Spectral-Spatial Offset Graph Convolutional Networks for Hyperspectral Image Classification
Abstract
:1. Introduction
2. Related Works
2.1. Graph Convolutional Network
2.2. Hyperspectral Image Classification
3. Method
3.1. Automatic Graph Learning (AGL)
3.2. Graph Classification Networks
3.2.1. GCN
3.2.2. OGC
3.2.3. Graph Pooling
4. Experiment
4.1. Data Sets
- (1)
- Indian Pines Data Set: The scene over northwestern Indiana, USA was acquired over the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor in 1992. The image consists of 145 × 145 pixels and the spatial resolution is 20 m per pixel.
- (2)
- Pavia University Data Set: The second image comprised by 610 × 340 pixels and each pixel are 1.3 m. It was acquired by the Reflective Optics System Imaging Spectrometer (ROSIS) sensor in 2002. There are 115 bands in the range of 0.43~0.86, and 103 bands without serious noise are selected for experiment. The data set includes nine land cover classes, and a total of 42,776 samples can be referred. As shown in Figure 6 below, the left image is a false color map, the middle column is a ground-truth map, and the right is the corresponding class name. Table 3 lists 9 major land-cover classes in this image, as well as the number of training and testing samples used for our experiments.
- (3)
- Salinas Data Set: The scene over Salinas Valley, California was acquired the AVIRIS sensor. The image consists of 512 × 217 pixels and the spatial resolution is 3.7 m per pixel. There are 204 bands are available after discarding the 20 water absorption bands. The data set contains 16 types of features, and 54,129 samples can be referred. Table 4 lists 16 main land-cover categories involved in this scene, as well as the number of training and testing samples used for our experiments. The false color map and ground-truth map are shown in Figure 7.
4.2. Experimental Settings
4.3. Classification Results
4.3.1. Results on the Indian Pines Data Set
4.3.2. Results on the Pavia University Data Set
4.3.3. Results on the Salinas Data Set
4.4. Impact of Patch Size
4.5. Ablation Study
4.6. Impact of the Number of Labeled Samples
4.7. Running Time
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Layer | Module | Input Tensor Size | Output Tensor Size |
---|---|---|---|
1 | GCN | 32 × D × 49 | 32 × 32 × 49 |
POOL | 32 × 32 × 49 | 32 × 32 × 16 | |
2 | OGC | 32 × 32 × 16 | 32 × 32 × 16 |
POOL | 32 × 32 × 16 | 32 × 32 × 4 | |
3 | OGC | 32 × 32 × 4 | 32 × 32 × 4 |
POOL | 32 × 32 × 4 | 32 × 32 × 1 | |
4 | FC | 32 × 32 | 32 × C |
Class | Class Name | Train | Test |
---|---|---|---|
1 | Alfalfa | 15 | 31 |
2 | Corn-notill | 50 | 1378 |
3 | Corn-mintill | 50 | 780 |
4 | Corn | 50 | 187 |
5 | Grass-pasture | 50 | 433 |
6 | Grass-trees | 50 | 680 |
7 | Grass-pasture-mowed | 15 | 13 |
8 | Hay-windrowed | 50 | 428 |
9 | Oats | 15 | 5 |
10 | Soybean-notill | 50 | 922 |
11 | Soybean-mintill | 50 | 2405 |
12 | Soybean-clean | 50 | 543 |
13 | Wheat | 50 | 155 |
14 | Woods | 50 | 1215 |
15 | Buildings-Grass-Trees-Drives | 50 | 336 |
16 | Stone-Steel-Towers | 50 | 43 |
Total | 695 | 9554 |
Class | Class Name | Train | Test |
---|---|---|---|
1 | Asphalt | 50 | 6581 |
2 | Meadows | 50 | 18,599 |
3 | Gravel | 50 | 2049 |
4 | Trees | 50 | 3014 |
5 | Painted metal sheets | 50 | 1295 |
6 | Bare Soil | 50 | 4979 |
7 | Bitumen | 50 | 1280 |
8 | Self-Blocking Bricks | 50 | 3632 |
9 | Shadows | 50 | 897 |
Total | 450 | 42,326 |
Class | Class Name | Train | Test |
---|---|---|---|
1 | Brocoli_green_weeds_1 | 50 | 1959 |
2 | Brocoli_green_weeds_2 | 50 | 3676 |
3 | Fallow | 50 | 1926 |
4 | Fallow_rough_plow | 50 | 1344 |
5 | Fallow_smooth | 50 | 2628 |
6 | Stubble | 50 | 3909 |
7 | Celery | 50 | 3529 |
8 | Grapes_untrained | 50 | 11,221 |
9 | Soil_vinyard_develop | 50 | 6153 |
10 | Corn_senesced_green_weeds | 50 | 3228 |
11 | Lettuce_romaine_4wk | 50 | 1018 |
12 | Lettuce_romaine_5wk | 50 | 1877 |
13 | Lettuce_romaine_6wk | 50 | 866 |
14 | Lettuce_romaine_7wk | 50 | 1020 |
15 | Vinyard_untrained | 50 | 7218 |
16 | Vinyard_vertical_trellis | 50 | 1757 |
Total | 800 | 53,329 |
Class | KNN | SVM | 2D-CNN | CNN-PPF | GCN | miniGCN | MDGCN | FuNet-C | SSOGCN |
---|---|---|---|---|---|---|---|---|---|
1 | 15.54 | 16.89 | 12.03 | 16.22 | 18.92 | 17.57 | 93.54 | 29.59 | 20.95 |
2 | 45.79 | 68.94 | 70.6 | 67.49 | 75.47 | 68.07 | 65.82 | 78.81 | 87.52 |
3 | 54.87 | 57.56 | 68.25 | 55.38 | 62.05 | 53.97 | 83.33 | 84.49 | 93.46 |
4 | 63.64 | 79.68 | 99.52 | 78.07 | 86.63 | 66.84 | 96.25 | 96.26 | 100.0 |
5 | 84.30 | 89.15 | 94.48 | 89.61 | 88.68 | 77.37 | 79.44 | 97.92 | 98.38 |
6 | 87.65 | 91.32 | 100.0 | 92.50 | 94.85 | 93.38 | 92.05 | 99.12 | 98.68 |
7 | 92.31 | 92.31 | 100.0 | 100.0 | 100.0 | 100.0 | 23.07 | 100.0 | 100.0 |
8 | 89.72 | 95.09 | 97.1 | 96.73 | 97.20 | 98.36 | 100.0 | 100.0 | 100.0 |
9 | 80.00 | 100.0 | 100.0 | 100.0 | 100.0 | 80.00 | 0.00 | 100.0 | 100.0 |
10 | 67.68 | 77.66 | 75.58 | 74.51 | 80.48 | 69.52 | 73.53 | 85.25 | 92.41 |
11 | 49.94 | 59.09 | 70.8 | 63.58 | 59.58 | 63.04 | 88.77 | 78.50 | 90.19 |
12 | 44.94 | 62.80 | 65.72 | 78.08 | 79.56 | 64.64 | 67.77 | 79.74 | 95.76 |
13 | 96.13 | 98.06 | 100.0 | 100.0 | 98.71 | 98.06 | 100.0 | 100.0 | 99.35 |
14 | 74.65 | 80.00 | 89.15 | 84.44 | 80.41 | 86.17 | 92.02 | 96.30 | 97.12 |
15 | 52.98 | 71.43 | 84.27 | 76.19 | 80.06 | 69.64 | 96.43 | 89.29 | 99.11 |
16 | 93.02 | 93.02 | 100.00 | 97.67 | 95.35 | 90.70 | 83.72 | 100.0 | 100.0 |
OA(%) | 61.06 | 71.20 | 78.93 | 73.42 | 74.7 | 71.33 | 80.53 | 85.54 | 92.51 |
AA(%) | 68.32 | 77.06 | 83.32 | 79.41 | 81.12 | 74.83 | 77.23 | 87.83 | 92.06 |
KA(%) | 56.29 | 67.60 | 76.10 | 69.91 | 71.47 | 67.42 | 81.11 | 83.52 | 91.45 |
Class | KNN | SVM | 2D-CNN | CNN-PPF | GCN | miniGCN | MDGCN | FuNet-C | SSOGCN |
---|---|---|---|---|---|---|---|---|---|
1 | 67.45 | 63.07 | 76.10 | 57.73 | 64.03 | 78.62 | 62.80 | 85.34 | 95.00 |
2 | 70.88 | 82.59 | 83.52 | 83.56 | 83.42 | 86.77 | 88.67 | 95.33 | 99.09 |
3 | 67.01 | 81.80 | 75.40 | 82.97 | 78.09 | 70.82 | 93.76 | 91.65 | 84.58 |
4 | 88.12 | 90.44 | 97.51 | 89.28 | 89.25 | 88.49 | 81.89 | 96.95 | 95.92 |
5 | 98.92 | 99.61 | 99.46 | 99.00 | 98.61 | 98.53 | 97.17 | 99.92 | 99.77 |
6 | 69.05 | 82.43 | 78.53 | 54.35 | 86.10 | 81.74 | 99.22 | 90.76 | 97.23 |
7 | 87.97 | 92.11 | 94.77 | 93.52 | 90.47 | 90.16 | 85.19 | 97.89 | 98.28 |
8 | 71.97 | 78.85 | 84.55 | 62.89 | 79.87 | 83.07 | 79.79 | 88.79 | 96.26 |
9 | 100.0 | 99.89 | 100.0 | 100.0 | 100.0 | 100.0 | 53.33 | 100.0 | 99.89 |
OA(%) | 73.26 | 80.44 | 83.65 | 75.35 | 81.14 | 84.69 | 84.24 | 92.93 | 97.08 |
AA(%) | 80.15 | 85.64 | 87.76 | 80.36 | 85.54 | 86.47 | 82.37 | 94.07 | 96.22 |
KA(%) | 65.96 | 74.96 | 78.79 | 67.64 | 75.84 | 79.99 | 79.57 | 90.66 | 96.11 |
Class | KNN | SVM | 2D-CNN | CNN-PPF | miniGCN | MDGCN | FuNet-C | SSOGCN |
---|---|---|---|---|---|---|---|---|
1 | 88.42 | 97.13 | 95.61 | 85.15 | 97.60 | 100.0 | 100.0 | 100.0 |
2 | 88.25 | 97.08 | 97.03 | 99.97 | 99.89 | 100.0 | 99.97 | 100.0 |
3 | 93.82 | 91.49 | 96.0 | 94.65 | 93.87 | 63.11 | 99.22 | 99.88 |
4 | 90.48 | 99.56 | 99.55 | 99.63 | 99.03 | 97.06 | 99.55 | 99.75 |
5 | 83.87 | 92.84 | 96.88 | 97.41 | 97.45 | 99.74 | 97.34 | 97.26 |
6 | 82.02 | 99.72 | 99.39 | 99.82 | 99.97 | 100.0 | 100.0 | 100.0 |
7 | 87.79 | 99.3 | 99.12 | 99.60 | 99.63 | 100.0 | 99.43 | 100.0 |
8 | 50.84 | 57.58 | 51.38 | 92.06 | 67.13 | 85.47 | 64.46 | 83.12 |
9 | 81.75 | 97.69 | 97.40 | 99.58 | 99.38 | 100.0 | 99.93 | 99.80 |
10 | 97.40 | 85.0 | 90.68 | 89.71 | 92.13 | 98.66 | 97.15 | 97.84 |
11 | 78.09 | 92.81 | 98.13 | 93.03 | 97.35 | 48.78 | 99.71 | 99.45 |
12 | 87.16 | 98.74 | 99.89 | 99.73 | 99.89 | 100.0 | 100.0 | 99.94 |
13 | 88.34 | 99.10 | 99.65 | 99.77 | 99.54 | 100.0 | 100.0 | 100.0 |
14 | 80.29 | 90.81 | 98.63 | 93.24 | 98.14 | 99.63 | 98.92 | 100.0 |
15 | 63.52 | 52.01 | 66.42 | 23.04 | 70.16 | 81.84 | 76.20 | 91.99 |
16 | 89.53 | 93.66 | 97.10 | 98.01 | 98.41 | 93.36 | 98.69 | 99.94 |
OA(%) | 76.05 | 81.84 | 83.41 | 85.98 | 87.85 | 91.72 | 88.84 | 95.05 |
AA(%) | 83.22 | 90.28 | 92.68 | 91.52 | 94.35 | 91.70 | 95.66 | 98.06 |
KA(%) | 73.52 | 79.85 | 81.62 | 84.27 | 86.5 | 90.77 | 87.61 | 94.49 |
Data Set | Without OGC | Without AGL | Without Graph Pooling | SSOGCN |
---|---|---|---|---|
Indian Pines | 90.12 | 91.38 | 86.65 | 92.51 |
Pavia University | 94.91 | 94.04 | 93.47 | 97.08 |
Salinas | 93.74 | 93.85 | 88.60 | 95.05 |
Methods | Time (s) | Params (K) |
---|---|---|
2D-CNN | 178.06 | 30.0 |
CNN-PPF | 61.86 | 44.82 |
GCN | 198.12 | 4.36 |
miniGCN | 776.86 | 28.70 |
MDGCN | 322.01 | 13.07 |
FuNet-C | 678.13 | 148.59 |
SSOGCN | 597.05 | 23.41 |
Methods | Time (s) | Params (K) |
---|---|---|
2D-CNN | 167.65 | 29.10 |
CNN-PPF | 149.18 | 31.50 |
GCN | 410.38 | 2.27 |
miniGCN | 2925.70 | 15.19 |
MDGCN | 447.87 | 6.81 |
FuNet-C | 2753.58 | 77.44 |
SSOGCN | 1471.03 | 12.22 |
Methods | Time (s) | Params (K) |
---|---|---|
2D-CNN | 283.94 | 30.00 |
CNN-PPF | 243.28 | 45.32 |
miniGCN | 1273.54 | 29.22 |
MDGCN | 458.48 | 13.31 |
FuNet-C | 5002.85 | 150.264 |
SSOGCN | 1183.93 | 23.96 |
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Zhang, M.; Luo, H.; Song, W.; Mei, H.; Su, C. Spectral-Spatial Offset Graph Convolutional Networks for Hyperspectral Image Classification. Remote Sens. 2021, 13, 4342. https://doi.org/10.3390/rs13214342
Zhang M, Luo H, Song W, Mei H, Su C. Spectral-Spatial Offset Graph Convolutional Networks for Hyperspectral Image Classification. Remote Sensing. 2021; 13(21):4342. https://doi.org/10.3390/rs13214342
Chicago/Turabian StyleZhang, Minghua, Hongling Luo, Wei Song, Haibin Mei, and Cheng Su. 2021. "Spectral-Spatial Offset Graph Convolutional Networks for Hyperspectral Image Classification" Remote Sensing 13, no. 21: 4342. https://doi.org/10.3390/rs13214342