Chinese Journal of Lasers, Volume. 47, Issue 7, 710001(2020)
Hyperspectral Remote Sensing Image Classification Based on Local Reconstruction Fisher Analysis
Figures & Tables(14)
Fig. 2. Pavia University hyperspectral image. (a) False-color image; (b) ground-truth map
Fig. 4. Overall accuracy of LRFA at different parameters (k and kp) in Pavia University dataset
Fig. 5. Overall accuracy of each algorithm at different dimensions in Pavia University dataset
Fig. 6. Classification maps of each algorithm on Pavia University dataset. (a) Ground-truth map; (b) RAW(OA:78.95%); (c) PCA(OA:78.98%); (d) LPP(OA:80.55%); (e) NPE(OA:80.98%); (f) LDA(OA:76.50%); (g) MMC(OA:75.11%); (h) MFA(OA:82.62%); (i) LGSFA(OA:78.23%); (j) LRFA(OA:86.07%)
Fig. 7. Overall accuracy of LRFA at different parameters (k and kp) in Urban dataset
Fig. 9. Classification maps of each algorithm on Urban dataset. (a) Ground-truth image; (b) RAW(OA:80.86%); (c) PCA(OA:80.79%); (d) LPP(OA:80.66%); (e) NPE(OA:81.89%); (f) LDA(OA:82.60%); (g) MMC(OA: 81.40%); (h) MFA(OA:82.32%); (i) LGSFA(OA:82.50%); (j) LRFA(OA:83.77%)
Fig. 10. Two-dimensional embedding distribution of each algorithm on Pavia University dataset. (a) PCA; (b) LPP; (c) NPE; (d) LDA; (e) MMC; (f) MFA; (g) LGSFA; (h) LRFA
Table 1. Classification results of each algorithm at different training sample sizes in Pavia University dataset
View tableTable 1. Classification results of each algorithm at different training sample sizes in Pavia University dataset
Algorithm (OA±std) /% Kappa 20 samples 40 samples 60 samples 100 samples 200 samples RAW 67.70±2.18 0.597 71.62±1.35 0.641 74.35±0.94 0.673 76.28±1.02 0.696 79.01±0.71 0.728 PCA 67.68±2.17 0.597 71.59±1.35 0.641 74.33±0.94 0.673 76.28±1.01 0.696 78.93±0.69 0.727 LPP 69.41±2.56 0.618 73.64±0.96 0.666 76.53±0.95 0.700 78.31±1.28 0.722 81.82±0.51 0.763 NPE 69.69±2.91 0.621 73.57±0.94 0.665 76.60±1.09 0.701 79.63±1.28 0.737 82.63±0.79 0.773 LDA 60.25±2.01 0.504 71.58±1.79 0.640 75.59±1.67 0.687 79.28±1.00 0.732 82.94±1.07 0.776 MMC 66.32±2.19 0.581 69.11±1.43 0.612 70.76±0.94 0.631 72.22±0.91 0.647 74.05±0.87 0.668 MFA 74.76±3.12 0.680 78.79±1.84 0.728 81.52±1.23 0.761 83.48±1.74 0.786 86.36±0.90 0.821 LGSFA[ 20 ]66.24±2.01 0.575 73.46±2.10 0.662 76.08±1.81 0.694 79.87±1.69 0.739 82.36±1.22 0.769 LRFA 75.69±3.32 0.691 80.25±1.40 0.745 82.18±1.11 0.769 84.55±1.52 0.798 86.80±0.55 0.826
Table 2. Classification performance of each algorithm on class samples in Pavia University dataset
View tableTable 2. Classification performance of each algorithm on class samples in Pavia University dataset
Class Classification accuracy /% RAW PCA LPP NPE LDA MMC MFA LGSFA[ 20 ]LRFA 1 79.94 80.09 81.04 82.01 81.65 83.53 84.36 77.76 84.63 2 91.45 91.46 93.01 92.92 89.81 84.21 98.28 98.09 97.77 3 45.52 45.43 50.67 65.16 52.94 40.66 42.69 36.28 59.72 4 66.37 66.34 68.08 70.33 73.69 69.11 80.15 80.51 79.92 5 99.02 99.02 99.32 99.62 99.55 99.10 99.55 99.62 99.55 6 44.33 44.33 46.37 52.80 51.34 44.43 42.16 35.77 60.80 7 82.54 82.69 85.50 64.16 25.44 62.11 58.92 36.67 80.71 8 76.63 76.65 77.15 69.82 60.88 70.04 78.38 65.60 77.53 9 99.79 99.79 99.68 98.29 73.13 99.47 99.04 78.68 99.79 OA /% 78.95 78.98 80.55 80.98 76.50 75.11 82.62 78.23 86.07 AA /% 76.18 76.20 77.87 77.24 67.60 72.52 75.95 67.67 82.27 Kappa 0.715 0.715 0.736 0.743 0.685 0.667 0.762 0.701 0.811 Time /s - 0.032 0.089 0.119 0.030 0.146 0.339 0.590 0.414
Table 3. Classification results of each algorithm at different training sample sizes in Urban dataset
View tableTable 3. Classification results of each algorithm at different training sample sizes in Urban dataset
Algorithm (OA±std) /% Kappa 20 samples 40 samples 60 samples 100 samples 200 samples RAW 71.61±3.22 0.57 74.81±1.7 0.611 75.34±1.29 0.618 75.96±1.06 0.627 76.78±0.99 0.638 PCA 71.6±3.22 0.57 74.8±1.69 0.611 75.35±1.27 0.618 75.95±1.04 0.627 76.79±0.97 0.638 LPP 71.97±3.16 0.575 74.23±2.12 0.603 74.85±1.12 0.612 75.08±1.13 0.615 76.23±1.19 0.63 NPE 71.04±3.42 0.561 74.69±2.2 0.611 75.7±1.17 0.625 76.08±1.03 0.63 77.78±1.17 0.653 LDA 58.77±3.94 0.414 68.24±2.57 0.529 76.76±0.97 0.64 78.75±0.97 0.667 79.14±0.79 0.672 MMC 70.61±1.87 0.553 71.45±3.4 0.567 71.87±2.18 0.57 72.51±1.54 0.577 72.7±1.44 0.582 MFA 74.77±1.67 0.617 75.4±1.41 0.621 76.26±2.21 0.635 76.82±1.65 0.642 77.83±1.18 0.655 LGSFA[ 20 ]74.82±2.6 0.612 76.61±1.64 0.640 78.3±1.19 0.662 78.54±1.14 0.664 79.28±1.08 0.674 LRFA 76.64±2.67 0.64 78.12±1.71 0.66 78.82±1.24 0.67 79.28±0.99 0.676 80.00±0.78 0.685
Table 4. Classification results of each algorithm on class samples in Urban dataset
View tableTable 4. Classification results of each algorithm on class samples in Urban dataset
Class Classification accuracy /% RAW PCA LPP NPE LDA MMC MFA LGSFA[ 20 ]LRFA 1 76.85 76.95 76.87 77.48 80.00 77.24 75.05 76.46 80.70 2 43.28 43.00 39.71 48.49 43.14 39.92 40.95 46.64 47.46 3 81.74 81.71 83.52 90.09 82.65 81.14 88.31 83.74 90.03 4 79.81 79.81 83.32 85.96 75.02 81.30 68.87 85.48 90.48 5 87.74 87.62 87.72 88.35 89.22 88.42 90.13 92.37 90.89 6 66.39 66.30 64.94 66.15 69.08 67.18 67.76 59.39 65.56 OA /% 80.86 80.79 80.66 81.89 82.60 81.40 82.32 82.50 83.77 AA /% 72.63 72.57 72.68 76.08 73.18 72.53 71.84 74.01 77.52 Kappa 0.677 0.675 0.673 0.694 0.706 0.686 0.698 0.695 0.723 Time /s - 0.063 0.119 0.221 0.047 0.559 0.975 1.459 1.194
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Liu Jiamin, Yang Song, Huang Hong. Hyperspectral Remote Sensing Image Classification Based on Local Reconstruction Fisher Analysis[J]. Chinese Journal of Lasers, 2020, 47(7): 710001
Paper Information
Category: remote sensing and sensor
Received: Dec. 23, 2019
Accepted: --
Published Online: Jul. 10, 2020
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