Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data
Abstract
:1. Introduction
2. Study Area and Data Sources
2.1. Overview of the Study Area
2.2. Data Source
2.2.1. Remote Sensing Data
2.2.2. Sample Data
3. Methods
3.1. Spectral Feature Analysis
3.1.1. ABS Method
3.1.2. JBh Distance
3.2. Texture Feature Extraction
3.3. Red Edge Index Analysis
3.4. Classification Scheme and Accuracy Evaluation
4. Results
4.1. Red Edge Spectral Analysis Results
4.1.1. Spectral Analysis Based on Information Content
4.1.2. Spectral Analysis Based on Separability Distance
4.2. Feature Importance Evaluation Results of Red Edge Indices
4.3. Classification Results of Red Edge Features
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Band Number | Band Name | Central Wavelength (nm) | Wavelength Range (nm) | Calibration Coefficient in 2019 | Spatial Resolution (m) |
---|---|---|---|---|---|
B1 | Blue (B) | 485 | 450–520 | 0.0705 | 16 |
B2 | Green (G) | 555 | 520–590 | 0.0567 | |
B3 | Red (R) | 660 | 630–690 | 0.0516 | |
B4 | Near-infrared (NIR) | 830 | 770–890 | 0.0322 | |
B5 | Red edge 1 (RE1) | 710 | 690–730 | 0.0532 | |
B6 | Red edge 2 (RE2) | 750 | 730–770 | 0.0453 | |
B7 | Purple (P) | 425 | 400–450 | 0.0786 | |
B8 | Yellow (Y) | 610 | 590–630 | 0.0585 |
Type | Training Samples | Validation Sample | Total | ||
---|---|---|---|---|---|
Number of Polygons | Number of Pixels | Number of Polygons | Number of Pixels | Number of Polygons | |
Summer maize | 92 | 5855 | 92 | 4690 | 184 |
Spring maize | 50 | 3434 | 50 | 3055 | 100 |
Cotton | 35 | 1788 | 35 | 1721 | 70 |
Minor crops | 30 | 415 | 30 | 388 | 60 |
Greenhouses | 15 | 396 | 15 | 391 | 30 |
Orchards | 25 | 871 | 25 | 855 | 50 |
Woods | 20 | 1234 | 20 | 881 | 40 |
Cities and towns | 24 | 5164 | 24 | 4609 | 48 |
Water bodies | 16 | 3143 | 16 | 3465 | 32 |
Red Edge Indices | Calculation Formula (GF-6 WFV) |
---|---|
Normalized Difference Red Edge (NDRE) [47] | |
Normalized Difference Vegetation Index red edge 1 (NDVIre1) [48] | |
Normalized Difference Vegetation Index red edge 2 (NDVIre2) [48] | |
Chlorophyll Index red edge 1 (CIre1) [49] | |
Chlorophyll Index red edge 2 (CIre2) [50] | |
Modified Chlorophyll Absorption Ratio Index 1 (MCARI1) [51] | |
Modified Chlorophyll Absorption Ratio Index 2 (MCARI2) [52] | |
Transformed Chlorophyll Absorption Reflectance Index 1 (TCARI1) [52] | |
Transformed Chlorophyll Absorption Reflectance Index 2 (TCARI2) [51] | |
MERIS Terrestrial Chlorophyll Index (MTCI) [53] |
Classification Schemes Classification Features | Scheme A-1 | Scheme A-2 | Scheme A-3 | Scheme A-4 | Scheme B-1 | Scheme B-2 | Scheme B-3 | Scheme B-4 | Scheme C-1 | Scheme C-2 | Scheme C-3 | Scheme C-4 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Traditional four bands (R,G,B,NIR) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Red edge spectral features | Red edge 710 | ✓ | ✓ | ||||||||||
Red edge 750 | ✓ | ✓ | |||||||||||
Red edge texture features | Red edge texture 710 | ✓ | ✓ | ||||||||||
Red edge texture 750 | ✓ | ✓ | |||||||||||
Near-infrared texture | ✓ | ||||||||||||
Red edge index features | Optimal red edge index 1 | ✓ | |||||||||||
Optimal red edge index 2 | ✓ | ||||||||||||
Optimal red edge index 3 | ✓ | ||||||||||||
Optimal red edge index 4 | ✓ |
Band Name | Band Order | ABS index | Ranking |
---|---|---|---|
Purple (P) | 1 | 0 | 7 |
Blue (B) | 2 | 383.4 | 6 |
Green (G) | 3 | 474.8 | 5 |
Red (R) | 4 | 556.3 | 4 |
Near-infrared (NIR) | 5 | 2418.7 | 1 |
Red edge 710 (RE1) | 6 | 728.3 | 3 |
Red edge 750 (RE2) | 7 | 1732.5 | 2 |
Yellow (Y) | 8 | 0 | 7 |
Red Edge Indices | F Value (SDA) | MDG (RF) |
---|---|---|
CIre1 | 237.268 | 0.111 |
CIre2 | 46.414 | 0.091 |
MCARI1 | 64.475 | 0.090 |
MCARI2 | 54.886 | 0.098 |
MTCI | 227.010 | 0.137 |
NDRE | 387.008 | 0.108 |
NDVIre1 | 337.605 | 0.110 |
NDVIre2 | 52.812 | 0.092 |
TCARI1 | 115.514 | 0.104 |
TCARI2 | 9.566 | 0.059 |
Class | Scheme A-1 | Scheme A-2 | Scheme A-3 | Scheme A-4 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PA% | UA% | F1% | PA% | UA% | F1% | PA% | UA% | F1% | PA% | UA% | F1% | |
Summer maize | 87.31 | 74.84 | 80.59 | 91.15 | 77.98 | 84.05 | 88.61 | 77.29 | 82.56 | 91.22 | 78.34 | 84.29 |
Spring maize | 48.35 | 58.38 | 52.89 | 55.61 | 68.92 | 61.55 | 53.39 | 64.16 | 58.28 | 56.20 | 71.66 | 62.99 |
Cotton | 84.83 | 83.33 | 84.07 | 87.68 | 85.21 | 86.42 | 87.91 | 85.05 | 86.45 | 91.69 | 86.99 | 89.27 |
Minor crops | 30.93 | 60.30 | 40.88 | 29.64 | 47.92 | 36.62 | 29.38 | 57.29 | 38.84 | 27.32 | 49.53 | 35.21 |
Greenhouses | 75.70 | 86.80 | 80.87 | 75.70 | 88.89 | 81.76 | 75.45 | 90.77 | 82.40 | 75.96 | 91.67 | 83.08 |
Orchards | 47.72 | 54.91 | 51.06 | 61.05 | 73.31 | 66.62 | 57.54 | 65.51 | 61.26 | 61.17 | 72.14 | 66.20 |
Woods | 75.60 | 75.94 | 75.76 | 82.41 | 80.94 | 81.66 | 79.80 | 76.08 | 77.89 | 83.20 | 77.24 | 80.11 |
Cities and towns | 98.42 | 74.08 | 84.53 | 98.72 | 75.86 | 85.79 | 98.85 | 74.65 | 85.06 | 98.78 | 79.14 | 87.88 |
Water bodies | 56.94 | 97.77 | 71.96 | 61.15 | 98.24 | 75.37 | 58.07 | 97.91 | 72.90 | 68.40 | 97.97 | 80.56 |
OA (%) | 74.95 | 78.84 | 77.15 | 80.55 | ||||||||
Kappa coefficient | 0.6937 | 0.7414 | 0.7208 | 0.7627 |
Class | Scheme B-1 | Scheme B-2 | Scheme B-3 | Scheme B-4 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PA% | UA% | F1% | PA% | UA% | F1% | PA% | UA% | F1% | PA% | UA% | F1% | |
Summer maize | 90.64 | 77.87 | 83.77 | 88.17 | 77.17 | 82.30 | 90.15 | 78.57 | 83.96 | 88.06 | 75.15 | 81.09 |
Spring maize | 53.85 | 67.01 | 59.71 | 55.35 | 65.42 | 59.96 | 56.01 | 69.05 | 61.85 | 47.89 | 60.06 | 53.29 |
Cotton | 89.25 | 85.52 | 87.34 | 87.45 | 86.44 | 86.94 | 91.34 | 86.52 | 88.86 | 86.23 | 83.94 | 85.07 |
Minor crops | 27.32 | 50.72 | 35.51 | 28.87 | 51.85 | 37.09 | 27.32 | 53.81 | 36.24 | 30.67 | 57.49 | 40.00 |
Greenhouses | 75.19 | 80.33 | 77.67 | 77.24 | 83.66 | 80.32 | 78.52 | 80.79 | 79.64 | 72.63 | 81.61 | 76.86 |
Orchards | 58.95 | 64.95 | 61.80 | 50.29 | 59.23 | 54.39 | 53.33 | 62.72 | 57.65 | 44.33 | 53.91 | 48.65 |
Woods | 82.52 | 82.33 | 82.42 | 78.09 | 73.66 | 75.81 | 81.27 | 74.12 | 77.53 | 74.91 | 68.75 | 71.69 |
Cities and towns | 96.92 | 97.77 | 97.34 | 97.94 | 77.71 | 86.66 | 97.03 | 98.07 | 97.55 | 98.24 | 81.09 | 88.84 |
Water bodies | 99.83 | 97.66 | 98.73 | 65.11 | 97.03 | 77.93 | 99.83 | 97.46 | 98.63 | 72.38 | 98.24 | 83.35 |
OA (%) | 84.71 | 77.95 | 84.90 | 77.56 | ||||||||
Kappa coefficient | 0.8142 | 0.731 | 0.8167 | 0.7263 |
Class | Scheme C-1 | Scheme C-2 | Scheme C-3 | Scheme C-4 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PA% | UA% | F1% | PA% | UA% | F1% | PA% | UA% | F1% | PA% | UA% | F1% | |
Summer maize | 90.94 | 77.64 | 83.76 | 91.07 | 76.95 | 83.42 | 89.49 | 76.73 | 82.62 | 90.75 | 77.64 | 83.68 |
Spring maize | 55.29 | 69.39 | 61.54 | 52.83 | 69.27 | 59.94 | 53.62 | 67.60 | 59.80 | 55.58 | 69.19 | 61.64 |
Cotton | 87.57 | 86.71 | 87.14 | 90.35 | 84.69 | 87.43 | 90.94 | 88.17 | 89.53 | 88.32 | 87.16 | 87.74 |
Minor crops | 30.15 | 49.79 | 37.56 | 33.51 | 48.51 | 39.64 | 28.61 | 46.64 | 35.46 | 30.67 | 51.07 | 38.32 |
Greenhouses | 75.70 | 86.55 | 80.76 | 74.17 | 90.06 | 81.35 | 78.26 | 90.00 | 83.72 | 73.40 | 87.50 | 79.83 |
Orchards | 64.09 | 74.25 | 68.79 | 57.66 | 73.58 | 64.65 | 61.52 | 67.44 | 64.34 | 63.27 | 73.51 | 68.01 |
Woods | 83.54 | 80.00 | 81.73 | 84.22 | 79.96 | 82.03 | 79.00 | 79.82 | 79.41 | 83.43 | 80.24 | 81.80 |
Cities and towns | 98.68 | 73.21 | 84.06 | 98.31 | 76.61 | 86.11 | 98.74 | 73.81 | 84.47 | 98.76 | 74.44 | 84.89 |
Water bodies | 54.86 | 97.84 | 70.30 | 62.97 | 97.54 | 76.53 | 56.25 | 97.89 | 71.45 | 57.89 | 98.00 | 72.78 |
OA (%) | 77.82 | 78.82 | 77.48 | 78.35 | ||||||||
Kappa coefficient | 0.7288 | 0.7413 | 0.7248 | 0.7354 |
Analysis | Scheme 1 | Scheme 2 | f12 | f21 | χ2 | p |
---|---|---|---|---|---|---|
1 | A-1 | A-2 | 6 | 786 | 768.18 | <0.0001% |
2 | A-1 | A-3 | 8 | 449 | 425.56 | <0.0001% |
3 | A-1 | A-4 | 15 | 1139 | 1094.78 | <0.0001% |
4 | A-2 | A-3 | 350 | 11 | 318.34 | <0.0001% |
5 | A-2 | A-4 | 10 | 354 | 325.09 | <0.0001% |
6 | A-3 | A-4 | 12 | 695 | 659.81 | <0.0001% |
7 | B-1 | B-2 | 1464 | 108 | 1169.68 | <0.0001% |
8 | B-1 | B-3 | 83 | 121 | 7.08 | 0.8% |
9 | B-1 | B-4 | 1509 | 75 | 1583.99 | <0.0001% |
10 | B-2 | B-3 | 49 | 1143 | 1302.44 | <0.0001% |
11 | B-2 | B-4 | 352 | 274 | 9.72 | 0.2% |
12 | B-3 | B-4 | 1542 | 70 | 1344.16 | <0.0001% |
13 | C-1 | C-2 | 154 | 355 | 79.37 | <0.0001% |
14 | C-1 | C-3 | 188 | 120 | 15.01 | 0.01% |
15 | C-1 | C-4 | 27 | 134 | 71.11 | <0.0001% |
16 | C-2 | C-3 | 373 | 104 | 151.70 | <0.0001% |
17 | C-2 | C-4 | 248 | 154 | 21.98 | 0.0003% |
18 | C-3 | C-4 | 65 | 240 | 100.41 | <0.0001% |
19 | A-1 | B-3 | 79 | 2075 | 1849.59 | <0.0001% |
20 | A-1 | C-2 | 12 | 789 | 753.72 | <0.0001% |
21 | A-4 | B-3 | 228 | 1100 | 572.58 | <0.0001% |
22 | A-4 | C-2 | 381 | 34 | 293.06 | <0.0001% |
23 | B-3 | C-2 | 1409 | 190 | 929.31 | <0.0001% |
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Kang, Y.; Meng, Q.; Liu, M.; Zou, Y.; Wang, X. Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data. Sensors 2021, 21, 4328. https://doi.org/10.3390/s21134328
Kang Y, Meng Q, Liu M, Zou Y, Wang X. Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data. Sensors. 2021; 21(13):4328. https://doi.org/10.3390/s21134328
Chicago/Turabian StyleKang, Yupeng, Qingyan Meng, Miao Liu, Youfeng Zou, and Xuemiao Wang. 2021. "Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data" Sensors 21, no. 13: 4328. https://doi.org/10.3390/s21134328
APA StyleKang, Y., Meng, Q., Liu, M., Zou, Y., & Wang, X. (2021). Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data. Sensors, 21(13), 4328. https://doi.org/10.3390/s21134328