Wet-ConViT: A Hybrid Convolutional–Transformer Model for Efficient Wetland Classification Using Satellite Data
Abstract
:1. Introduction
2. Study Area and Dataset
Reference Data
3. Methodology
3.1. Proposed Framework
3.1.1. Modified Multi-Head Self-Attention (MMHSA)
3.1.2. Multi-Head Convolutional Attention (MHCA)
3.1.3. Local Feed-Forward Network (LFFN)
3.1.4. Convolutional and Local Transformer Blocks
3.2. Evaluation Models and Metrics
4. Experiments and Results
4.1. Implementation Details
4.2. Classification Maps
4.3. UMAP Feature Distribution
4.4. Statistical Results
4.5. Result Analysis and Discussion
4.6. Impact of Multi-Source Satellite Data
4.7. Efficiency Analysis
4.8. Patch Size Effect
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Class | Polygons | Samples |
---|---|---|
Bog | 98 | 42,148 |
Fen | 113 | 29,648 |
Swamp | 116 | 15,424 |
Marsh | 49 | 3445 |
Water | 68 | 24,615 |
Forests | 103 | 35,452 |
Shrublands | 37 | 6400 |
Grassland | 126 | 17,624 |
Pastures | 47 | 19,745 |
Barren | 24 | 1789 |
Urban | 48 | 25,308 |
Total | 829 | 221,598 |
Models | ||||||||
---|---|---|---|---|---|---|---|---|
Convolution | Transformer | Hybrid | ||||||
Class | ResNet50 | EfficientNet | ViT | Swin | CvT | CoAtNet | WMP | Proposed |
Bog | 90.14% | 89.07% | 81.94% | 88.87% | 90.71% | 92.15% | 88.15% | 94.79% |
Fen | 83.29% | 82.61% | 78.14% | 81.19% | 84.63% | 86.85% | 82.45% | 90.57% |
Swamp | 80.66% | 79.81% | 73.93% | 75.56% | 79.01% | 84.47% | 81.37% | 89.04% |
Marsh | 75.80% | 79.42% | 79.59% | 76.91% | 84.94% | 89.52% | 82.03% | 89.91% |
Water | 98.66% | 98.62% | 98.97% | 98.63% | 99.07% | 99.21% | 99.43% | 99.02% |
Forests | 90.52% | 90.34% | 87.32% | 89.20% | 90.94% | 94.29% | 91.09% | 95.96% |
Shrublands | 88.35% | 87.61% | 84.46% | 85.56% | 91.29% | 96.66% | 88.18% | 94.88% |
Grassland | 82.86% | 82.77% | 78.15% | 79.45% | 83.29% | 89.75% | 85.89% | 93.41% |
Pastures | 93.89% | 94.93% | 92.23% | 91.71% | 93.74% | 95.40% | 96.33% | 98.08% |
Barren | 92.33% | 89.85% | 92.35% | 86.20% | 82.03% | 98.66% | 94.63% | 94.23% |
Urban | 98.77% | 98.32% | 98.90% | 97.90% | 98.44% | 99.21% | 99.13% | 99.46% |
OA | 90.39% | 90.18% | 87.05% | 88.75% | 90.82% | 93.34% | 90.91% | 95.36% |
AA | 88.66% | 88.49% | 86.00% | 86.47% | 88.92% | 93.29% | 88.84% | 94.49% |
k | 89.08% | 88.82% | 85.26% | 87.22% | 89.56% | 92.44% | 89.64% | 94.72% |
S1 | S2 | S1S2 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Class | CvT | CoAtNet | WMF | Proposed | CvT | CoAtNet | WMF | Proposed | CvT | CoAtNet | WMF | Proposed |
Bog | 70.41% | 81.89% | 58.56% | 79.79% | 81.60% | 83.22% | 79.86% | 93.78% | 90.71% | 92.15% | 88.15% | 94.79% |
Fen | 61.47% | 75.63% | 54.44% | 72.88% | 79.99% | 79.26% | 78.46% | 89.19% | 84.63% | 86.85% | 82.45% | 90.57% |
Swamp | 50.37% | 65.07% | 28.62% | 68.36% | 67.46% | 71.90% | 70.34% | 86.63% | 79.01% | 84.47% | 81.37% | 89.04% |
Marsh | 64.57% | 73.84% | 48.72% | 79.11% | 48.44% | 59.78% | 51.98% | 87.73% | 84.94% | 89.52% | 82.03% | 89.91% |
Water | 96.80% | 96.49% | 95.73% | 97.75% | 97.25% | 98.52% | 98.14% | 97.83% | 99.07% | 99.21% | 99.43% | 99.02% |
Forests | 72.28% | 82.66% | 72.58% | 85.36% | 80.93% | 85.03% | 85.29% | 94.06% | 90.94% | 94.29% | 91.09% | 95.96% |
Shrublands | 56.78% | 67.09% | 46.79% | 67.56% | 85.72% | 83.40% | 87.33% | 90.98% | 91.29% | 96.66% | 88.18% | 94.88% |
Grassland | 52.39% | 65.76% | 16.41% | 70.36% | 77.26% | 79.86% | 79.35% | 90.14% | 83.29% | 89.75% | 85.89% | 93.41% |
Pastures | 85.89% | 91.33% | 84.11% | 91.46% | 85.29% | 90.79% | 90.89% | 96.51% | 93.74% | 95.40% | 96.33% | 98.08% |
Barren | 60.53% | 73.73% | 30.16% | 72.39% | 39.24% | 45.81% | 72.22% | 92.31% | 82.03% | 98.66% | 94.63% | 94.23% |
Urban | 84.97% | 93.37% | 86.08% | 90.47% | 94.85% | 94.78% | 94.49% | 98.74% | 98.44% | 99.21% | 99.13% | 99.46% |
OA | 72.43% | 82.36% | 67.52% | 82.56% | 82.23% | 84.42% | 83.89% | 93.73% | 90.82% | 93.34% | 90.91% | 95.36% |
AA | 69.51% | 78.79% | 56.62% | 80.78% | 77.37% | 81.93% | 79.43% | 92.75% | 88.92% | 93.29% | 88.84% | 94.49% |
k | 68.77% | 79.95% | 62.77% | 80.18% | 79.75% | 82.23% | 81.59% | 92.87% | 89.56% | 92.44% | 89.64% | 94.72% |
ResNet50 | EfficientNet | ViT | Swin | CvT | CoAtNet | WMF | Proposed | |
---|---|---|---|---|---|---|---|---|
Params (millions) | 23.57 | 20.19 | 87.72 | 27.61 | 19.66 | 17.37 | 0.7 | 9.44 |
Memory size (MB) | 90.2 | 77.9 | 335.1 | 105.8 | 75.3 | 66.4 | 8.3 | 36.3 |
Training time (s) | 63 | 122 | 122 | 276 | 116 | 75 | 37 | 77 |
Inference time (s) | 5.99 | 13.64 | 13.08 | 32.99 | 19.73 | 15.55 | 3.07 | 9.16 |
OA (%) | 90.39 | 90.18 | 87.05 | 88.75 | 90.82 | 93.34 | 90.91 | 95.36 |
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Radman, A.; Mohammadimanesh, F.; Mahdianpari, M. Wet-ConViT: A Hybrid Convolutional–Transformer Model for Efficient Wetland Classification Using Satellite Data. Remote Sens. 2024, 16, 2673. https://doi.org/10.3390/rs16142673
Radman A, Mohammadimanesh F, Mahdianpari M. Wet-ConViT: A Hybrid Convolutional–Transformer Model for Efficient Wetland Classification Using Satellite Data. Remote Sensing. 2024; 16(14):2673. https://doi.org/10.3390/rs16142673
Chicago/Turabian StyleRadman, Ali, Fariba Mohammadimanesh, and Masoud Mahdianpari. 2024. "Wet-ConViT: A Hybrid Convolutional–Transformer Model for Efficient Wetland Classification Using Satellite Data" Remote Sensing 16, no. 14: 2673. https://doi.org/10.3390/rs16142673