光子学报, 2020, 49 (4): 0410004, 网络出版: 2020-04-24 复制并引用该论文  本文已被引 2 次  被引通知

图 & 表

图 1. Comparison of spectral feature before and after filtering

Fig. 1. Comparison of spectral feature before and after filtering

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图 2. The flowchart of HGF-NRS

Fig. 2. The flowchart of HGF-NRS

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图 3. Reconstruction residuals of a test sample

Fig. 3. Reconstruction residuals of a test sample

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图 4. The influence of varying ε and r on OA

Fig. 4. The influence of varying ε and r on OA

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图 5. The influence of varying λ and T on OA

Fig. 5. The influence of varying λ and T on OA

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图 6. Classification results of algorithms on Indian Pines dataset

Fig. 6. Classification results of algorithms on Indian Pines dataset

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图 7. Classification results of algorithms on Salinas dataset

Fig. 7. Classification results of algorithms on Salinas dataset

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图 8. Classification results of algorithms on GRSS_DFC_2013 dataset

Fig. 8. Classification results of algorithms on GRSS_DFC_2013 dataset

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表 1实验参数设置

Table1. Experimental parameters setting

g r T λ Indian Pines 0.01 2 8 0.05 Salinas 0.000 5 2 18 0.01 GRSS_DFC_2013 0.000 1 1 7 0.03

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表 2分类性能对比(Indian Pines)

Table2. Classification performance comparison (Indian Pines)

Class Train Test NRS[10] Gabor-NRS[19] JCR2[12] HiFi-We[17] EPF-G-g[16] HGF-NRS Alfalfa 24 22 95.45 100.0 16.20 100.0 100.0 100.0 Corn-N 90 1338 78.68 90.15 95.83 93.39 94.22 97.93 Corn-M 80 750 76.57 93.42 99.44 93.60 96.66 95.05 Corn 68 169 47.16 95.48 95.83 100.0 78.97 98.83 Grass-P 71 412 90.87 97.54 99.50 98.78 97.53 100.0 Grass-T 74 656 97.62 98.18 97.57 99.69 99.24 99.70 Grass-P-M 14 14 100.0 100.0 100.0 92.85 100.0 100.0 Hay-W 70 408 99.03 100.0 99.51 100.0 100.0 100.0 Oats 10 10 81.82 100.0 100.0 100.0 100.0 100.0 Soybean-N 79 893 75.95 98.03 97.79 92.37 85.07 98.07 Soybean-M 109 2346 80.48 92.52 95.75 97.62 95.84 98.81 Soybean-C 69 524 83.16 94.14 98.53 98.09 94.45 99.05 Wheat 68 137 99.28 100.0 100.0 100.0 100.0 97.86 Woods 85 1180 95.63 100.0 100.0 99.06 98.62 100.0 Buildings-G-T-D 68 318 69.95 85.99 95.56 100.0 85.31 100.0 Stone-S-T 46 47 97.87 86.79 82.35 100.0 92.16 94.0 OA - - 82.85 94.58 96.14 96.87 94.57 98.63 AA - - 85.59 95.77 92.12 97.84 94.88 98.71 Kappa - - 80.27 93.74 95.55 96.39 93.75 98.43

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表 3分类性能对比(Salinas)

Table3. Classification performance comparison (Salinas)

Class Train Test NRS[10] Gabor-NRS[19] JCR2[12] HiFi-We[17] EPF-G-g[16] HGF-NRS Brocoli-G-W-1 30 1979 99.95 100.0 100.0 99.24 100.0 100.0 Brocoli-G-W-2 30 3696 99.81 99.11 99.70 99.86 99.95 99.92 Fallow 30 1946 95.15 96.38 100.0 99.79 94.95 100.0 Fallow-R-P 30 1364 97.63 95.29 96.51 98.97 97.36 96.06 Fallow-S 30 2648 99.77 99.06 99.58 99.16 99.89 100.0 Stubble 30 3929 99.95 100.0 100.0 99.23 99.92 100.0 Celery 30 3549 99.30 99.66 100.0 99.04 100.0 99.94 Grapes-U 30 11241 81.48 94.50 92.30 82.93 90.56 99.94 Soil-V-D 30 6173 99.44 98.88 99.80 99.96 99.13 99.97 Corn-S-G-W 30 3248 90.82 95.79 97.99 89.28 91.44 99.16 Lettuce-R-4 30 1038 94.86 88.19 99.81 100.0 94.70 100.0 Lettuce-R-5 30 1897 98.91 99.95 100.0 100.0 100.0 100.0 Lettuce-R-6 30 886 99.66 99.44 100.0 98.87 100.0 100.0 Lettuce-R-7 30 1040 98.04 90.43 98.38 96.82 98.47 93.88 Vinyard-U 30 7238 58.04 79.50 80.98 88.31 77.03 95.98 Vinyard-V-T 30 1777 95.81 98.88 100.0 99.38 99.33 99.83 OA - - 88.11 94.48 95.36 93.86 93.67 99.13 AA - - 94.29 95.94 97.82 96.93 96.42 99.04 Kappa - - 86.81 93.87 94.83 93.17 92.96 99.03

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表 4分类性能对比(GRSS_DFC_2013)

Table4. Classification performance comparison (GRSS_DFC_2013)

Class Train Test NRS[10] Gabor-NRS[19] JCR2[12] HiFi-We[17] EPF-G-g[16] HGF-NRS Healthy grass 99 1152 99.20 99.65 97.04 92.10 99.03 99.91 Stressed grass 95 1159 98.29 97.22 99.91 95.85 96.71 99.91 Synthetic grass 96 601 100.0 100.0 100.0 98.83 100.0 100.0 Trees 94 1150 97.87 98.83 99.74 88.08 99.48 100.0 Soil 93 1149 97.60 99.91 100.0 99.65 98.63 100.0 Water 91 234 100.0 87.64 89.65 97.86 100.0 100.0 Residential 98 1170 84.82 93.19 93.66 82.64 97.04 97.73 Commercial 95 1149 91.59 96.82 97.40 62.83 97.37 99.56 Road 96 1156 77.34 87.53 93.58 88.06 96.84 97.22 Highway 95 1132 95.96 98.57 99.55 96.11 97.66 100.0 Railway 90 1145 93.23 97.82 98.06 89.95 93.59 99.13 Parking lot 1 96 1137 93.05 99.27 100.0 86.10 94.59 100.0 Parking lot 2 92 377 57.96 93.70 94.68 93.37 87.61 99.47 Tennis court 90 338 99.40 100.0 94.41 100.0 95.48 100.0 Running track 93 567 100.0 96.76 100.0 100.0 100.0 100.0 OA - - 91.86 96.68 97.67 89.70 97.10 99.42 AA - - 92.42 96.46 97.18 91.43 96.94 99.53 Kappa - - 91.19 96.41 97.48 88.85 96.86 99.37

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表 5不同训练样本比例下的全局准确率(Indian Pines)

Table5. Overall classification accuracy in varyingproportion of training samples(Indian Pines)

Proportion of training samples/% 1 2 3 4 5 NRS[10] 58.78(3.57) 66.71(1.93) 72.28(2.03) 74.46(1.10) 76.40(1.48) Gabor-NRS[19] 58.40(3.54) 71.50(2.11) 81.13(1.85) 84.75(2.73) 88.38(1.97) JCR2[12] 68.42(3.98) 79.45(3.79) 87.27(1.09) 89.88(1.34) 92.49(1.06) HiFi-We[17] 74.74(2.31) 85.66(1.98) 87.73(3.16) 91.72(1.11) 93.40(1.03) EPF-G-g[16] 64.34(3.69) 74.63(4.00) 83.02(2.96) 86.30(0.74) 88.54(1.42) HGF-NRS 79.66(4.04) 89.47(1.60) 92.75(0.94) 94.19(1.29) 96.38(0.66)

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表 6不同训练样本比例下的全局准确率(Salinas)

Table6. Overall classification accuracy in varying proportion of training samples(Salinas)

Proportion of training samples/% 0.4 0.5 0.6 0.7 0.8 NRS[10] 85.33(1.94) 87.32(1.74) 87.18(1.02) 87.52(1.13) 87.77(1.23) Gabor-NRS[19] 90.20(0.97) 91.67(0.76) 92.42(1.33) 92.77(1.30) 93.61(0.56) JCR2[12] 91.30(1.54) 92.32(0.61) 92.90(1.34) 93.48(1.26) 94.22(1.36) HiFi-We[17] 91.78(1.43) 91.78(0.94) 92.40(1.32) 92.50(1.32) 93.19(1.14) EPF-G-g[16] 89.03(3.39) 89.23(1.77) 91.58(2.71) 91.77(2.70) 91.34(3.07) HGF-NRS 96.28(0.79) 96.86(0.88) 98.40(0.51) 98.42(0.50) 98.93(0.43)

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表 7不同训练样本比例下的全局准确率(GRSS_DFC_2013)

Table7. Overall classification accuracy in varying proportion of training samples (GRSS_DFC_2013)

Proportion of training samples/% 1 2 3 4 5 NRS[10] 80.46(1.67) 86.24(1.23) 88.70(1.09) 90.22(0.56) 90.85(0.58) Gabor-NRS[19] 75.19(1.60) 84.76(1.35) 88.96(0.85) 91.21(0.98) 93.21(0.78) JCR2[12] 84.92(1.33) 91.15(0.95) 93.85(1.08) 95.41(0.94) 96.52(0.69) HiFi-We[17] 80.43(2.62) 85.22(1.71) 86.71(1.66) 88.08(1.03) 88.72(0.63) EPF-G-g[16] 78.08(2.75) 87.00(1.60) 90.87(2.08) 93.11(0.66) 93.74(0.61) HGF-NRS 86.14(2.13) 93.00(1.66) 95.18(1.03) 96.71(0.74) 97.61(0.74)

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表 8各算法的计算时间(s)

Table8. Computing time of algorithms (s)

NRS[10] Gabor-NRS[19] JCR2[12] HiFi-We[17] EPF-G-g[16] HGF-NRS Indian Pines 48.3 33.10 49.5 589.5 7.9 53.4 Salinas 136.6 91.32 140.2 647.26 13.7 179.9 GRSS_DFC_2013 90.9 86.6 103.6 1 208.4 108.5 270.4

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徐冬冬, 程德强, 陈亮亮, 寇旗旗, 唐守锋. 基于分层引导滤波与最近邻正则化子空间的高光谱图像分类[J]. 光子学报, 2020, 49(4): 0410004. Dong-dong XU, De-qiang CHENG, Liang-liang CHEN, Qi-qi KOU, Shou-feng TANG. Hyperspectral Image Classification Based on Hierarchical Guidance Filtering and Nearest Regularized Subspace[J]. ACTA PHOTONICA SINICA, 2020, 49(4): 0410004.