Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery
<p>Paddy rice distribution in monsoon Asia with the boundary of Jiangsu Province overlaid. The map is derived from the International Rice Research Institute (IRRI) at the pixel size of 500 m [<a href="#B37-remotesensing-10-00546" class="html-bibr">37</a>].</p> "> Figure 2
<p>Location of the selected case counties in Jiangsu Province for assessing the performance of the four classification methods.</p> "> Figure 3
<p>Year of Landsat OLI imagery used in this study.</p> "> Figure 4
<p>Spatial distribution of field survey points in 2016 across Jiangsu Province.</p> "> Figure 5
<p>A conceptual illustration of the OCSVC method. The characters “+1” and “−1” denote the target class and the outlier class, respectively.</p> "> Figure 6
<p>A conceptual illustration of MCSVC method. Solid points and hollow points represent two different classes.</p> "> Figure 7
<p>A conceptual illustration of the DTC method.</p> "> Figure 8
<p>Rice classification maps of 18 counties in Jiangsu Province, China with the methods (<b>A</b>) MCSVC; (<b>B</b>) DTC; (<b>C</b>) VIT; and (<b>D</b>) OCSVC.</p> "> Figure 9
<p>Maps of county-level overall accuracy for the classifications with the methods (<b>A</b>) MCSVC; (<b>B</b>) DTC; (<b>C</b>) VIT; and (<b>D</b>) OCSVC.</p> "> Figure 10
<p>Comparison of training samples used by MCC and OCC methods. The training samples were counted as the number of polygons of interest defined for the classifiers. MCC stands for MCSVC and DTC and OCC stands for VIT and OCSVC.</p> "> Figure 11
<p>The classification efficiency of MCSVC, DTC and OCSVC as measured by processing time for Norther, Central, and Southern Jiangsu. Four groups of tests were designed by reorganizing all the 14 classes (<b>A</b>) of training samples into four classes (<b>B</b>), three classes (<b>C</b>), and two classes (<b>D</b>).</p> "> Figure 12
<p>Comparisons of the reported area of 2016 from the agricultural statistics department of Jiangsu and classification area of the 18 case counties. Blue, yellow, purple, and red data points represent classifications obtained with MCSVC (<b>A</b>), DTC (<b>B</b>), VIT (<b>C</b>), and OCSVC (<b>D</b>), respectively. Solid trend lines correspond to the fitting for all case counties, while dashed ones correspond to the fittings excluding the circled data points. The black solid lines represent the 1:1 lines.</p> "> Figure 13
<p>The provincial map of Jiangsu for the rice distribution of 2016 obtained using the OCSVC method.</p> "> Figure 14
<p>(<b>A</b>) Rice classification with OCSVC for the three representative county sites in Jiangsu Province, China. Subfigures (<b>B</b>–<b>D</b>) are Landsat image composites (R: NIR, G: SWIR1, B: Red) for the sites in Shuyang (Northern Jiangsu), Gaoyou (Central Jiangsu), and Kunshan (Southern Jiangsu) at the peak growth stage of rice. Subfigures (<b>E</b>–<b>G</b>) are classification maps for the three sites. All subfigures are displayed at the same scale (18 km × 18 km). The center coordinates of those three sites are (34°18′39.95″N, 118°59′36.98″E), (32°53′38.77″N, 119°34′17.26″E), and (31°13′37.30″N, 120°55′07.19″E), respectively.</p> "> Figure 15
<p>Comparison of the reported area from the agricultural statistics department of Jiangsu and the identified area from the OCSVC-based rice classification map of 2016 at the county-level. The dashed line represents the 1:1 line.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Imagery Data
2.3. Field Data
2.4. Classification Methods
2.5. Evaluation of Classification Accuracy
2.6. Evaluation of Classification Efficiency
2.7. Assessment of Rice Acreage Estimation
3. Results
3.1. Classification Accuracy
3.2. Efficiency Comparison between OCSVC and Other Classification Methods
3.3. County-Level Estimation of Rice Acreage
3.4. Generation of the 2016 Provincial Rice Map of Jiangsu
4. Discussion
4.1. Misclassifications and Omissions in OCSVC Classification
4.2. Advantages of OCSVC
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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April | May | June | July | August | September | October | |
---|---|---|---|---|---|---|---|
Rice | Sowing | Emergence | Senescence | Harvest | |||
Summer corn | Sowing & Emergence | Senescence | Harvest | ||||
Soybean | Sowing | Emergence | Senescence | Harvest | |||
Peanut | Sowing & Emergence | Harvest | |||||
NO. | Path-Row | Date | Field Campaign Date | Phenological Stage |
---|---|---|---|---|
1 | 118-038 | 03/08/2015 | / | Vegetative stage (jointing) |
2 | 119-037 | 27/09/2015 | / | Ripening stage (filling) |
3 | 119-037 | 28/08/2016 | 08/10/2016 | Reproductive stage (heading) |
4 | 119-038 | 27/09/2015 | / | Ripening stage (filling) |
5 | 119-038 | 28/08/2016 | 26/09/2016 | Reproductive stage (heading) |
6 | 119-039 | 27/07/2016 | 29/09/2016 | Vegetative stage (tillering) |
7 | 120-036 | 18/09/2015 | / | Ripening stage (filling) |
8 | 120-036 | 20/09/2016 | 04/10/2016 | Ripening stage (filling) |
9 | 120-037 | 01/10/2014 | / | Ripening stage (filling) |
10 | 120-037 | 02/09/2015 | / | Reproductive stage (heading) |
11 | 120-037 | 18/09/2015 | / | Ripening stage (filling) |
12 | 120-037 | 20/09/2016 | 05/10/2016 | Ripening stage (filling) |
13 | 120-038 | 29/07/2014 | / | Vegetative stage (jointing) |
14 | 120-038 | 02/09/2015 | / | Reproductive stage (heading) |
15 | 121-036 | 25/07/2016 | 06/10/2016 | Vegetative stage (tillering) |
16 | 121-037 | 25/07/2016 | 07/10/2016 | Vegetative stage (tillering) |
17 | 122-036 | 02/09/2016 | / | Reproductive stage (heading) |
Band 2 | Band 3 | Band 4 | Band 5 | Band 6 | Band 7 | |
---|---|---|---|---|---|---|
TCT | (Blue) | (Green) | (Red) | (NIR) | (SWIR1) | (SWIR2) |
Brightness | 0.3029 | 0.2786 | 0.4733 | 0.5599 | 0.508 | 0.1872 |
Greenness | −0.2941 | −0.243 | −0.5424 | 0.7276 | 0.0713 | −0.1608 |
Wetness | 0.1511 | 0.1973 | 0.3283 | 0.3407 | −0.7117 | −0.4559 |
Training Set | Validation Set | ||||
---|---|---|---|---|---|
Class | County and Province Level | Class | County Level (All Four Methods) | Province Level (OCSVC) | |
MCC | OCC | ||||
Rice | 1332/88,381 | 1332/88,381 | Rice | 1206/64,218 | 834/44,786 |
Forest | 272/11,288 | 0 | Non-rice | 1494/77,530 | 819/45,974 |
Greenhouse vegetable | 35/688 | 0 | |||
Grass or Vegetables | 114/5391 | 0 | |||
Shrub | 13/511 | 0 | |||
Soybean | 153/4166 | 0 | |||
Corn | 59/2177 | 0 | |||
Peanut | 4/153 | 0 | |||
Tea | 5/87 | 0 | |||
Lotus | 9/886 | 0 | |||
Aquatic vegetation | 193/7732 | 0 | |||
Built-up land | 223/22,235 | 0 | |||
Water body | 280/55,894 | 0 | |||
Barren land | 37/1066 | 0 |
Classifier | Producer’s Accuracy (%) | User’s Accuracy (%) | Overall Accuracy (%) | Kappa Coefficient | OAmean (%) | OAstd (%) | CV(%) |
---|---|---|---|---|---|---|---|
MCSVC | 95.63 | 83.85 | 89.68 | 0.79 | 89.27a | 5.10 | 5.71 |
DTC | 95.52 | 87.05 | 91.53 | 0.83 | 91.25a | 4.31 | 4.73 |
VIT | 96.65 | 57.63 | 66.3 | 0.36 | 64.96b | 15.97 | 24.59 |
OCSVC | 89.87 | 90.52 | 91.15 | 0.82 | 91.36a | 2.79 | 3.05 |
Classifier | All Counties | One Excluded | ||||
---|---|---|---|---|---|---|
R2 | RMSE (km2) | Bias (km2) | R2 | RMSE (km2) | Bias (km2) | |
MCSVC | 0.75 | 139.97 | −53.54 | 0.85 | 128.87 | −72.29 |
DTC | 0.78 | 127.17 | −49.52 | 0.88 | 114.21 | −67.93 |
VIT | 0.52 | 420.43 | −275.45 | 0.58 | 432.37 | −288.03 |
OCSVC | 0.88 | 84.68 | 19.18 | 0.96 | 42.76 | 1.90 |
Class | Ground Truth Pixels | Total Classified Pixels | User Accuracy (%) | ||
---|---|---|---|---|---|
Rice | Non-rice | ||||
Classification | Rice | 39,076 | 4689 | 43,765 | 89.29 |
Non-rice | 5710 | 41,285 | 46,995 | 87.85 | |
Total validation pixels | 44,786 | 45,974 | 90,760 | ||
Producer accuracy (%) | 87.25 | 89.80 | |||
Overall accuracy (%) | 88.54 | Kappa coefficient = 0.77 |
Classifier | Location Accuracy | Area Accuracy | Efficiency | ||
---|---|---|---|---|---|
Omission | Commission | Labor Cost | Time Cost | ||
MCSVC | Lower | Low | Good | High | High |
DTC | Lower | Low | Good | High | Low |
VIT | Lower | High | Poor | Low | Negligible |
OCSVC | Low | Lower | Good | Low | Low |
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Xu, X.; Ji, X.; Jiang, J.; Yao, X.; Tian, Y.; Zhu, Y.; Cao, W.; Cao, Q.; Yang, H.; Shi, Z.; et al. Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery. Remote Sens. 2018, 10, 546. https://doi.org/10.3390/rs10040546
Xu X, Ji X, Jiang J, Yao X, Tian Y, Zhu Y, Cao W, Cao Q, Yang H, Shi Z, et al. Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery. Remote Sensing. 2018; 10(4):546. https://doi.org/10.3390/rs10040546
Chicago/Turabian StyleXu, Xinjie, Xusheng Ji, Jiale Jiang, Xia Yao, Yongchao Tian, Yan Zhu, Weixing Cao, Qiang Cao, Hongjian Yang, Zhongkui Shi, and et al. 2018. "Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery" Remote Sensing 10, no. 4: 546. https://doi.org/10.3390/rs10040546