Optics and Precision Engineering, Volume. 30, Issue 15, 1889(2022)
Semi-supervised dual path network for hyperspectral image classification
Figures & Tables(12)
Fig. 2. False color map and color marked real feature map of Heihe dataset
Fig. 3. False color map and color marked real feature map of LongKou dataset
Fig. 4. Experimental results of different unlabeled sample numbers on Heihe data set
Fig. 5. Classification results of each algorithm on Heihe data set
Fig. 6. Classification results of each algorithm on Longkou data set
Table 1. Classification results with different methods on Heihe data set（overall accuracy ± Std）
View tableView in ArticleTable 1. Classification results with different methods on Heihe data set（overall accuracy ± Std）
Algorithm ni=5 ni=10 ni=20 ni=30 ni =40 ni =50 RAW 79.39±4.36 82.50±1.65 86.62±1.81 87.13±1.13 88.18±1.08 88.71±0.83 PCA 79.39±4.36 82.50±1.65 86.62±1.81 87.13±1.13 88.18±1.08 88.71±0.83 NPE 70.29±6.75 72.37±4.72 81.01±2.33 80.37±2.03 86.64±1.25 88.63±1.16 LDA 68.93±5.02 73.09±5.04 76.89±3.79 89.41±1.05 91.81±0.88 92.79±0.96 MFA 79.48±4.80 83.63±2.22 90.99±1.18 91.44±1.21 92.04±0.90 92.10±1.20 LGSFA 69.59±4.32 74.94±8.30 70.20±4.22 90.22±0.95 91.20±1.21 93.14±0.90 SDA 84.63±2.55 88.89±1.38 91.56±2.64 91.84±1.35 92.45±1.03 92.88±1.53 SELD 81.08±3.62 86.78±2.35 69.68±4.52 89.40±0.84 91.83±0.90 92.54±1.01 SCGSP 80.40±3.29 86.87±1.78 65.45±6.74 87.20±1.70 90.13±1.53 92.19±0.98 S3MDA 79.60±5.59 84.36±1.50 87.70±1.57 89.08±1.86 88.38±3.07 90.05±1.40 1DCNN 79.65±2.56 84.89±2.49 85.30±2.08 86.99±2.28 89.67±1.40 90.28±2.19 2DCNN 79.99±3.77 85.77±3.80 85.77±1.78 87.00±3.47 89.15±1.88 93.37±0.74 3DCNN 83.97±3.21 88.98±2.21 92.96±1.90 93.37±2.32 94.47±2.46 95.46±0.88 DMRBN 81.10±4.22 87.80±1.14 88.49±2.71 89.68±1.75 91.24±0.73 91.88±0.94 DLPNet 81.19±3.65 86.80±1.39 88.36±2.48 89.71±1.56 90.95±0.68 91.85±0.56 SSDPNet 88.69 ±3.60 93.24 ±0.82 94.33 ±0.43 95.23 ±1.46 95.96 ±0.74 96.79 ±0.79
Table 2. Ablation experimental results on Heihe dataset （overall classification accuracy ± standard deviation）
View tableView in ArticleTable 2. Ablation experimental results on Heihe dataset （overall classification accuracy ± standard deviation）
Algorithm 5 samples per class 50 samples per class OA/% KC OA/% KC 2DCNN 81.28±4.06 0.749±0.051 90.43±1.99 0.870±0.027 2DCNN+SSGragh 86.78±1.72 0.817±0.024 91.04±2.38 0.877±0.032 NN 83.80±3.27 0.784±0.042 92.99±0.93 0.904±1.22 NN+SSGragh 86.76±2.05 0.820±0.028 93.65±0.97 0.913±1.34 SSDPNet 89.90 ±1.78 0.862 ±0.024 94.02 ±0.63 0.918 ±0.009
Table 3. Classification results of each class samples via different methods on Heihe data set
View tableView in ArticleTable 3. Classification results of each class samples via different methods on Heihe data set
Class Raw PCA NPE LDA MFA LGSFA 1DCNN 2DCNN 1 90.24±1.38 90.07±1.55 88.68±1.23 78.26±4.66 93.49±1.69 87.88±2.65 87.19±4.89 97.75 ±0.74 2 81.73±4.67 81.54±4.77 74.49±10.21 84.05±5.23 93.96±1.58 77.95±6.64 84.30±13.8 96.83±2.18 3 87.00±2.56 86.61±2.60 86.10±3.25 70.26±5.41 90.26±2.47 68.53±5.53 80.73±7.19 89.57±3.56 4 69.78±4.57 68.88±4.64 56.19±6.29 61.62±9.62 82.65±3.61 43.12±11.73 75.06±9.75 82.94±6.11 5 84.76±4.13 84.61±4.13 75.44±6.56 50.97±6.21 83.95±6.63 24.50±6.27 62.23±15.45 87.11±4.02 6 90.93±2.38 90.89±2.44 90.59±1.65 74.58±7.04 93.26±1.21 66.93±10.74 17.34±33.62 56.94±20.8 7 74.31±6.09 73.89±6.04 67.35±7.00 55.42±8.03 79.06±6.08 39.04±6.85 10.31±21.75 46.30±20.69 8 92.26±3.15 92.10±3.13 87.32±4.32 89.92±3.91 96.48±1.73 87.03±4.97 34.07±32.91 76.69±25.33 OA 87.81±1.16 87.58±1.26 85.40±0.33 74.25±3.86 91.80±1.29 73.46±4.16 82.53±6.52 90.32±1.32 AA 83.87±1.50 83.58±1.55 78.27±2.01 70.64±3.27 89.14±1.26 61.87±2.88 56.40±6.73 79.27±5.34 KC 0.83±0.02 0.83±0.02 0.80±0.01 0.66±0.05 0.88±0.02 0.64±0.05 0.75±0.10 0.91±0.02 Class 3DCNN DLPNet DMRBN SDA SELD SCGSP S3MDA SSDPNet 1 95.17±4.02 91.26±2.56 92.04±1.99 94.08±1.74 72.66±5.02 74.82±3.10 91.35±1.62 95.87±0.91 2 99.02 ±1.03 90.68±4.36 92.56±3.81 96.74±1.05 73.47±8.93 64.68±12.78 90.22±4.74 94.14±2.03 3 85.01±17.44 89.14±2.62 88.94±2.36 90.47±2.61 43.29±9.83 56.33±8.74 88.07±2.33 93.78 ±1.55 4 92.18 ±5.56 84.48±5.19 85.32±4.99 84.62±3.27 40.08±8.33 50.01±6.95 76.64±4.26 85.03±5.98 5 87.85±10.24 86.12±5.23 88.02±4.01 86.26±4.92 29.34±4.97 39.15±7.87 86.42±5.16 89.07 ±2.69 6 57.66±30.80 89.68±4.36 90.38±3.78 95.16 ±1.98 67.57±9.72 47.13±11.03 94.34±1.19 93.76±3.43 7 13.75±21.28 80.73±4.18 72.09±25.52 80.73±7.35 37.83±7.84 35.29±3.65 77.25±7.96 81.98 ±6.17 8 53.20±38.10 89.60±4.85 92.05±3.46 96.76±1.94 82.07±6.50 76.68±6.76 94.89 ±1.93 92.24±2.65 OA 90.74±8.19 89.71±2.32 90.30±1.79 92.91±0.63 62.76±5.46 60.71±3.40 90.44±1.12 93.75 ±1.00 AA 72.98±10.20 87.71±2.32 88.81±1.34 90.60±1.02 55.79±5.00 55.51±3.77 87.65±2.15 90.73 ±1.30 KC 0.87±11.14 0.86±0.03 0.87±0.02 0.90±0.01 0.51±0.07 0.49±0.04 0.87±0.02 0.92 ±0.01
Table 4. Classification results with different methods on LongKou data set（overall accuracy±std）
View tableView in ArticleTable 4. Classification results with different methods on LongKou data set（overall accuracy±std）
Algorithm ni=5 ni=10 ni=20 ni=30 ni =40 ni =50 RAW 73.51±3.05 75.52±2.06 79.75±1.65 80.71±1.49 82.37±0.94 83.20±0.92 PCA 73.51±3.05 75.52±2.06 79.75±1.65 80.70±1.49 82.37±0.94 83.20±0.92 NPE 74.09±4.87 74.18±4.08 74.37±3.21 79.22±1.58 74.44±1.82 79.52±1.25 LDA 80.20±2.38 76.89±3.79 76.29±3.02 56.27±3.02 77.30±1.50 88.47±0.77 MFA 85.48±4.91 83.25±2.22 88.09±0.88 89.25±1.87 90.84±1.07 90.90±1.07 LGSFA 74.11±4.82 76.53±3.95 70.12±1.67 36.31±9.21 79.55±1.57 85.29±1.44 SDA 82.84±3.89 88.89±1.38 91.49±1.32 92.15±0.78 93.57±0.94 94.36±0.50 SELD 86.17±2.10 86.78±2.36 86.42±1.97 87.35±1.51 89.14±1.05 91.18±0.72 SCGSP 80.53±3.63 86.87±1.78 89.91±1.54 91.71±0.80 92.83±1.01 93.82±0.80 S3MDA 82.84±3.89 84.35±1.50 86.24±1.77 87.81±1.54 88.47±2.80 90.34±1.06 1DCNN 82.20±7.43 83.14±7.96 87.10±1.77 89.44±2.82 91.84±1.08 93.82±1.21 2DCNN 83.14±5.11 87.03±3.55 88.05±2.82 90.85±2.09 92.44±3.58 94.33±2.85 3DCNN 84.66±5.50 87.05±2.48 88.37±1.56 91.45±1.47 93.62±1.47 95.94±0.97 DMRBN 81.56±4.50 87.80±1.14 90.73±1.27 91.99±0.77 93.45±1.34 94.46±0.51 DLPNet 83.99±2.53 86.80±1.39 90.68±1.29 91.60±1.33 93.19±0.85 94.40±0.59 SSDPNet 90.56 ±1.70 93.24 ±0.82 95.62 ±0.58 96.06 ±0.93 96.67 ±1.23 97.53 ±0.60
Table 5. Ablation experimental results on Longkou data set
View tableView in ArticleTable 5. Ablation experimental results on Longkou data set
Algorithm 5 samples per class 50 samples per class OA/% Kappa OA/% Kappa 2DCNN 82.74±9.01 0.783±0.106 93.64±0.29 0.918±0.135 2DCNN+SSGragh 87.98±6.20 0.845±0.076 95.13±0.13 0.937±0.243 NN 80.30±7.47 0.751±0.089 94.37±0.42 0.927±0.533 NN+SSGragh 82.55±3.58 0.779±0.043 94.41±0.16 0.927±0.206 SSDPNet 89.53 ±1.96 0.864 ±0.025 96.05 ±0.42 0.946 ±0.496
Table 6. Classification results of each class samples via different methods on Heihe data set
View tableView in ArticleTable 6. Classification results of each class samples via different methods on Heihe data set
Class Raw PCA NPE LDA MFA LGSFA 1DCNN 2DCNN 1 88.11±1.89 87.99±1.94 54.81±8.35 37.96±7.01 96.47±2.39 35.41±5.73 95.01±4.69 90.22±5.08 2 42.32±5.11 42.11±5.14 25.27±10.49 28.71±7.29 64.00±7.46 15.52±1.94 23.46±21.50 59.39±30.68 3 52.74±6.28 52.41±6.22 31.35±9.65 36.97±8.19 79.16±6.13 26.76±7.09 45.53±34.79 62.99±32.21 4 80.92±2.44 80.93±2.40 60.62±6.27 25.07±5.61 89.47±3.45 39.18±5.18 90.12±2.80 92.40±2.76 5 58.49±7.02 57.89±6.94 29.62±11.59 25.76±4.69 74.28±6.96 14.56±2.89 8.07±11.18 54.58±22.66 6 86.27±6.77 86.02±6.85 41.87±8.91 46.76±7.63 95.74±3.25 40.42±8.11 96.37±0.94 95.26±7.13 7 99.94±0.05 99.94±0.05 98.17±1.96 54.74±9.33 99.92±0.07 78.66±7.79 98.82±0.81 99.05±0.58 8 77.59±3.60 77.48±3.42 36.31±13.94 33.08±7.25 82.83±5.00 22.11±5.38 67.22±28.15 69.61±15.10 9 39.30±4.71 38.96±4.75 18.89±4.88 38.76±8.80 74.76±3.50 21.88±4.54 78.21±36.48 81.74±7.75 OA 85.05±0.58 84.98±0.59 65.45±4.43 39.20±4.15 92.33±1.00 48.88±3.96 87.39±1.31 90.92±1.97 AA 69.51±1.52 69.30±1.53 44.10±5.90 36.42±1.95 84.07±1.03 32.71±1.98 66.98±6.44 78.36±9.43 KC 0.80±0.01 0.81±0.01 0.57±0.05 0.29±0.04 0.90±0.01 0.35±0.05 0.83±0.02 0.88±0.03 Class 3DCNN DLPNet DMRBN SDA SELD SCGSP S3MDA SSDPNet 1 93.97±8.56 95.23±1.94 95.45±1.90 96.87±1.01 94.54±1.81 96.35±1.14 94.20±3.12 98.86 ±0.54 2 51.82±23.83 73.88±5.78 72.91±7.82 65.19±6.11 60.82±5.20 61.57±8.31 57.17±10.47 80.60 ±6.97 3 45.07±39.15 85.62±4.36 84.85±4.84 85.14±2.46 82.01±3.23 74.04±4.29 81.20±4.66 93.22 ±3.36 4 92.21±4.00 90.28±1.66 90.31±1.70 93.91±1.69 88.40±1.90 89.51±2.34 89.30±1.83 96.15 ±2.16 5 51.39±36.33 86.42±4.58 75.42±27.24 69.58±7.84 63.09±4.44 75.30±4.01 75.15±8.29 88.57 ±4.89 6 75.90±38.77 90.29±5.05 91.52±6.18 96.74 ±3.54 96.29±2.90 95.79±4.58 92.52±4.38 92.85±7.57 7 99.31±0.30 99.78±0.23 99.95±0.04 99.92±0.05 99.91±0.06 99.95±0.04 99.90±0.13 99.98 ±0.02 8 85.90±16.74 82.53±6.59 82.18±5.61 86.69±3.11 76.03±6.24 82.03±3.85 72.32±4.21 86.76 ±5.05 9 87.27 ±13.63 77.53±6.59 75.00±5.82 77.89±3.84 75.69±3.58 72.05±4.26 69.18±6.55 80.04±7.49 OA 90.63±2.85 92.81±0.53 92.64±0.54 94.08±0.53 91.18±0.79 92.09±0.70 91.21±1.17 96.10 ±0.84 AA 75.94±4.86 86.82±0.53 86.28±1.80 85.77±1.68 81.86±1.11 82.96±1.74 81.99±2.58 90.78 ±1.48 KC 0.88±0.04 0.87±0.01 0.88±0.01 0.90±0.01 0.88±0.01 0.89±0.01 0.88±0.02 0.95 ±0.01
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Hong HUANG, Zhen ZHANG, Ling JI, Zhengying LI. Semi-supervised dual path network for hyperspectral image classification[J]. Optics and Precision Engineering, 2022, 30(15): 1889
Paper Information
Category: Information Sciences
Received: May. 7, 2022
Accepted: --
Published Online: Sep. 7, 2022
The Author Email: Hong HUANG (hhuang@cqu.edu.cn)
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