Optics and Precision Engineering, Volume. 32, Issue 7, 1087(2024)
Collaborative classification of hyperspectral and LiDAR data based on CNN-transformer
Figures & Tables(15)
Fig. 5. Pseudo color map and ground-truth map of Houston2013 dataset
Fig. 9. Classification results of different methods on Houston2013 dataset
Fig. 10. Classification results of different methods on Trento dataset
Table 1. Land class details in Houston2013 dataset
View tableView in ArticleTable 1. Land class details in Houston2013 dataset
Class name Train num Test num Color Healthy grass 198 1 053 Stressed grass 190 1 064 Synthetic grass 192 505 Trees 188 1 056 Soil 186 1 056 Water 182 143 Residential 196 1 072 Commercial 191 1 053 Road 193 1 059 Highway 191 1 036 Railway 181 1 054 Parking Lot1 192 1 041 Parking Lot2 184 285 Tennis court 181 247 Running track 187 473 Total 2 832 12 197
Table 2. Land class details in Trento dataset
View tableView in ArticleTable 2. Land class details in Trento dataset
Class name Train num Test num Color Apples 129 3 905 Buildings 15 2 778 Ground 105 374 Woods 154 8 969 Wineyard 184 10 317 Roads 122 3 052 Total 819 29 395
Table 3. Comparison of classification accuracy of different methods on Houston2013 dataset
View tableView in ArticleTable 3. Comparison of classification accuracy of different methods on Houston2013 dataset
Class Two-Branch EndNet MDL-Middle MAHiDFNet Spectrum-LiDAR TB-HSI CLCT-Net C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
83.10
84.10
100.00
93.09
100.00
99.30
92.82
82.34
84.70
65.44
88.24
89.53
92.28
96.76
99.79
81.58
83.65
100.00
93.09
99.91
95.10
82.65
81.29
88.29
89.00
83.78
90.39
82.46
100.00
98.10
83.10
85.06
99.60
91.57
98.86
100.00
97.64
88.13
85.93
74.42
84.54
95.39
87.37
95.14
100.00
98.53
92.87
91.11
98.10
98.38
98.58
99.15
80.94
98.04
72.81
72.71
76.80
95.80
99.53
100.53
49.17
35.93
72.12
65.08
59.63
24.54
75.61
75.23
74.74
62.37
85.37
50.13
41.26
28.46
70.24
100.0
98.04
30.64
99.28
99.62
93.38
85.66
92.76
94.46
88.41
96.16
84.15
96.30
100.00
90.27
87.07
98.05 96.67
94.78
99.34
77.14
89.50
83.31
94.33 92.84
95.79 86.26
87.37
100.00 94.22
OA 87.98 88.52 89.55 89.58 60.81 85.22 92.01 AA 90.11 89.95 91.05 91.36 57.99 89.96 91.78 K×100 86.98 87.59 87.59 88.74 57.67 84.02 91.33
Table 4. Comparison of classification accuracy of different methods on Trento dataset
View tableView in ArticleTable 4. Comparison of classification accuracy of different methods on Trento dataset
Class Two-Branch EndNet MDL-Middle MAHiDFNet Spectrum-LiDAR TB-HSI CLCT-Net C1
C2
C3
C4
C5
C6
99.78
97.93
99.93
99.46
98.96
91.68
88.19
98.49
95.19
99.30
91.96
90.14
99.50
97.55
99.10
99.90
99.71
92.25
99.91
88.92
97.53
99.98
99.90
99.78
74.00
62.45
26.00
99.54
98.45
88.94
99.19
81.24
63.46
99.93
97.35
94.52
99.30
97.49
96.45
99.29
99.70
96.28
OA 98.36 94.17 98.73 98.59 84.94 95.42 98.90 AA 97.96 93.88 98.00 97.55 74.90 89.28 98.10 K×100 97.83 92.22 98.32 98.12 80.56 93.89 98.54
Table 5. FLOPs and parameters of different classification models
View tableView in ArticleTable 5. FLOPs and parameters of different classification models
Method #param./M FLOPs/M Two-Branch 12 25 EndNet 0.07 0.49 MDL-Middle
MAHiDFNet
0.25
77
4.7
155
CLCT-Net 5.1 384
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Haibin WU, Shiyu DAI, Aili WANG, Iwahori YUJI, Xiaoyu YU. Collaborative classification of hyperspectral and LiDAR data based on CNN-transformer[J]. Optics and Precision Engineering, 2024, 32(7): 1087
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Received: Oct. 23, 2023
Accepted: --
Published Online: May. 28, 2024
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