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作物学报 ›› 2018, Vol. 44 ›› Issue (04): 569-580.doi: 10.3724/SP.J.1006.2018.00569

• 耕作栽培·生理生化 • 上一篇    下一篇

随机森林方法在玉米-大豆精细识别中的应用

王利民(), 刘佳, 杨玲波, 杨福刚, 富长虹   

  1. 中国农业科学院农业资源与农业区划研究所, 北京 100081
  • 收稿日期:2017-05-08 接受日期:2018-01-08 出版日期:2018-01-26 网络出版日期:2018-01-26
  • 作者简介:

    wanglimin01@caas.cn

  • 基金资助:
    本研究由国家“十二五”科技重大专项(09-Y30B03-9001-13/15)和国家重点研发计划项目(2016YFD0300603)资助

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 Published:2018-01-26 Published online: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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摘要：

研究基于遥感影像的作物精确识别技术方法, 对获取作物分布信息具有重要意义。随机森林分类(random forest classification, RFC)是机器学习的一种, 本文使用Landsat-8 OLI卫星影像数据, 针对研究区内的大豆、玉米和其他地物等3种主要作物类型, 系统比较了该方法与较为成熟的最大似然分类(maximum likelihood classification, MLC)、支持向量机分类(support vector machine, SVM)方法的分类精度。结果表明, MLC、SVM、RFC的总体分类精度分别为91.68%、91.49%、94.32%, Kappa系数分别为0.87、0.87、0.91, RFC方法作物识别精度比MLC和SVM分类显著提升。对原始7波段影像进行主成分变换(principal component analysis, PCA), 提取前4个主成分分量, 同时计算归一化植被指数(normalized difference vegetation index, NDVI)和归一化水体指数(normalized difference water index, NDWI), 将6个额外辅助特征波段叠加到原始7个波段影像上进行再次分类, MLC和SVM方法作物识别精度未有提升, RFC方法总体精度提高了1.49个百分点, Kappa系数提高0.03, 精度提升幅度有限, 主要原因是6个辅助波段在类型识别中作用较小。在分类耗时上, MLC、SVM、RFC分别为145 s、11 000 s、1800 s, 表明随机森林分类具有最好的分类精度和适中的耗时。综合评价后, 随机森林分类方法在进行大豆-玉米精细识别中具有较大优势, 具有业务应用的潜力。

关键词: Landsat-8, 随机森林, 玉米, 大豆, 遥感, 识别能力

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

图1

研究区地理位置"

表1

Landsat 8 OLI影像各波段辐射定标系数"

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

图2

研究区Landsat 8 OLI影像及样方分布 a: Landsat 8原始影像及样方分布; b: 样方原图; c: 样方分类结果。"

图3

研究区主要地物类别光谱曲线"

图4

研究技术流程"

图5

基于RapidEye影像的目视解译结果 a: Rapideye影像(5/4/3波段); b: Rapideye影像的目视解译结果。"

图6

基于原始影像3种方法分类结果 a: 最大似然分类结果; b: 支持向量机分类结果; c: 随机森林分类结果; d: 最大似然分类结果局部; e: 支持向量机分类结果局部; f: 随机森林分类结果局部。"

表2

基于原始影像3种分类方法的混淆矩阵"

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

图7

增加辅助特征后的3种方法分类结果 a: 最大似然分类结果; b: 支持向量机分类结果; c: 随机森林分类结果; d: 最大似然分类结果局部; e: 支持向量机分类结果局部; f: 随机森林分类结果局部。"

表3

增加辅助特征波段后的3种分类方法分类结果混淆矩阵"

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

图8

增加辅助特征前后各特征变量的重要性 a: 增加辅助特征前各波段重要性; b: 增加辅助特征后各波段重要性。"

表4

3种分类方式作物分类提取耗费时间"

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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