Optics and Precision Engineering, Volume. 32, Issue 23, 3504(2024)
Specral-spatial classification of hyperspectral imagery with hybrid architecture of 3D-CNN and Transformer
Figures & Tables(12)
Fig. 3. Visualization results of different methods on Indian Pine dataset
Fig. 4. Visualization results of different methods on Pavia University dataset
Fig. 5. Visualization results of different methods on WHU-Hi-LongKou dataset
Fig. 6. Scatter plot of overall accuracy predicted by five methods on three datasets
Table 1. Land cover class in Indian Pines dataset
View tableView in ArticleTable 1. Land cover class in Indian Pines dataset
Class Train num Test num Alfalfa 46 10 Corn-notill 1 428 150 Corn-mintill 830 150 Corn 237 10 Grass-pasture 483 150 Grass-trees 730 150 Grass-pasture-mowed 28 10 Hay-windrowed 478 150 Oats 20 10 Soybean-notill 972 150 Soybean-mintill 2 455 150 Soybean-clean 593 150 Wheat 205 10 Woods 1 265 150 Buildings-Grass-Trees-Drives 386 150 Stone-Steel-Towers 93 10 Total 10 249 1 560
Table 2. Land cover class in Pavia University dataset
View tableView in ArticleTable 2. Land cover class in Pavia University dataset
Class Train num Test num Asphalt 6 631 150 Meadows 18 649 150 Gravel 2 099 150 Trees 3 064 150 Painted metal sheets 1 345 150 Bare Soil 5 029 150 Bitumen 1 330 150 Self-Blocking Bricks 3 682 150 Shadows 947 150 Total 42 776 1 350
Table 3. Land cover class in WHU-Hi-LongKou dataset
View tableView in ArticleTable 3. Land cover class in WHU-Hi-LongKou dataset
Class Train num Test num Corn 34 511 150 Cotton 8 374 150 Sesame 3 031 150 Broad-leaf soybean 63 212 150 Narrow-leaf soybean 4 151 150 Rice 11 854 150 Water 67 056 150 Roads and houses 7 124 150 Mixed weed 5 229 150 Total 204 542 1 350
Table 4. Comparison of classification results of different methods on Indian Pines dataset
View tableView in ArticleTable 4. Comparison of classification results of different methods on Indian Pines dataset
Class 3D-CNN LWNet SpectralFormer SSFTT 3D-ConvFormer C1 97.22 100.00 97.83 100.00 91.67 C2 97.81 98.28 91.39 95.07 95.93 C3 98.82 99.85 96.02 100.00 99.56 C4 93.39 82.82 47.68 92.51 91.63 C5 100.00 100.00 98.34 99.70 100.00 C6 100.00 97.59 98.90 99.48 100.00 C7 100.00 100.00 100.00 100.00 100.00 C8 100.00 100.00 100.00 100.00 100.00 C9 100.00 100.00 100.00 100.00 100.00 C10 97.93 99.39 94.34 99.15 99.03 C11 98.57 99.05 84.97 99.22 99.13 C12 99.77 97.52 98.31 95.26 99.77 C13 85.64 82.56 71.22 85.64 89.74 C14 100.00 99.91 98.81 100.00 99.28 C15 100.00 100.00 98.70 100.00 100.00 C16 86.75 92.77 87.10 78.31 95.18 OA（%） 98.37 98.22 91.98 96.52 98.41 AA（%） 97.24 96.86 91.48 97.97 97.56 KAPPA（%） 98.11 97.94 90.89 97.65 98.16
Table 5. Comparison of classification results of different methods on Pavia University dataset
View tableView in ArticleTable 5. Comparison of classification results of different methods on Pavia University dataset
Class 3D-CNN LWNet SpectralFormer SSFTT 3D-ConvFormer C1 96.62 93.18 88.80 96.76 99.15 C2 94.37 97.71 84.10 99.34 99.70 C3 91.74 97.69 79.66 98.92 99.69 C4 96.12 93.45 94.88 81.67 97.25 C5 100.00 99.83 99.78 98.24 99.92 C6 99.80 100.00 91.41 99.92 100.00 C7 99.92 99.92 95.34 99.49 100.00 C8 96.72 99.04 89.35 99.52 98.58 C9 97.62 97.24 99.79 91.09 99.37 OA（%） 95.94 97.20 87.88 96.11 99.39 AA（%） 96.99 97.56 91.45 97.57 99.30 KAPPA（%） 94.62 96.26 84.34 96.74 99.18
Table 6. Comparison of classification results of different methods on WHU-Hi-LongKou dataset
View tableView in ArticleTable 6. Comparison of classification results of different methods on WHU-Hi-LongKou dataset
Class 3D-CNN LWNet SpectralFormer SSFTT 3D-ConvFormer C1 98.00 97.05 98.91 99.56 99.66 C2 98.20 98.54 60.45 99.29 99.77 C3 99.79 99.93 98.81 100.00 99.90 C4 91.47 93.18 84.90 98.02 96.29 C5 99.58 99.93 86.27 99.48 99.63 C6 98.79 98.97 98.76 98.10 98.89 C7 98.64 98.38 98.86 96.81 99.73 C8 92.63 92.47 93.95 95.23 98.48 C9 98.41 97.99 98.41 96.63 98.39 OA（%） 96.12 96.42 92.53 98.12 98.53 AA（%） 97.28 97.38 91.03 97.68 98.97 KAPPA（%） 94.94 95.32 90.37 97.20 98.07
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Haizhao JING, Lijie TAO, Haokui ZHANG. Specral-spatial classification of hyperspectral imagery with hybrid architecture of 3D-CNN and Transformer[J]. Optics and Precision Engineering, 2024, 32(23): 3504
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Received: Sep. 30, 2024
Accepted: --
Published Online: Mar. 10, 2025
The Author Email: ZHANG Haokui (hkzhang@nwpu.edu.cn)
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