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Acta Agronomica Sinica ›› 2018, Vol. 44 ›› Issue (04): 569-580.doi: 10.3724/SP.J.1006.2018.00569

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Application of Random Forest Method in Maize-soybean Accurate Identification

Li-Min WANG(), Jia LIU, Ling-Bo YANG, Fu-Gang YANG, Chang-Hong FU   

  1. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • Received:2017-05-08 Accepted:2018-01-08 Online:2018-01-26 Published:2018-01-26
  • Supported by:
    This study was supported by the National Science and Technology Major Project (09-Y30B03-9001-13/15) and the National Key Research and Development Program of China (2016YFD0300603).

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Abstract:

It is very important to obtain the crop identification information based on remote sensing image. Remote sensing images have the advantages of high efficiency, high accuracy, low costs, and wide monitoring scope. Applying remote sensing images in maize-soybean accurate identification and planting area evaluation can give full play to the advantages of remote sensing images. Random forest classification (RFC) is a new classification method, a type of machine learning. Currently, there are very few studies on crop classification based on RFC. In order to evaluate the potential of the method on maize-soybean crop accurate identification, the paper conducted classification of major crops of soybean, maize, and other ground objects. Utilizing Landsat-8 OLI satellite image data, and three methods including maximum likelihood classification (MLC), support vector machine (SVM), and random forest classification (RFC). The overall classification accuracies of MLC, SVM, and RFC were 91.68%, 91.49%, and 94.32%, with their kappa coefficients of 0.87, 0.87, and 0.91, respectively, showing that RFC is better. The principal component analysis (PCA) was made on original seven wave band images, and the first four wave bands of the major components were extracted. Meanwhile, the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were calculated; six additional supporting characteristic wave bands were overlapped on original seven wave band images, and the classifications with MLC, SVM, and RFC were conducted again. After adding characteristic wave bands, crop identification accuracies by MLC and SVM methods were not improved. The accuracy of RFC method was increased slightly with overall accuracy of 95.81% increasing by 1.49 percent, and Kappa coefficient of 0.94 increasing by 0.03, showing accuracy slightly increased, and limited improvement effect. Near-infrared band and two short infrared wave bands were most important, while newly added wave band was not significant for soybean-maize identification, showing the limited improvement effect of supporting wave band. SVM had the longest time spent on classification, with about 11 000 s; MLC the least, only 145 s; and RFC about 1800 s. It indicates that SVM doesn’t have any advantages in both accuracy and time-consumed, however, MLC can quickly get the classification results, and RFC has the highest classification accuracy with moderate time consumed. In conclusion, RFC has greater advantage in soybean-maize accurate identification, and is suitable to be widely applied in the operation of regional agriculture remote sensing monitoring crop area extraction.

Key words: Landsat-8, random forest, maize, soybean, remote sensing, identification capacity

Fig. 1

Location of study area"

Table 1

Radiometric calibration coefficient of Landsat 8 OLI image"

波段
Band		 定标斜率
Gain		 定标截距
Bias
海岸蓝波段 Coastal aerosol 0.01298 -64.89967
蓝 Blue 0.013292 -66.45805
绿 Green 0.012248 -61.24053
红 Red 0.010328 -51.64146
近红外 Near infrared 0.0063204 -31.602
短波红外1 SWIR 1 0.0015718 -7.85913
短波红外2 SWIR 2 0.00052979 -2.64895

Fig. 2

Landsat 8 OLI image and distribution of ground sample in study areaa: Landsat 8 image and distribution of ground samples; b: original image of sample; c: classification of sample."

Fig. 3

Spectral curves of main ground objects in study area"

Fig. 4

Technical flow chart of the study"

Fig. 5

Visual interpreting result based on the RapidEye imagea: Rapideye image (5/4/3 band); b: Result of manual visual interpretation based on RapidEye image."

Fig. 6

Classification results by three methods based on original imagea: maximum likelihood classification result; b: support vector machine classification result; c: random forest classification result; d: part of maximum likelihood classification result; e: part of support vector machine classification result; f: part of random forest classification result."

Table 2

Confusion matrix of three classification methods based on original image"

作物
Crop		 分类方法
Method		 大豆
Soybean (pixel)		 玉米
Maize (pixel)		 其他
Other (pixel)		 总计
Total (pixel)		 制图精度
Mapping accuracy (%)
大豆
Soybean		 MLC 864849 15393 56545 936787 91.03
SVM 878082 7498 66202 951782 92.42
RFC 911841 9285 65006 986132 95.98
玉米
Maize		 MLC 21443 1134475 76472 1232390 92.38
SVM 35185 1140200 93000 1268385 92.85
RFC 4158 1186858 68423 1259439 96.65
其他
Other		 MLC 63758 78172 1436164 1578094 91.52
SVM 36783 80342 1409979 1527104 89.85
RFC 34051 31897 1435752 1501700 91.50
总计 Total (pixel) 950050 1228040 1569181 3747271
用户精度
User accuracy (%)		 MLC 92.32 92.05 91.01
SVM 92.26 89.89 92.33
RFC 92.47 94.24 95.61
总体精度
Overall accuracy (%)		 MLC 91.68 Kappa系数
Kappa coefficient		 MLC 0.87
SVM 91.49 SVM 0.87
RFC 94.32 RFC 0.91

Fig. 7

Classification results of three methods based on the stacked imagea: maximum likelihood classification result; b: support vector machine classification result; c: random forest classification result; d: part of maximum likelihood classification result; e: part of support vector machine classification result; f: part of random forest classification result."

Table 3

Confusion matrix of three methods based on the stacked image"

作物
Crop		 分类方法
Method		 大豆
Soybean (pixel)		 玉米
Maize (pixel)		 其他
Other (pixel)		 总计
Total (pixel)		 制图精度
Mapping accuracy (%)
大豆
Soybean		 MLC 857118 3824 56780 917722 90.22
SVM 885153 7611 66296 959060 93.17
RFC 934124 2276 55865 992265 98.32
玉米
Maize		 MLC 56343 1201206 167854 1425403 97.81
SVM 31759 1145348 87264 1264371 93.27
RFC 2053 1205962 63085 1271100 98.20
其他
Other		 MLC 36589 23010 1344547 1404146 85.68
SVM 33138 75081 1415621 1523840 90.21
RFC 13873 19802 1450231 1483906 92.42
总计 Total (pixel) 950050 1228040 1569181 3747271
用户精度
User accuracy (%)		 MLC 93.40 84.27 95.76
SVM 92.29 90.59 92.90
RFC 94.14 94.88 97.73
总体精度
Overall accuracy (%)		 MLC 90.81 Kappa系数
Kappa coefficient		 MLC 0.86
SVM 91.96 SVM 0.88
RFC 95.81 RFC 0.94

Fig. 8

Variable importance before and after adding auxiliary featuresa: variable importance before adding auxiliary features; b: variable importance after adding auxiliary features."

Table 4

Classification time of the three methods"

分类方式
Classification method		 分类时间
Time cost (s)
最大似然分类MLC 145
支持向量机SVM 11000
随机森林分类RFC 1800
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