Infrared and Laser Engineering, Volume. 50, Issue 12, 20210112(2021)
Conditional random field classification method based on hyperspectral-LiDAR fusion
Figures & Tables(9)
Fig. 1. Hyperspectral and LiDAR co-classification by CRF integrating feature dissimilarity and class co-occurrence
Fig. 4. Classification results obtained from different feature fusion settings in the shaded area (Houston data set)
Fig. 5. Initial classification map and results optimized by different CRF methods (Houston data set)
Table 1. Class names and their numbers
View tableView in ArticleTable 1. Class names and their numbers
(a) 休斯顿 (a) Houston (b) 高峰林场 (b) Gaofeng forest farm Class name Number of training/testing samples/pixel Sample color Class name Number of training/testing samples/pixel Sample color Healthy grass 198/1053 Eucalyptus 193/315 Stressed grass 190/1064 Road 74/106 Synthetic grass 192/505 Tilia tuan 40/52 Trees 188/1056 Cultivated land 95/127 Soil 186/1056 Acacia crassicarpa benth 208/308 Water 182/143 Wasteland 16/20 Residential 196/1072 Michelia macclurei dandy 69/95 Commercial 191/1053 Building 165/251 Road 193/1059 Other broad leaved forests 184/275 Highway 191/1036 Pinus massoniana lamb 214/300 Railway 181/1054 Cunninghamia lanceolata 34/47 Parking Lot 1 192/1041 Water 390/562 Parking Lot 2 184/285 Mixed shrub forest 53/84 Tennis court 181/247 Bamboo 21/34 Running track 187/473 Grassland 23/20
Table 2. Influence of different
β values on the final classification accuracy (Houston)View tableView in ArticleTable 2. Influence of different
β values on the final classification accuracy (Houston)Precision β 0.5 1 1.5 2 2.5 3 3.5 4 4.5 OA 93.99% 94.00% 93.93% 93.88% 93.89% 93.86% 93.85 93.84% 93.83% Kappa 0.935 0.935 0.934 0.933 0.934 0.933 0.933 0.933 0.933 AA 93.47% 93.42% 93.19% 93.07% 91.30% 93.06% 93.04% 93.04% 93.03%
Table 3. Producer's accuracy comparison of seven classification methods for different data sets
View tableView in ArticleTable 3. Producer's accuracy comparison of seven classification methods for different data sets
(a) 休斯顿数据集 (a) Houston data set Category Pixel level classification method CRF classification optimization method FSpe FDSM FSpe+FSpa FSpe+FDSM GGF GGF_CRF1 GGF-CRF Healthy grass 82.34 24.88 55.69 55.89 81.67 82.43 83.1 Stressed grass 83.36 55.92 84.40 84.49 99.34 99.62 99.81 Synthetic grass 100 91.88 100 100 100 100 100 Trees 93.37 67.23 91.57 98.11 99.24 99.24 99.62 Soil 98.30 76.80 100 99.15 100 100 100 Water 91.61 80.42 99.30 96.50 95.10 95.10 94.41 Residential 76.59 71.74 82.84 91.32 92.35 92.26 93.47 Commercial 56.51 61.92 53.09 52.42 94.59 94.78 95.73 Road 66.57 51.37 79.04 83.95 86.02 85.93 85.74 Highway 72.39 53.86 68.15 79.92 93.24 93.63 94.98 Railway 92.88 83.97 97.34 87.76 90.70 90.80 90.61 Parking Lot 1 78.58 60.71 97.70 79.63 94.24 94.43 97.41 Parking Lot 2 72.98 57.19 81.05 74.04 72.28 71.93 66.67 Tennis Court 98.79 97.17 100 98.79 100 100 100 Running Track 98.31 28.96 98.52 97.67 99.37 99.37 99.79 OA 81.98% 60.48% 85.12% 85.14% 93.34% 93.47% 94.00% AA 84.17% 64.27% 85.91% 85.31% 93.21% 93.30% 93.42% Kappa 0.805 0.597 0.839 0.839 0.928 0.929 0.935 (b)高峰林场数据集 (b) Gaofeng forest farm data set Category Pixel level classification method CRF classification optimization method FSpe FDSM FSpe+FSpa FSpe+FDSM GGF GGF_CRF1 GGF-CRF Eucalyptus 73.65 60.63 90.79 77.46 86.67 96.82 97.14 Road 48.11 52.83 90.57 66.98 74.53 73.50 73.58 Tilia tuan 5.77 46.15 59.62 53.85 25 32.69 32.69 Cultivated land 83.46 98.43 100 96.85 100 100 100 Acacia crassicarpa benth 71.75 88.31 97.08 87.66 90.91 97.73 97.73 Wasteland 80 55 95 90 95 100 100 Michelia macclurei dandy 31.58 55.79 75.79 70.53 67.37 83.16 84.24 Building 83.27 84.06 96.41 92.83 98.01 97.21 97.21 Other broad leaved forests 70.91 66.55 96.36 65.82 83.27 85.45 85.82 Pinus massoniana lamb 73.67 92.67 92.00 85.67 89.00 96.67 97.00 Cunninghamia lanceolata 12.77 68.09 95.74 65.96 78.72 95.74 95.74 Water 99.82 98.22 100 99.64 100 100 100 Mixed shrub forest 2.38 51.19 73.81 67.86 73.81 88.1 88.1 Bamboo 0 2.94 17.65 17.65 0 0 0 Grassland 10.00 40.00 55.00 85.00 95.00 100 100 OA 71.46% 78.58% 92.41% 83.32% 87.71% 92.37% 92.84% AA 49.81% 64.06% 82.39% 74.92% 77.15% 83.14% 83.28% Kappa 0.674 0.756 0.914 0.811 0.860 0.913 0.919
Table 4. Comparison of classification accuracy of different methods on Houston data set
View tableView in ArticleTable 4. Comparison of classification accuracy of different methods on Houston data set
Precision Deep fusion[ 15 ]HyMCKs[ 8 ]Multi level fusion method[ 4 ]EC-CRF[ 11 ]GGF-CRF OA 91.32% 90.33% 93.22% 91.70% 94.00% Kappa 0.9057 0.8949 0.930 0.907 0.935
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Leiguang Wang, Ruozheng Geng, Qinling Dai, Jun Wang, Chen Zheng, Zhitao Fu. Conditional random field classification method based on hyperspectral-LiDAR fusion[J]. Infrared and Laser Engineering, 2021, 50(12): 20210112
Paper Information
Category: Image processing
Received: Feb. 17, 2021
Accepted: --
Published Online: Feb. 9, 2022
The Author Email: Chen Zheng (zhengchen_data@126.com)
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