Infrared and Laser Engineering, Volume. 50, Issue 5, 20200318(2021)
Crop classification of modern agricultural park based on time-series Sentinel-2 images
Figures & Tables(12)
Fig. 1. Geographical location and sample distributions of the study area
Fig. 2. Temporal changes of NDVI (a), RVI (b), EVI (c) and Ref (NIR) (d) for main crops
Fig. 4. Crop classification results using Decision Tree (a), Random Forest (b), Support Vector Machine (c), Maximum Likelihood (d)
Table 1. Growth period of five crops in the study area
View tableView in ArticleTable 1. Growth period of five crops in the study area
Type May June July August September October E M L E M L E M L E M L E M L E M Note: E means the early 10 days of a month,M is the middle 10 days，and L represents the lately 10 days. Rice Sowing Tillering Heading Filling Maturity Soybean Sowing Seeding Flowering Podding Filling Maturity Stevia Transplanting Branching Flowering Maturity Corn Sowing Seeding Jointing Tasseling Filling Maturity Dry rice Sowing Tillering Heading Filling Maturity
Table 2. Data lists of Sentinel-2 images
View tableView in ArticleTable 2. Data lists of Sentinel-2 images
Data time Sensor Quality 2019-05-22 Sentinel-2A Best 2019-06-11 Sentinel-2A Good 2019-06-21 Sentinel-2A Best 2019-07-01 Sentinel-2A Good 2019-08-15 Sentinel-2B Best 2019-08-30 Sentinel-2A Best 2019-09-14 Sentinel-2B Best 2019-09-24 Sentinel-2B Best 2019-10-04 Sentinel-2B Best
Table 3. Spectral bands of the Sentinel-2 sensors (S2A & S2B)
View tableView in ArticleTable 3. Spectral bands of the Sentinel-2 sensors (S2A & S2B)
Sentinel-2 bands Wavelength/μm Reflection/m Band1-Coastal aerosol 0.443 60 Band2-Blue 0.490 10 Band3-Green 0.560 10 Band4-Red 0.665 10 Band5-Vegetation red edge 0.705 20 Band6-Vegetation red edge 0.740 20 Band7-Vegetation red edge 0.783 20 Band8-NIR 0.842 10 Band8A-Vegetation red edge 0.865 20 Band9-Water vapour 0.945 60 Band10-SWIR-Cirrus 1.375 60 Band11-SWIR1 1.610 20 Band12-SWIR2 2.190 20
Table 4. Classification indicators used in the study
View tableView in ArticleTable 4. Classification indicators used in the study
Indicator Description Source Notes: In the formula, $ {\mathrm{\rho }}_{\mathrm{N}\mathrm{I}\mathrm{R}} $ $ {\mathrm{\rho }}_{\mathrm{R}\mathrm{E}\mathrm{D}} $ $ {\mathrm{\rho }}_{\mathrm{B}\mathrm{L}\mathrm{U}\mathrm{E}} $ Normalized Difference Vegetation Index(NDVI) ${\rm NDVI} = \dfrac{ { {\rho _{\rm NIR} } - {\rho _{\rm RED} } } }{ { {\rho _{\rm NIR} } + {\rho _{\rm RED} } } }$ Ref.[15] Ratio Vegetation Index(RVI) ${\rm RVI} = \dfrac{ { {\rho _{\rm NIR} } } }{ { {\rho _{\rm RED} } } }$ Ref. [16] Enhanced Vegetation Index(EVI) ${\rm EVI} = 2.5×\dfrac{ { {\rho _{\rm NIR} } - {\rho _{\rm RED} } } }{ { {\rho _{\rm NIR} } + 6.0×{\rho _{\rm RED} } - 7.5×{\rho _{\rm BLUE} } + L} }$ Ref. [17-18] Near Infrared Ray(Ref(NIR)) The reflection of Band-8 in Tab.3 Ref. [19]
Table 5. Formulas of accuracy evaluation
View tableView in ArticleTable 5. Formulas of accuracy evaluation
Type Calculation formula Notes: where k represents the number of rows and columns of the confusion matrix, Xii represents the value on the diagonal of the confusion matrix that is the number of pixels correctly classified,N represents the total number of pixels verified, Xi represents the i row of the confusion matrix. The sum of elements, Xj represents the sum of elements in the j column of the confusion matrix. Mapping accuracy $\mathrm{P}\mathrm{A}=\dfrac{ {X}_{ii} }{ {X}_{j} }×100\%$ User accuracy $\mathrm{U}\mathrm{A}=\dfrac{ {X}_{ii} }{ {X}_{i} }×100\%$ Overall accuracy $\mathrm{O}\mathrm{A}=\displaystyle\sum _{i=1}^{k}\dfrac{ {X}_{ii} }{N}×100\%$ Kappa coefficient ${{K} } = \dfrac{ {N\displaystyle\sum\nolimits_{i = 1}^k { {X_{ii} } } - \sum\nolimits_{i = 1}^k { {X_i}{X_j} } } }{ { {N^2} - \displaystyle\sum\nolimits_{i = 1}^k { {X_i}{X_j} } } }$
Table 6. Confusion matrix result of Decision Tree
View tableView in ArticleTable 6. Confusion matrix result of Decision Tree
Type Soybean Rice Stevia Corn Dry rice Total User accuracy Soybean 3288 4 0 0 320 3612 91.03% Rice 0 5680 16 33 176 5905 96.19% Stevia 0 0 2039 0 59 2098 97.19% Corn 0 1 0 8451 221 8673 97.44% Dry rice 8 9 147 1055 4820 6039 79.81% Mapping accuracy 99.76% 99.75% 92.60% 88.57% 86.13%
Table 7. Confusion matrix result of Random Forest
View tableView in ArticleTable 7. Confusion matrix result of Random Forest
Type Soybean Rice Stevia Corn Dry rice Total User accuracy Soybean 3294 0 0 0 393 3687 89.34% Rice 0 5692 19 183 216 6110 93.16% Stevia 0 0 1993 0 8 2001 99.60% Corn 0 0 2 9301 41 9344 99.54% Dry rice 4 2 142 102 4938 5188 95.18% Mapping accuracy 99.88% 99.96% 92.44% 97.03% 88.24%
Table 8. Overall accuracy estimation and Kappa coefficient of classification based on each method
View tableView in ArticleTable 8. Overall accuracy estimation and Kappa coefficient of classification based on each method
Classification method Overall accuracy Kappa coefficient Maximum Likelihood 86.5% 0.823 Support Vector Machine 91.6% 0.890 Decision Tree 92.2% 0.897 Random Forest 95.8% 0.944
Tools
Get Citation
Copy Citation Text
Dongyan Zhang, Zhen Dai, Xingang Xu, Guijun Yang, Yang Meng, Haikuan Feng, Qi Hong, Fei Jiang. Crop classification of modern agricultural park based on time-series Sentinel-2 images[J]. Infrared and Laser Engineering, 2021, 50(5): 20200318
Paper Information
Category: Spectroscopy
Received: Dec. 7, 2020
Accepted: --
Published Online: Aug. 13, 2021
The Author Email: Xu Xingang (xxgpaper@126.com)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20