57 | 61.97 | 71.43 | 99.10 | 91.12 | 79.63 | 98.17 |
11 | Railway | 96.39 | 98.06 | 96.24 | 98.96 | 96.86 | 97.91 | 83.30 | 93.55 |
12 | Parking Lot 1 | 99.62 | 98.09 | 94.46 | 98.94 | 94.56 | 95.39 | 88.95 | 96.35 |
13 | Parking Lot 2 | 98.60 | 80.35 | 87.02 | 85.67 | 95.30 | 92.98 | 87.37 | 81.40 |
14 | Tennis Court | 100.00 | 100.00 | 99.73 | 91.90 | 93.22 | 100.00 | 100.00 | 100.00 |
15 | Running Track | 94.93 | 100.00 | 96.69 | 100.00 | 97.57 | 100.00 | 99.52 | 100.00 |
OA | 97.41 | 90.29 | 87.85 | 92.52 | 95.01 | 93.83 | 89.42 | 95.11 | |
AA | 97.59 | 90.63 | 89.66 | 93.11 | 95.23 | 94.92 | 89.24 | 95.17 | |
97.19 | 89.50 | 86.81 | 92.52 | 94.60 | 93.31 | 88.51 | 94.68 |
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Shu, X.; Ma, L.; Chang, F. Integrating Hyperspectral Images and LiDAR Data Using Vision Transformers for Enhanced Vegetation Classification. Forests 2025, 16, 620. https://doi.org/10.3390/f16040620
Shu X, Ma L, Chang F. Integrating Hyperspectral Images and LiDAR Data Using Vision Transformers for Enhanced Vegetation Classification. Forests. 2025; 16(4):620. https://doi.org/10.3390/f16040620
Chicago/Turabian StyleShu, Xingquan, Limin Ma, and Fengqin Chang. 2025. "Integrating Hyperspectral Images and LiDAR Data Using Vision Transformers for Enhanced Vegetation Classification" Forests 16, no. 4: 620. https://doi.org/10.3390/f16040620
APA StyleShu, X., Ma, L., & Chang, F. (2025). Integrating Hyperspectral Images and LiDAR Data Using Vision Transformers for Enhanced Vegetation Classification. Forests, 16(4), 620. https://doi.org/10.3390/f16040620