Author Affiliations
1 Chang Guang Satellite Technology Co.Ltd., Key Laboratory of Satellite Remote Sensing Application Technology of Jilin Province, Changchun, Jilin 130000, China2 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China3 Jilin Provincial Land Survey & Planning Institute, Changchun, Jilin 130061, China;show less
Fig. 1. Flow chart of experimental method
Fig. 2. Hyperspectral image classification framework based on spatial-spectral 3D-CNN model
Fig. 3. Illustration of hyperspectral pixel adjacent sparse matrix. (a) Pixel adjacent sparse matrix; (b) image four-neighbor model (K=4); (c) image eight-neighbor model (K=8)
Fig. 4. Classification results comparison of different algorithms on Indian Pines dataset (16 categories). (a) Pseud color image; (b) true image; (c) LDM-FL; (d) 2D-CNN; (e) 3D-CNN; (f) 3D-CNN-CRF
Fig. 5. Classification results comparison of different algorithms on Pavia University dataset (9 categories). (a) Pseud color image; (b) true image; (c) LDM-FL; (d) 2D-CNN; (e) 3D-CNN; (f) 3D-CNN-CRF
Fig. 6. Classification and unknown region generalization result on Pavia Center dataset (9 categories). (a) Pseud color image; (b) true image;(c) 3D-CNN-CRF(feature dimension: 34); (d) 3D-CNN-CRF (feature dimension: 68); (e) 3D-CNN-CRF (feature dimension: 102); (f) unknown region result
Fig. 7. Influence of spectral features with different dimensions on classification accuracy
Algorithm | Parameter |
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kc1 | sc1 | pc1 | kc2 | sc2 | pc2 | fc1 | fc2 | lr | kcrf | λcrf |
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2D-CNN | 3×3 | [1,1] | 2×2 | 3×3 | [1,2] | 2×2 | 400 | 200 | 0.005 | - | - | 3D-CNN | 3×3×6 | [1,1,4] | 3×3×3 | 3×3×6 | [1,1,2] | 3×3×3 | 400 | 200 | 0.005 | - | - | 3D-CNN-CRF | 3×3×6 | [1,1,4] | 3×3×3 | 3×3×6 | [1,1,2] | 3×3×3 | 400 | 200 | 0.005 | 8 | 0.375 |
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Table 1. Related parameter settings of different algorithms
Accuracy indicator | Category | Algorithm |
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LDM-FL | p-CNN* | 2D-CNN | 3D-CNN | 3D-CNN-CRF |
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| C1 | 97.87 | 83.39 | 100 | 100 | 100 | | C2 | 89.67 | 85.41 | 96.37 | 89.60 | 97.25 | | C3 | 89.64 | 82.76 | 78.52 | 96.04 | 99.88 | | C4 | 93.60 | 82.14 | 89.43 | 87.40 | 95.16 | | C5 | 96.47 | 95.24 | 93.28 | 97.20 | 99.14 | | C6 | 100 | 99.25 | 96.24 | 97.96 | 99.05 | | C7 | 77.78 | 91.47 | 95.45 | 87.50 | 100 | CA | C8 | 100 | 99.81 | 100 | 100 | 100 | | C9 | 100 | 90.44 | 92.31 | 100 | 100 | | C10 | 87.60 | 82.39 | 93.13 | 88.58 | 92.62 | | C11 | 98.61 | 90.20 | 92.15 | 96.97 | 99.62 | | C12 | 91.21 | 89.81 | 87.0 | 92.93 | 98.10 | | C13 | 91.93 | 87.60 | 98.56 | 99.01 | 100 | | C14 | 98.98 | 96.20 | 96.03 | 99.36 | 99.76 | | C15 | 96.92 | 91.54 | 88.37 | 92.04 | 94.54 | | C16 | 94.90 | 93.86 | 90.29 | 98.91 | 98.92 | OA | | 94.6 | 90.16 | 92.27 | 94.85 | 98.18 | Kappa | | 93.88 | 89.91 | 91.21 | 94.14 | 97.92 |
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Table 2. Results of accuracy comparison of different algorithms on Indian Pines dataset (16 categories) %
Accuracy indicator | Category | Algorithm |
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LDM-FL | p-CNN* | 2D-CNN | 3D-CNN | 3D-CNN-CRF |
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| C1 | 96.03 | 87.34 | 97.12 | 98.22 | 99.01 | | C2 | 99.03 | 94.63 | 99.45 | 99.34 | 99.67 | | C3 | 90.42 | 86.47 | 89.84 | 90.46 | 94.17 | | C4 | 91.99 | 96.29 | 98.48 | 99.28 | 99.74 | CA | C5 | 97.8 | 99.65 | 100 | 99.48 | 99.78 | | C6 | 89.61 | 93.23 | 86.85 | 90.57 | 95.45 | | C7 | 71.75 | 93.19 | 86.12 | 95.09 | 98.21 | | C8 | 87.17 | 86.42 | 94.46 | 96.32 | 98.18 | | C9 | 92.68 | 100 | 98.85 | 98.44 | 98.23 | OA | 94 | 92.56 | 95.6 | 97.2 | 98.6 | | Kappa | 92.1 | 91.7 | 94.7 | 96.3 | 98.1 | |
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Table 3. Results of accuracy comparison of different algorithms on Pavia University dataset (9 categories)%
Dataset | Size of dataset | Algorithm | Time | | | |
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| | | Feature extraction /min | 2D/3D-CNN training /min | 2D/3D-CNN testing /min | CRF /s |
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Indian Pines | 145×145×220 | 2D-CNN3D-CNN-CRF | 0.60.9 | 2.63.7 | 0.851.2 | -26.3 | Pavia University | 610×340×103 | 2D-CNN3D-CNN-CRF | 1.31.8 | 2.94.3 | 1.62.2 | -32.5 | Pavia Center | 1096×715×102 | 2D-CNN3D-CNN-CRF | 1.72.1 | 3.45.3 | 1.72.6 | -42.5 |
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Table 4. 2D/3D-CNN training, testing and optimization time for three datasets