Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning
Abstract
:1. Introduction
2. Densely Connected CNN for HSI Supervised Fine-grained Classification
2.1. Deep Learning and Convolutional Neural Network
2.2. Densely Connected CNN for HSI Supervised Fine-grained Classification
2.3. Dimensionality Reduction with DenseNet for HSI Fine-Grained Classification
2.4. CRF with DenseNet for HSI Fine-grained Classification
3. Generative Adversarial Networks for HSI Semi-Supervised Fine-grained Classification
3.1. Generative Adversarial Network (GAN)
3.2. Generative Adversarial Networks for HSI Semi-Supervised Fine-grained Classification
4. Experimental Results
4.1. Data Description and Environmental Setup
4.2. HSI Supervised Fine-Grained Classification
4.3. HSI Semi-Supervised Fine-grained Classification
4.4. Limited Training Samples and Classification Maps
4.5. Consuming Time
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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No. | Color | Number of Samples | Number of Training Samples (I) | Number of Training Samples (II) | Number of Test Samples (I) | Number of Test Samples (II) |
---|---|---|---|---|---|---|
1 | 17,195 | 423 | 436 | 1080 | 1042 | |
2 | 17,783 | 428 | 437 | 1043 | 1088 | |
3 | 158 | 3 | 4 | 6 | 11 | |
4 | 514 | 10 | 9 | 41 | 32 | |
5 | 2356 | 53 | 68 | 130 | 139 | |
6 | 12,404 | 319 | 300 | 677 | 732 | |
7 | 26,486 | 638 | 612 | 1600 | 1556 | |
8 | 39,678 | 985 | 947 | 2471 | 2400 | |
9 | 800 | 12 | 16 | 47 | 47 | |
10 | 1728 | 36 | 40 | 105 | 127 | |
11 | 1049 | 29 | 31 | 54 | 63 | |
12 | 5629 | 144 | 149 | 318 | 311 | |
13 | 8862 | 204 | 205 | 550 | 541 | |
14 | 4381 | 114 | 109 | 240 | 249 | |
15 | 1206 | 36 | 43 | 77 | 80 | |
16 | 5685 | 131 | 125 | 358 | 367 | |
17 | 114 | 6 | 2 | 10 | 4 | |
18 | 1147 | 29 | 27 | 56 | 78 | |
19 | 2331 | 51 | 71 | 132 | 148 | |
20 | 1128 | 30 | 25 | 53 | 61 | |
21 | 2185 | 49 | 53 | 124 | 138 | |
22 | 2258 | 52 | 51 | 144 | 140 | |
23 | 224 | 5 | 7 | 8 | 13 | |
24 | 1940 | 50 | 38 | 116 | 124 | |
25 | 1742 | 42 | 53 | 103 | 106 | |
26 | 335 | 7 | 10 | 14 | 18 | |
27 | 10,386 | 273 | 210 | 634 | 657 | |
28 | 102 | 4 | 2 | 6 | 8 | |
29 | 9391 | 220 | 223 | 553 | 597 | |
30 | 894 | 23 | 22 | 51 | 45 | |
31 | 1110 | 23 | 26 | 74 | 75 | |
32 | 5074 | 109 | 138 | 293 | 318 | |
33 | 2726 | 45 | 61 | 159 | 166 | |
34 | 11,802 | 249 | 266 | 677 | 707 | |
35 | 10,387 | 247 | 253 | 660 | 608 | |
36 | 2242 | 64 | 40 | 115 | 126 | |
37 | 543 | 20 | 6 | 28 | 23 | |
38 | 15,118 | 339 | 382 | 904 | 885 | |
39 | 2667 | 49 | 63 | 166 | 159 | |
40 | 1832 | 51 | 54 | 122 | 107 | |
41 | 8098 | 188 | 186 | 460 | 484 | |
42 | 4953 | 128 | 140 | 281 | 295 | |
43 | 2157 | 41 | 55 | 133 | 137 | |
44 | 2533 | 39 | 56 | 120 | 158 | |
45 | 929 | 26 | 28 | 49 | 55 | |
46 | 8731 | 221 | 215 | 535 | 498 | |
47 | 583 | 11 | 16 | 36 | 30 | |
48 | 3110 | 92 | 69 | 190 | 194 | |
49 | 580 | 12 | 14 | 39 | 36 | |
50 | 4979 | 118 | 118 | 281 | 293 | |
51 | 63,562 | 1519 | 1486 | 3861 | 3715 | |
52 | 144 | 3 | 3 | 16 | 9 | |
Total number | 333,951 | 8000 | 8000 | 20,000 | 20,000 |
Layers | Output Size | DenseNet |
---|---|---|
Convolution | 56 × 56 | 9 × 9 conv, stride = 1 |
Dense Block (1) | 56 × 56 | |
Transition Layer (1) | 56 × 56 | 1 × 1 conv |
28 × 28 | 2 × 2 average pool, stride = 2 | |
Dense Block (2) | 28 × 28 | |
Transition Layer (2) | 28 × 28 | 1 × 1 conv |
14 × 14 | 2 × 2 average pool, stride = 2 | |
Dense Block (3) | 14 × 14 | |
Transition Layer (3) | 14 × 14 | 1 × 1 conv |
7 × 7 | 2 × 2 average pool, stride = 2 | |
Dense Block (4) | 7 × 7 | |
Classification Layer | 1 × 1 | 7 × 7 global average pool |
fully-connected, softmax |
Number of Principal Components | 5 | 10 | 20 |
---|---|---|---|
OA (%) | 91.48 ± 0.42 | 92.35 ± 0.57 | 92.23 ± 0.53 |
AA (%) | 86.54 ± 1.76 | 87.89 ± 2.07 | 87.44 ± 1.65 |
K × 100 | 89.61 ± 0.54 | 91.30 ± 0.66 | 91.12 ± 0.79 |
Model | Learning Rate | Number of Epochs | Batch Size |
---|---|---|---|
Auto-Encoder | 0.001 | 150 | 5000 |
CNN | 0.001 | 150 | 200 |
DenseNet | 0.001 | 150 | 200 |
Semi-GAN | 0.0002 | 200 | 200 |
No | Color | SVM | EMP-RF | EMAP-RF | CNN | CNN-CRF | PCA-DenseNet | Auto-Encoder-DenseNet | DenseNet-CRF |
---|---|---|---|---|---|---|---|---|---|
1 | 52.61 ± 4.35 | 69.71 ± 3.24 | 60.09 ± 3.24 | 77.77 ± 2.01 | 85.54 ± 1.17 | 84.45 ± 0.49 | 84.93 ± 2.74 | 90.92 ± 0.02 | |
2 | 32.68 ± 6.34 | 71.96 ± 5.69 | 66.84 ± 5.69 | 71.40 ± 4.56 | 85.08 ± 2.43 | 94.59 ± 0.82 | 89.44 ± 1.78 | 89.96 ± 0.26 | |
3 | 18.142 ± 6.12 | 28.43 ± 0.38 | 23.05 ± 0.16 | 72.72 ± 0.04 | 80.22 ± 0.50 | 87.22 ± 9.24 | 81.11 ± 2.54 | 86.67 ± 0.15 | |
4 | 17.00 ± 9.22 | 20.26 ± 5.12 | 49.05 ± 5.12 | 86.06 ± 5.35 | 72.05 ± 3.45 | 87.53 ± 3.54 | 76.86 ± 3.87 | 91.47 ± 0.19 | |
5 | 18.91 ± 5.32 | 60.81 ± 5.76 | 67.48 ± 6.34 | 88.39 ± 1.93 | 83.61 ± 0.12 | 90.37 ± 1.94 | 94.13 ± 4.33 | 91.41 ± 0.05 | |
6 | 28.30 ± 0.03 | 67.44 ± 4.35 | 78.24 ± 4.56 | 82.33 ± 0.45 | 84.90 ± 0.08 | 93.08 ± 0.21 | 90.26 ± 3.60 | 90.79 ± 0.07 | |
7 | 33.86 ± 2.15 | 75.58 ± 0.34 | 72.65 ± 0.08 | 77.46 ± 2.55 | 80.06 ± 1.45 | 93.07 ± 0.25 | 94.41 ± 3.05 | 92.67 ± 0.43 | |
8 | 34.25 ± 0.03 | 82.49 ± 1.04 | 85.05 ± 0.16 | 84.84 ± 1.45 | 84.87 ± 0.05 | 95.82 ± 0.35 | 94.68 ± 2.24 | 93.23 ± 0.21 | |
9 | 9.56 ± 2.54 | 66.34 ± 3.02 | 59.15 ± 2.24 | 87.36 ± 4.45 | 97.45 ± 0.46 | 100.00 ± 0.00 | 100.00 ± 0.00 | 96.97 ± 0.19 | |
10 | 16.10 ± 0.03 | 44.74 ± 8.43 | 68.45 ± 3.46 | 80.82 ± 5.34 | 90.90 ± 2.45 | 96.37 ± 1.48 | 96.18 ± 1.45 | 90.47 ± 0.03 | |
11 | 19.77 ± 0.13 | 42.31 ± 1.16 | 76.37 ± 2.08 | 72.11 ± 0.17 | 89.77 ± 2.56 | 87.78 ± 2.46 | 81.26 ± 3.65 | 87.02 ± 0.09 | |
12 | 27.30 ± 6.45 | 65.46 ± 5.02 | 76.15 ± 4.35 | 75.91 ± 3.45 | 89.94 ± 1.56 | 95.05 ± 0.71 | 92.90 ± 3.10 | 93.38 ± 0.16 | |
13 | 37.87 ± 5.63 | 74.57 ± 4.53 | 81.86 ± 0.03 | 86.37 ± 3.56 | 91.46 ± 4.32 | 93.58 ± 0.91 | 94.63 ± 2.70 | 90.93 ± 0.15 | |
14 | 36.31 ± 9.46 | 68.15 ± 3.46 | 81.58 ± 3.13 | 80.96 ± 0.08 | 86.49 ± 0.12 | 91.52 ± 0.24 | 88.32 ± 3.92 | 92.03 ± 0.23 | |
15 | 35.84 ± 1.98 | 58.33 ± 3.45 | 66.70 ± 0.21 | 77.63 ± 2.46 | 89.23 ± 1.56 | 90.91 ± 0.45 | 96.36 ± 2.53 | 96.42 ± 0.15 | |
16 | 57.14 ± 2.54 | 70.01 ± 0.42 | 85.65 ± 0.14 | 81.69 ± 0.43 | 83.57 ± 0.20 | 93.97 ± 0.96 | 96.97 ± 1.24 | 94.54 ± 0.06 | |
17 | 39.25 ± 12.53 | 55.56 ± 9.43 | 43.25 ± 0.15 | 73.33 ± 9.30 | 65.42 ± 0.14 | 3.82 ± 5.25 | 76.61 ± 2.14 | 91.37 ± 0.42 | |
18 | 23.19 ± 9.07 | 25.39 ± 7.45 | 34.78 ± 0.25 | 47.61 ± 0.15 | 78.72 ± 0.15 | 65.71 ± 5.81 | 69.27 ± 3.73 | 80.51 ± 0.32 | |
19 | 52.65 ± 7.53 | 66.62 ± 6.26 | 78.95 ± 2.43 | 85.81 ± 2.45 | 85.87 ± 3.56 | 87.67 ± 1.55 | 91.17 ± 2.96 | 91.28 ± 0.06 | |
20 | 48.63 ± 7.15 | 57.06 ± 4.56 | 55.58 ± 3.23 | 73.91 ± 3.45 | 90.47 ± 3.46 | 71.20 ± 3.96 | 92.23 ± 1.24 | 86.74 ± 0.04 | |
21 | 63.14 ± 0.06 | 69.24 ± 0.14 | 82.85 ± 0.02 | 62.48 ± 0.21 | 79.62 ± 2.35 | 90.01 ± 1.87 | 95.50 ± 2.37 | 92.83 ± 0.12 | |
22 | 68.35 ± 2.89 | 68.14 ± 1.35 | 83.25 ± 2.39 | 70.94 ± 0.98 | 79.36 ± 0.24 | 90.98 ± 1.55 | 91.07 ± 3.10 | 89.86 ± 0.14 | |
23 | 61.51 ± 0.24 | 83.33 ± 0.16 | 72.57 ± 0.03 | 92.85 ± 0.01 | 89.18 ± 0.46 | 79.36 ± 3.19 | 28.34 ± 2.45 | 100.00 ± 0.000 | |
24 | 39.94 ± 1.54 | 51.69 ± 0.68 | 71.34 ± 0.35 | 65.74 ± 0.15 | 89.20 ± 0.23 | 71.77 ± 3.64 | 79.79 ± 0.01 | 86.88 ± 0.16 | |
25 | 44.63 ± 7.54 | 58.35 ± 5.45 | 63.02 ± 5.01 | 76.54 ± 2.45 | 82.74 ± 0.34 | 77.45 ± 7.22 | 69.13 ± 2.56 | 75.67 ± 0.24 | |
26 | 33.24 ± 0.05 | 14.14 ± 0.04 | 37.35 ± 0.13 | 35.05 ± 0.25 | 72.35 ± 0.16 | 58.40 ± 9.37 | 54.13 ± 2.72 | 81.76 ± 0.32 | |
27 | 65.98 ± 4.33 | 76.01 ± 2.45 | 83.55 ± 3.09 | 90.36 ± 3.01 | 78.37 ± 0.26 | 91.13 ± 0.89 | 91.46 ± 1.50 | 91.92 ± 0.04 | |
28 | 28.33 ± 0.14 | 25.22 ± 4.64 | 23.79 ± 13.54 | 20.49 ± 10.24 | 26.25 ± 0.19 | 70.83 ± 1.31 | 70.79 ± 3.18 | 87.78 ± 0.42 | |
29 | 43.25 ± 3.46 | 69.28 ± 10.45 | 76.58 ± 0.19 | 73.01 ± 0.87 | 87.90 ± 1.56 | 89.48 ± 0.36 | 90.51 ± 1.07 | 90.73 ± 0.13 | |
30 | 16.56 ± 2.06 | 62.32 ± 0.11 | 69.36 ± 0.04 | 46.69 ± 2.56 | 86.06 ± 0.12 | 90.44 ± 0.97 | 92.20 ± 0.13 | 94.12 ± 0.04 | |
31 | 15.54 ± 6.74 | 70.62 ± 5.39 | 62.95 ± 6.43 | 89.41 ± 4.67 | 49.29 ± 1.45 | 84.97 ± 2.59 | 86.90 ± 1.08 | 91.91 ± 0.20 | |
32 | 29.97 ± 7.46 | 57.49 ± 6.34 | 66.75 ± 4.67 | 79.16 ± 0.36 | 90.12 ± 2.45 | 90.47 ± 0.14 | 92.92 ± 0.45 | 90.82 ± 0.05 | |
33 | 23.64 ± 6.42 | 68.59 ± 4.07 | 77.39 ± 1.56 | 91.51 ± 4.57 | 82.71 ± 0.25 | 90.52 ± 2.59 | 89.55 ± 2.85 | 88.65 ± 0.06 | |
34 | 35.00 ± 4.56 | 74.15 ± 8.31 | 76.85 ± 6.46 | 82.73 ± 5.67 | 86.54 ± 0.43 | 92.01 ± 0.14 | 91.24 ± 1.27 | 90.90 ± 0.23 | |
35 | 24.83 ± 5.03 | 68.78 ± 4.04 | 72.75 ± 6.23 | 87.37 ± 4.92 | 82.82 ± 0.51 | 95.29 ± 0.52 | 95.35 ± 0.15 | 91.35 ± 0.11 | |
36 | 47.15 ± 0.05 | 53.33 ± 0.06 | 74.55 ± 0.14 | 91.26 ± 0.45 | 86.71 ± 1.56 | 94.15 ± 1.80 | 85.65 ± 0.07 | 96.63 ± 0.39 | |
37 | 26.99 ± 5.43 | 54.77 ± 4.56 | 51.45 ± 6.43 | 81.42 ± 1.45 | 90.20 ± 0.32 | 92.47 ± 0.02 | 94.01 ± 0.04 | 98.96 ± 0.06 | |
38 | 40.86 ± 6.42 | 72.75 ± 3.64 | 69.75 ± 2.34 | 74.94 ± 2.45 | 89.96 ± 0.12 | 90.70 ± 0.49 | 92.95 ± 0.01 | 86.10 ± 0.24 | |
39 | 54.55 ± 4.56 | 68.54 ± 2.54 | 61.24 ± 2.45 | 88.53 ± 1.46 | 73.64 ± 1.57 | 90.18 ± 0.37 | 91.61 ± 2.64 | 89.91 ± 0.08 | |
40 | 50.53 ± 2.03 | 70.01 ± 0.74 | 62.99 ± 3.45 | 86.42 ± 1.90 | 82.37 ± 0.31 | 94.70 ± 1.63 | 97.26 ± 3.14 | 85.82 ± 0.25 | |
41 | 49.15 ± 6.64 | 71.46 ± 3.45 | 86.25 ± 0.13 | 89.72 ± 3.57 | 82.24 ± 0.34 | 96.32 ± 0.94 | 94.33 ± 0.13 | 90.99 ± 0.06 | |
42 | 31.88 ± 4.43 | 65.17 ± 2.15 | 78.95 ± 1.45 | 84.82 ± 0.03 | 84.94 ± 0,56 | 97.46 ± 0.37 | 95.84 ± 3.25 | 91.01 ± 0.05 | |
43 | 43.98 ± 9.46 | 65.55 ± 5.64 | 78.84 ± 0.09 | 85.49 ± 4.67 | 82.36 ± 0.45 | 86.02 ± 0.81 | 92.61 ± 2.14 | 94.90 ± 0.12 | |
44 | 41.55 ± 3.56 | 82.38 ± 0.04 | 60.65 ± 0.06 | 85.12 ± 2.54 | 91.44 ± 0.10 | 88.62 ± 1.83 | 96.16 ± 2.06 | 97.78 ± 0.10 | |
45 | 47.40 ± 3.64 | 59.18 ± 0.04 | 81.02 ± 3.42 | 72.66 ± 1.36 | 79.33 ± 0.42 | 69.66 ± 6.45 | 88.13 ± 1.02 | 78.03 ± 0.51 | |
46 | 61.53 ± 0.04 | 71.59 ± 0.56 | 87.55 ± 3.46 | 89.08 ± 0.67 | 82.66 ± 0.32 | 92.61 ± 0.26 | 91.35 ± 0.45 | 93.51 ± 0.05 | |
47 | 61.05 ± 1.97 | 40.18 ± 0.04 | 85.35 ± 0.32 | 86.04 ± 0.14 | 81.18 ± 0.24 | 94.75 ± 1.51 | 79.89 ± 4.88 | 91.30 ± 0.24 | |
48 | 69.26 ± 4.56 | 99.49 ± 3.53 | 81.97 ± 0.36 | 95.06 ± 2.56 | 88.41 ± 0.42 | 96.40 ± 1.19 | 98.26 ± 0.88 | 95.08 ± 0.04 | |
49 | 53.92 ± 0.14 | 79.82 ± 0.08 | 79.76 ± 0.57 | 78.37 ± 0.17 | 84.47 ± 3.46 | 79.32 ± 4.19 | 100.00 ± 0.00 | 95.99 ± 0.22 | |
50 | 78.48 ± 4.56 | 82.81 ± 4.34 | 65.86 ± 2.44 | 92.16 ± 2.45 | 83.47 ± 2.45 | 90.40 ± 2.49 | 93.02 ± 0.52 | 91.70 ± 0.34 | |
51 | 72.48 ± 4.64 | 96.95 ± 1.45 | 81.44 ± 0.97 | 96.26 ± 1.45 | 84.45 ± 0.46 | 97.96 ± 0.19 | 97.35 ± 0.21 | 96.58 ± 0.10 | |
52 | 83.28 ± 1.45 | 61.71 ± 3.45 | 43.25 ± 0.68 | 77.50 ± 0.14 | 85.01 ± 0.21 | 100.00 ± 0.00 | 79.58 ± 3.43 | 91.67 ± 0.12 | |
OA (%) | 52.24 ± 0.43 | 76.36 ± 0.24 | 77.95 ± 0.57 | 85.47 ± 0.28 | 86.08 ± 0.21 | 92.08 ± 0.87 | 92.35 ± 0.57 | 93.07 ± 0.18 | |
AA (%) | 42.55 ± 0.16 | 64.79 ± 0.39 | 66.05 ± 0.12 | 78.29 ± 0.23 | 82.53 ± 0.86 | 87.01 ± 1.25 | 87.89 ± 2.07 | 91.45 ± 0.31 | |
K × 100 | 47.15 ± 0.20 | 72.24 ± 0.35 | 73.72 ± 0.66 | 82.86 ± 0.13 | 85.64 ± 0.27 | 90.26 ± 0.94 | 91.30 ± 0.66 | 92.76 ± 0.19 |
NO. | Color | CNN | PCA-CNN | Auto-Encoder-CNN | DenseNet | PCA-DenseNet | DenseNet-1 × 1 Conv | Auto-Encoder-DenseNet |
---|---|---|---|---|---|---|---|---|
1 | 77.77 ± 2.01 | 77.69 ± 1.09 | 86.18 ± 0.45 | 83.32 ± 1.25 | 84.45 ± 0.49 | 82.45 ± 1.59 | 84.93 ± 2.74 | |
2 | 71.40 ± 4.56 | 84.91 ± 0.42 | 77.87 ± 0.78 | 93.16 ± 0.43 | 94.59 ± 0.82 | 93.28 ± 1.26 | 89.44 ± 1.78 | |
3 | 72.72 ± 0.04 | 72.40 ± 7.09 | 94.56 ± 0.23 | 68.58 ± 15.14 | 87.22 ± 9.24 | 66.59 ± 2.15 | 81.11 ± 2.54 | |
4 | 86.06 ± 5.35 | 90.87 ± 5.19 | 80.57 ± 2.53 | 67.79 ± 5.82 | 87.53 ± 3.54 | 75.86 ± 3.19 | 76.86 ± 3.87 | |
5 | 88.39 ± 1.93 | 94.02 ± 0.15 | 78.33 ± 0.66 | 88.62 ± 1.39 | 90.37 ± 1.94 | 81.19 ± 2.15 | 94.13 ± 4.33 | |
6 | 82.33 ± 0.45 | 85.16 ± 0.60 | 85.61 ± 0.60 | 91.65 ± 2.05 | 93.08 ± 0.21 | 92.45 ± 0.43 | 90.26 ± 3.60 | |
7 | 77.46 ± 2.55 | 89.57 ± 1.12 | 87.82 ± 0.16 | 93.63 ± 0.43 | 93.07 ± 0.25 | 93.79 ± 0.21 | 94.41 ± 3.05 | |
8 | 84.84 ± 1.45 | 93.75 ± 0.66 | 94.47 ± 0.66 | 95.61 ± 0.42 | 95.82 ± 0.35 | 95.69 ± 0.19 | 94.68 ± 2.24 | |
9 | 87.36 ± 4.45 | 100.00 ± 0.00 | 91.00 ± 0.12 | 96.98 ± 1.69 | 100.00 ± 0.00 | 96.46 ± 1.68 | 100.00 ± 0.00 | |
10 | 80.82 ± 5.34 | 95.11 ± 2.25 | 93.11 ± 0.79 | 95.79 ± 0.89 | 96.37 ± 1.48 | 90.36 ± 0.09 | 96.18 ± 1.45 | |
11 | 72.11 ± 0.17 | 75.95 ± 1.25 | 90.76 ± 1.25 | 94.58 ± 0.80 | 87.78 ± 2.46 | 88.04 ± 0.16 | 81.26 ± 3.65 | |
12 | 75.91 ± 3.45 | 85.67 ± 1.54 | 88.00 ± 0.76 | 93.59 ± 0.72 | 95.05 ± 0.71 | 93.18 ± 0.15 | 92.90 ± 3.10 | |
13 | 86.37 ± 3.56 | 94.41 ± 0.73 | 94.46 ± 0.11 | 97.06 ± 0.32 | 93.58 ± 0.91 | 92.43 ± 0.23 | 94.63 ± 2.70 | |
14 | 80.96 ± 0.08 | 88.89 ± 1.56 | 91.69 ± 0.02 | 88.36 ± 1.50 | 91.52 ± 0.24 | 94.73 ± 0.15 | 88.32 ± 3.92 | |
15 | 77.63 ± 2.46 | 95.19 ± 1.44 | 98.58 ± 0.02 | 85.33 ± 2.11 | 90.91 ± 0.45 | 96.47 ± 3.58 | 96.41 ± 2.53 | |
16 | 81.69 ± 0.43 | 98.49 ± 0.38 | 97.11 ± 0.69 | 96.71 ± 0.68 | 93.97 ± 0.96 | 95.96 ± 0.42 | 96.97 ± 1.24 | |
17 | 73.33 ± 9.30 | 60.02 ± 13.94 | 40.00 ± 13.34 | 32.75 ± 10.25 | 3.82 ± 5.25 | 98.67 ± 0.32 | 76.61 ± 2.14 | |
18 | 47.61 ± 0.15 | 63.06 ± 2.51 | 64.76 ± 2.54 | 71.55 ± 6.27 | 65.71 ± 5.81 | 75.26 ± 4.23 | 69.27 ± 3.73 | |
19 | 85.81 ± 2.45 | 93.22 ± 1.61 | 72.99 ± 2.03 | 83.52 ± 1.97 | 87.67 ± 1.55 | 94.18 ± 2.41 | 91.28 ± 2.96 | |
20 | 73.91 ± 3.45 | 52.05 ± 3.62 | 71.06 ± 0.11 | 73.99 ± 3.65 | 71.20 ± 3.96 | 79.58 ± 1.12 | 92.23 ± 1.24 | |
21 | 62.48 ± 0.21 | 93.11 ± 1.14 | 83.98 ± 0.22 | 71.94 ± 2.36 | 90.01 ± 1.87 | 90.14 ± 0.14 | 95.50 ± 2.37 | |
22 | 70.94 ± 0.98 | 86.22 ± 1.86 | 83.45 ± 0.45 | 93.12 ± 1.11 | 90.98 ± 1.55 | 83.54 ± 2.59 | 91.07 ± 3.10 | |
23 | 92.85 ± 0.01 | 77.19 ± 11.41 | 100.00 ± 0.00 | 96.29 ± 4.29 | 79.36 ± 3.19 | 90.91 ± 0.16 | 28.34 ± 2.45 | |
24 | 65.74 ± 0.15 | 78.47 ± 4.27 | 69.81 ± 2.27 | 70.66 ± 2.83 | 71.77 ± 3.64 | 67.53 ± 0.24 | 79.79 ± 0.01 | |
25 | 76.54 ± 2.45 | 75.59 ± 1.24 | 73.32 ± 1.89 | 70.16 ± 1.70 | 77.45 ± 7.22 | 71.69 ± 0.32 | 69.13 ± 2.56 | |
26 | 35.05 ± 0.25 | 54.55 ± 5.86 | 64.29 ± 4.77 | 42.20 ± 7.19 | 58.40 ± 9.37 | 66.25 ± 0.04 | 54.13 ± 2.72 | |
27 | 90.36 ± 3.01 | 93.01 ± 0.8 | 92.01 ± 0.17 | 93.55 ± 0.98 | 91.13 ± 0.89 | 93.59 ± 0.42 | 91.46 ± 1.50 | |
28 | 20.49 ± 10.24 | 20.42 ± 16.4 | 82.50 ± 1.67 | 58.45 ± 6.91 | 70.83 ± 1.31 | 13.25 ± 8.59 | 70.79 ± 3.18 | |
29 | 73.01 ± 0.87 | 83.14 ± 1.61 | 84.35 ± 0.87 | 83.44 ± 1.18 | 89.48 ± 0.36 | 83.25 ± 0.13 | 90.51 ± 1.07 | |
30 | 46.69 ± 2.56 | 75.73 ± 2.12 | 92.82 ± 1.67 | 86.65 ± 6.32 | 90.44 ± 0.97 | 71.25 ± 0.04 | 92.20 ± 0.13 | |
31 | 89.41 ± 4.67 | 94.56 ± 1.60 | 74.56 ± 1.76 | 81.53 ± 4.05 | 84.97 ± 2.59 | 75.24 ± 0.20 | 86.90 ± 1.08 | |
32 | 79.16 ± 0.36 | 87.03 ± 0.67 | 75.53 ± 0.67 | 95.10 ± 0.73 | 90.47 ± 0.14 | 83.02 ± 0.96 | 92.92 ± 0.45 | |
33 | 91.51 ± 4.57 | 86.67 ± 1.16 | 89.88 ± 1.28 | 85.94 ± 4.12 | 90.52 ± 2.59 | 80.15 ± 0.06 | 89.55 ± 2.85 | |
34 | 82.73 ± 5.67 | 85.51 ± 0.67 | 91.02 ± 0.67 | 85.60 ± 1.22 | 92.01 ± 0.14 | 87.25 ± 0.23 | 91.24 ± 1.27 | |
35 | 87.37 ± 4.92 | 85.16 ± 1.20 | 79.61 ± 0.30 | 93.39 ± 0.28 | 95.29 ± 0.52 | 76.59 ± 0.11 | 95.35 ± 0.15 | |
36 | 91.26 ± 0.45 | 87.39 ± 1.65 | 91.02 ± 1.45 | 91.42 ± 1.15 | 94.15 ± 1.80 | 91.03 ± 0.39 | 85.65 ± 0.07 | |
37 | 81.42 ± 1.45 | 94.80 ± 4.57 | 97.58 ± 1.62 | 95.22 ± 3.51 | 92.47 ± 0.02 | 95.13 ± 0.06 | 94.01 ± 0.04 | |
38 | 74.94 ± 2.45 | 86.04 ± 1.51 | 85.93 ± 0.30 | 88.88 ± 0.37 | 90.70 ± 0.49 | 85.69 ± 0.24 | 92.95 ± 0.01 | |
39 | 88.53 ± 1.46 | 88.68 ± 1.32 | 91.07 ± 1.32 | 92.27 ± 1.30 | 90.18 ± 0.37 | 83.06 ± 0.08 | 91.61 ± 2.64 | |
40 | 86.42 ± 1.90 | 84.69 ± 0.87 | 89.06 ± 0.34 | 92.10 ± 0.93 | 94.70 ± 1.63 | 72.28 ± 0.25 | 97.26 ± 3.14 | |
41 | 89.72 ± 3.57 | 91.47 ± 0.48 | 89.86 ± 1.06 | 91.44 ± 0.31 | 96.32 ± 0.94 | 93.27 ± 1.04 | 94.33 ± 0.13 | |
42 | 84.82 ± 0.03 | 90.37 ± 1.97 | 92.32 ± 0.09 | 95.59 ± 0.89 | 97.46 ± 0.37 | 95.27 ± 2.45 | 95.84 ± 3.25 | |
43 | 85.49 ± 4.67 | 89.61 ± 2.24 | 87.67 ± 2.21 | 85.54 ± 1.72 | 86.02 ± 0.81 | 94.39 ± 0.12 | 92.61 ± 2.14 | |
44 | 85.12 ± 2.54 | 97.45 ± 0.17 | 92.50 ± 0.46 | 97.99 ± 0.09 | 88.62 ± 1.83 | 95.52 ± 0.10 | 96.16 ± 2.06 | |
45 | 72.66 ± 1.36 | 89.25 ± 0.46 | 86.44 ± 0.88 | 79.09 ± 2.45 | 69.66 ± 6.45 | 92.59 ± 0.51 | 88.13 ± 1.02 | |
46 | 89.08 ± 0.67 | 91.75 ± 0.88 | 87.45 ± 0.49 | 92.50 ± 0.35 | 92.61 ± 0.26 | 92.10 ± 0.05 | 91.35 ± 0.45 | |
47 | 86.04 ± 0.14 | 91.07 ± 0.74 | 94.48 ± 4.03 | 96.34 ± 2.51 | 94.75 ± 1.51 | 83.32 ± 0.24 | 79.89 ± 4.88 | |
48 | 95.06 ± 2.56 | 98.08 ± 0.29 | 98.89 ± 0.05 | 98.91 ± 0.48 | 96.40 ± 1.19 | 94.09 ± 3.47 | 98.26 ± 0.88 | |
49 | 78.37 ± 0.17 | 90.01 ± 1.45 | 92.00 ± 0.13 | 96.09 ± 2.94 | 79.32 ± 4.19 | 97.26 ± 0.22 | 100.00 ± 0.00 | |
50 | 92.16 ± 2.45 | 89.11 ± 0.87 | 92.13 ± 0.10 | 87.92 ± 1.91 | 90.40 ± 2.49 | 92.14 ± 0.34 | 93.02 ± 0.52 | |
51 | 96.26 ± 1.45 | 98.22 ± 0.14 | 98.22 ± 0.14 | 98.62 ± 0.03 | 97.96 ± 0.19 | 96.58 ± 1.47 | 97.35 ± 0.21 | |
52 | 77.50 ± 0.14 | 75.68 ± 4.23 | 100.00 ± 0.00 | 100.00 ± 0.00 | 100.00 ± 0.00 | 95.67 ± 1.89 | 79.58 ± 3.43 | |
OA (%) | 85.47 ± 0.28 | 87.95 ± 0.18 | 88.08 ± 0.29 | 90.84 ± 1.04 | 92.08 ± 0.87 | 91.17 ± 0.67 | 92.35 ± 0.57 | |
AA (%) | 78.29 ± 0.23 | 84.43 ± 0.41 | 86.13 ± 0.59 | 85.98 ± 2.38 | 87.01 ± 1.25 | 86.21 ± 1.14 | 87.89 ± 2.07 | |
K × 100 | 82.86 ± 0.13 | 86.44 ± 0.39 | 86.62 ± 0.11 | 90.27 ± 1.42 | 90.26 ± 0.94 | 90.54 ± 0.24 | 91.30 ± 0.66 | |
Train Time (min.) | 457.25 | 40.12 | 143.20 | 785.64 | 188.16 | 544.63 | 284.71 | |
Test Time (min.) | 23.55 | 0.51 | 0.58 | 28.64 | 2.05 | 7.13 | 2.15 |
DenseNet (1, 2, 3, 4) | DenseNet (2, 4, 6, 8) | DenseNet (3, 5, 7, 9) | |
---|---|---|---|
OA (%) | 90.97 ± 0.40 | 92.08 ± 0.87 | 91.12 ± 0.51 |
AA (%) | 85.63 ± 1.09 | 87.01 ± 1.25 | 86.21 ± 0.90 |
K × 100 | 89.75 ± 0.43 | 90.26 ± 0.94 | 89.13 ± 0.62 |
Train Time (min.) | 117.74 | 188.16 | 224.86 |
Test Time (min.) | 1.41 | 2.05 | 2.62 |
No. | Name | TSVM | Label Propagation | Semi-GAN |
---|---|---|---|---|
1 | Buildings | 54.48 ± 2.25 | 53.02 ± 2.32 | 82.15 ± 1.45 |
2 | Corn | 29.98 ± 0.43 | 39.96 ± 1.47 | 86.92 ± 2.29 |
3 | Corn? | 21.42 ± 8.56 | 22.02 ± 6.16 | 91.67 ± 0.59 |
4 | Corn-EW | 22.58 ± 6.82 | 44.67 ± 2.14 | 100.00 ± 0.00 |
5 | Corn-NS | 23.73 ± 2.39 | 33.33 ± 4.52 | 95.24 ± 0.45 |
6 | Corn-CleanTill | 25.58 ± 1.05 | 37.67 ± 2.59 | 96.40 ± 0.03 |
7 | Corn-CleanTill-EW | 44.21 ± 0.94 | 53.53 ± 3.62 | 96.38 ± 0.57 |
8 | Corn-CleanTill-NS | 66.54 ± 1.69 | 66.64 ± 0.02 | 91.15 ± 1.43 |
9 | Corn-CleanTill-NS-Irrigated | 6.12 ± 12.34 | 13.13 ± 9.54 | 83.44 ± 2.46 |
10 | Corn-CleanTill-NS? | 18.68 ± 1.34 | 26.24 ± 3.48 | 84.97 ± 2.16 |
11 | Corn-MinTill | 15.25 ± 0.13 | 32.36 ± 2.49 | 100.00 ± 0.00 |
12 | Corn-MinTill-EW | 27.65 ± 0.68 | 37.56 ± 3.49 | 81.23 ± 0.13 |
13 | Corn-MinTill-NS | 39.27 ± 0.72 | 49.34 ± 4.52 | 90.54 ± 0.01 |
14 | Corn-NoTill | 36.74 ± 1.97 | 48.21 ± 1.49 | 95.73 ± 0.45 |
15 | Corn-NoTill-EW | 37.36 ± 2.43 | 34.63 ± 3.21 | 91.26 ± 0.98 |
16 | Corn-NoTill-NS | 48.54 ± 0.68 | 63.52 ± 1.78 | 83.33 ± 4.07 |
17 | Fescue | 77.78 ± 5.62 | 79.00 ± 0.96 | 90.43 ± 1.45 |
18 | Grass | 20.69 ± 6.27 | 58.02 ± 4.78 | 100.00 ± 0.00 |
19 | Grass/Tress | 72.67 ± 1.97 | 74.67 ± 1.78 | 90.30 ± 2.45 |
20 | Hay | 46.38 ± 3.65 | 55.67 ± 0.79 | 66.67 ± 0.15 |
21 | Hay? | 69.23 ± 2.36 | 79.00 ± 6.23 | 90.91 ± 0.63 |
22 | Hay-Alfalfa | 77.27 ± 1.12 | 82.33 ± 0.25 | 85.71 ± 0.57 |
23 | Lake | 54.54 ± 3.29 | 63.14 ± 0.89 | 100.00 ± 0.00 |
24 | NotCropped | 38.18 ± 1.41 | 56.01 ± 0.21 | 66.91 ± 0.34 |
25 | Oats | 48.45 ± 4.29 | 43.94 ± 0.78 | 91.91 ± 4.57 |
26 | Oats? | 7.69 ± 2.83 | 4.34 ± 4.21 | 57.62 ± 0.45 |
27 | Pasture | 64.98 ± 1.07 | 75.00 ± 0.12 | 92.26 ± 2.35 |
28 | pond | 25.12 ± 7.21 | 40.14 ± 3.69 | 67.54 ± 0.25 |
29 | Soybeans | 40.25 ± 0.98 | 46.24 ± 0.36 | 98.81 ± 0.42 |
30 | Soybeans? | 20.23 ± 3.25 | 11.23 ± 4.56 | 80.79 ± 0.94 |
31 | Soybeans-NS | 19.64 ± 4.01 | 34.45 ± 7.52 | 89.48 ± 2.42 |
32 | Soybeans-CleanTill | 31.16 ± 2.73 | 37.54 ± 0.14 | 95.16 ± 0.35 |
33 | Soybeans-CleanTill? | 22.59 ± 1.22 | 32.45 ± 4.96 | 80.95 ± 1.47 |
34 | Soybeans-CleanTill-EW | 36.84 ± 0.85 | 44.33 ± 0.17 | 92.85 ± 0.45 |
35 | Soybeans-CleanTill-NS | 27.55 ± 1.15 | 27.24 ± 0.02 | 94.36 ± 0.56 |
36 | Soybeans-CleanTill-Drilled | 52.50 ± 3.50 | 46.00 ± 2.14 | 84.48 ± 0.35 |
37 | Soybeans-CleanTill-Weedy | 28.99 ± 0.98 | 23.67 ± 3.41 | 93.33 ± 1.27 |
38 | Soybeans- Drilled | 40.02 ± 1.30 | 52.32 ± 0.79 | 98.49 ± 1.45 |
39 | Soybeans-MinTill | 55.81 ± 0.93 | 65.00 ± 2.14 | 89.04 ± 0.92 |
40 | Soybeans-MinTill-EW | 53.84 ± 0.31 | 63.46 ± 3.78 | 99.01 ± 0.17 |
41 | Soybeans-MinTill-Drilled | 50.10 ± 0.89 | 50.36 ± 4.69 | 91.26 ± 2.45 |
42 | Soybeans-MinTill-NS | 31.10 ± 1.72 | 37.33 ± 0.05 | 100.00 ± 0.00 |
43 | Soybeans-NOTill | 49.62 ± 0.09 | 45.10 ± 0.03 | 86.45 ± 4.14 |
44 | Soybeans-NoTill-EW | 44.38 ± 2.45 | 47.33 ± 0.16 | 100.00 ± 0.00 |
45 | Soybeans-NoTill-NS | 16.20 ± 0.35 | 27.44 ± 0.79 | 98.16 ± 0.25 |
46 | Soybeans-NoTill-Drilled | 59.09 ± 2.51 | 70.00 ± 6.35 | 95.48 ± 0.03 |
47 | Swampy Area | 78.57 ± 0.47 | 94.38 ± 1.45 | 72.37 ± 0.11 |
48 | River | 98.94 ± 2.94 | 99.37 ± 1.79 | 88.12 ± 3.25 |
49 | Trees? | 48.57 ± 3.94 | 59.40 ± 0.17 | 94.78 ± 2.45 |
50 | Wheat | 78.87 ± 4.58 | 86.60 ± 4.23 | 100.00 ± 0.00 |
51 | Woods | 92.03 ± 1.94 | 90.23 ± 7.45 | 84.32 ± 0.01 |
52 | Woods? | 90.90 ± 1.59 | 91.96 ± 6.32 | 82.50 ± 3.45 |
OA (%) | 55.49 ± 0.87 | 60.02 ± 0.21 | 94.02 ± 1.43 | |
AA (%) | 43.02 ± 1.04 | 51.20 ± 0.43 | 90.11 ± 2.07 | |
K × 100 | 50.38 ± 0.75 | 56.86 ± 0.24 | 92.76 ± 1.03 |
N | 4000 | 6000 | 8000 | |
---|---|---|---|---|
Methods | ||||
Semi-GAN | OA (%) | 89.45 ± 3.02 | 92.53 ± 2.98 | 94.02 ± 2.43 |
AA (%) | 83.41 ± 3.87 | 88.14 ± 3.07 | 90.11 ± 2.57 | |
K × 100 | 87.76 ± 2.75 | 91.75 ± 2.09 | 92.76 ± 1.56 |
Methods | Running Time (min.) | |
---|---|---|
SVM | Training | 2650.91 |
Test | 32.92 | |
CNN | Training | 457.25 |
Test | 23.55 | |
Semi-GAN | Training | 1053.42 |
Test | 0.81 | |
PCA-DenseNet | Training | 188.16 |
Test | 2.05 | |
Auto-Encoder-DenseNet | Training | 284.71 |
Test | 2.15 |
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Chen, Y.; Huang, L.; Zhu, L.; Yokoya, N.; Jia, X. Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning. Remote Sens. 2019, 11, 2690. https://doi.org/10.3390/rs11222690
Chen Y, Huang L, Zhu L, Yokoya N, Jia X. Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning. Remote Sensing. 2019; 11(22):2690. https://doi.org/10.3390/rs11222690
Chicago/Turabian StyleChen, Yushi, Lingbo Huang, Lin Zhu, Naoto Yokoya, and Xiuping Jia. 2019. "Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning" Remote Sensing 11, no. 22: 2690. https://doi.org/10.3390/rs11222690
APA StyleChen, Y., Huang, L., Zhu, L., Yokoya, N., & Jia, X. (2019). Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning. Remote Sensing, 11(22), 2690. https://doi.org/10.3390/rs11222690