Infrared and Laser Engineering, Volume. 52, Issue 2, 20220376(2023)
Airborne MS-LiDAR data classification by combining NDRI features and spatial correlation
Figures & Tables(14)
Fig. 1. Titan MS-LiDAR point cloud of Area1 and its processing results. (a)
Fig. 2. Titan MS-LiDAR point cloud of Area2 and its processing results. (a)
Fig. 4. Relationships between wavelengths and reflectance properties of objects[26]
Fig. 6. NDRI statistics histogram for different objects of Area1. (a) Grass; (b) Road; (c) Tree; (d) Building
Fig. 7. Differentiability of different NDRI used for distinguishing from objects of Area1. (a)
Table 1. Specifications of Titan
View tableView in ArticleTable 1. Specifications of Titan
Parameter Specification Wavelengths C1: 1550 nm, C2: 1064 nm, C3: 532 nm Forward angles C1: 3.5°, C2: 0°, C3: 7° Beam divergence C1: 0.35 mrad, C2: 0.35 mrad, C3: 0.7 mrad Altitude Topographic: 300-2000 m above ground level (AGL), all channels Pulse repetition frequency 50-300 kHz/channel; 900 kHz total Scan angle (FOV) Programmable; 0-60° max Scan frequency Programmable; 0-210 Hz Swath width 0-115% AGL Accuracy Horizontal: 1/7500×altitude, 1σ; Vertical: < 5-10 cm, 1 σ Laser range precision <0.008 m, 1σ
Table 2. The errors of the filtering results
View tableView in ArticleTable 2. The errors of the filtering results
Experimental area Type I error Type II error Total error Area1 5.24% 1.87% 3.90% Area2 2.61% 4.25% 3.26%
Table 3. NDRI threshold
t *View tableView in ArticleTable 3. NDRI threshold
t *NDRI Area1 Area2 Ground points Non- ground points Ground points Non- ground points C2−C3 0.344 0.373 0.296 0.367 C1−C3 0.227 0.284 0.344 0.373 C1−C2 0.405 0.262 0.231 0.258
Table 4. Classification accuracy of different NDRI indexes
View tableView in ArticleTable 4. Classification accuracy of different NDRI indexes
NDRI Area1 Area2 OA Kappa OA Kappa C2−C3 90.45% 0.869% 89.89% 0.853% C1−C3 88.59% 0.841% 86.98% 0.812% C1−C2 80.68% 0.738% 83.07% 0.752%
Table 5. Confusion matrix of the land cover classification result (Area1）
View tableView in ArticleTable 5. Confusion matrix of the land cover classification result (Area1）
Classification data Reference data Total row User’s accuracy Roads Grass Trees Buildings Roads 24501 (A1) 1524 (A2) 939 (A3) 746 (A4) 27710 (Ta) 88.42% (Ua=A1/Ta) Grass 1386 (B1) 40260 (B2) 1137 (B3) 1128 (B4) 43911 (Tb) 91.69% (Ub=B2/Tb) Trees 234 (C1) 360 (C2) 24019 (C3) 1594 (C4) 26207 (Tc) 91.65% (Uc=C3/Tc) Buildings 40 (D1) 248 (D2) 2017 (D3) 18750 (D4) 21055 (Td) 89.05% (Ud=D4/Td) Total column 26161 (T1) 42392 (T2) 28112 (T3) 22218 (T4) 118883 Producer’s accuracy 93.65% (P1=A1/T1) 94.97% P2=B2/T2 85.44% (P3=C3/T3) 84.39% (P4=D1/T4) Overall accuracy: 90.45%; Kappa statistic: 0.869
Table 6. Confusion matrix of the land cover classification result (Area2）
View tableView in ArticleTable 6. Confusion matrix of the land cover classification result (Area2）
Classification data Reference data Total row User’s ccuracy Roads Grass Trees Buildings Roads 53 974 11 422 1 130 695 67 221 80.29% Grass 9 165 150 101 6 712 2 175 168 153 89.26% Trees 239 619 93 484 2 095 96 437 96.94% Buildings 282 249 5 222 35 435 41 188 86.03% Total column 63 660 162 391 106 548 40 400 372 999 Producer’s accuracy 84.78% 92.43% 87.74% 87.71% Overall accuracy: 89.89%; Kappa statistic: 0.853
Table 7. Accuracy comparison between the proposed algorithm and the other classical algorithms
View tableView in ArticleTable 7. Accuracy comparison between the proposed algorithm and the other classical algorithms
Authors Algorithm principle Features OA Kappa Our paper Step-by-step separation Spatial；NDRI 90.17% 0.861 Fernandez-Diaz et al[ 16 ]Mahalanobis distance Five structural; three intensity images 82.33% 0.77 Chen et al[ 13 ]SVM；k-NN re- classification Spectral reflectance of four channels; five vegetation indexes; neighborhood spatial 87.19% N/A Fernandez- Diaz et al[ 16 ]Maximum likelihood Five structural; two intensity images 90.22% 0.870 Zou et al[ 20 ]Decision tree Pseudo normalized difference vegetation index; ratio of green; ratio of returns counts; difference of elevation between maximum elevation of first returns and minimum elevation of last returns 91.63% 0.895 Chen et al[ 13 ]Random forest 7 spectral features; 11 geometric features 93.00% N/A Wang and Gu[ 9 ]SVM Spatial location; spectral; neighborhood geometric; spectral structures; geometric-spectral 94.76% 0.935
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Liying Wang, Ze You, Ji Wu, Mahamadou CAMARA. Airborne MS-LiDAR data classification by combining NDRI features and spatial correlation[J]. Infrared and Laser Engineering, 2023, 52(2): 20220376
Paper Information
Category: Image processing
Received: May. 31, 2022
Accepted: --
Published Online: Mar. 13, 2023
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