Scientia Agricultura Sinica ›› 2019, Vol. 52 ›› Issue (6): 997-1008.doi: 10.3864/j.issn.0578-1752.2019.06.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Pixel-Based and Object-Oriented Classification of Jujube and Cotton Based on High Resolution Satellite Imagery over Alear, Xinjiang

JI XuSheng1,2,3,4,LI Xu1,5,WAN ZeFu1,2,3,4,YAO Xia1,2,3,4,ZHU Yan1,2,3,CHENG Tao1,2,3,4()   

  1. 1 National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095
    2 Jiangsu Key Laboratory for Information Agriculture, Nanjing 210095
    3 Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing 210095
    4 Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095, 5College of Information Engineering, Tarim University, Alear 843300, Xinjiang
  • Received:2018-09-19 Accepted:2019-01-17 Online:2019-03-16 Published:2019-03-22
  • Contact: Tao CHENG E-mail:tcheng@njau.edu.cn

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Abstract:

【Objective】Jujube and cotton are widely cultivated in Xinjiang. In this study, we had an insight into the planting area and distribution area of jujube and cotton quickly and accurately by crop identification based on remote sensing technology, which was helpful to policies making and crops precise management.【Method】This paper evaluated the pixel-based and object-oriented classification methods for crop mapping using several mono-temporal (date of image acquisition: May 10, 2016; September 7, 2016; and October 8, 2016) high spatial resolution images of Alear city, Xinjiang. This research involved four different machine learning algorithms, including Spectral Angle Mapping (SAM), Support Vector Machine (SVM), CART Decision Trees (DTs) and Random Forest (RF). 【Result】 The results showed that it had the highest crop classification accuracy when using the satellite images acquired on May, followed by the satellite images acquired on October. Crop classification accuracy was the lowest when using the satellite image acquired on September. In addition, compared to pixel-based classification methods, the classification accuracies were improved when using object-oriented classification methods. The classification accuracies of each period were improved by 4.83%, 7.77%, and 7.22%, respectively. The highest classification accuracy was 93.52% (May 10, 2016), 85.36% (September 7, 2016), and 88.88% (October 8, 2016), respectively.【Conclusion】 The research results suggested that using the image acquired in May, which covers cotton seedling stage, could improve crop classification accuracy for our study area. The cotton in this period was covered by plastic film, and the jujube trees showed obvious spectral characteristics of vegetation. The two crops showed different spectral characteristics in the early stage of growth. Therefore, crop classification should be executed at the early growth stage. What’s more, spectral, texture and spatial features can be combined when using object-oriented classification methods, especially the addition of texture information, so that the overall accuracy of crop classification in each period was improved (except SAM). We can delineate the field boundaries efficiently by this method, which is important for the improvement of local crop field management. Additionally, texture features were more important than spectral and spatial features. Green and red bands had a greater contribute on crop classification.

Key words: Xinjiang, high spatial resolution, satellite image, pixel-based, object-oriented, jujube, cotton, classification

Fig. 1

Location of the study area"

Table 1

Acquired satellite images of study area"

卫星
Satellite		 波段类型
Band type		 空间分辨率
Spatial resolution (m)		 波段
Band		 获取时间
Date of image acquisition
SPOT-6 全色Panchromatic 1.5 全色Panchromatic 2016-05-10
多光谱Multispectral 6 蓝Blue
绿Green
红Red
近红外Near-infrared
Pleiades-1 全色Panchromatic 0.5 全色Panchromatic 2016-09-17
多光谱Multispectral 2 蓝Blue
绿Green
红Red
近红外Near-infrared
Worldview-3 全色Panchromatic 0.5 全色Panchromatic 2016-10-08
多光谱Multispectral 2 海岸Coastal
蓝Blue
绿Green
黄色Yellow
红Red
红边Red edge
近红外Near-infrared
近红外2 Near-infrared 2

Fig. 2

The spatial distribution of field samples The base image iWWs a false color image of the study area, R: Red; G: NIR; B: Green, the vector data is the sampling area"

Fig. 3

The flowchart of crop classification"

Table 2

Overall accuracies of crop classification for different growth stages"

获取时间
Date of image acquisition		 光谱角制图SAM 支持向量机SVM 决策树DTs 随机森林RF
基于像素
Pixel-based		 基于对象
Object-based		 基于像素
Pixel-based		 基于对象
Object-based		 基于像素
Pixel-based		 基于对像
Object-based		 基于像素
Pixel-based		 基于对象
Object-based
OA K OA K OA K OA K OA K OA K OA K OA K
2016-05-10 87.47 0.81 64.62 0.47 78.35 0.67 90.25 0.85 90.93 0.86 92.12 0.88 91.27 0.87 93.52 0.90
2016-09-07 68.56 0.52 67.36 0.50 74.23 0.62 84.04 0.76 76.90 0.66 85.36 0.78 79.37 0.69 84.40 0.77
2016-10-08 78.30 0.67 74.21 0.61 77.53 0.67 88.88 0.83 80.13 0.70 88.77 0.83 82.14 0.73 83.82 0.78

Fig. 4

Comparison of user’s accuracies and producer’s accuracies of jujube and cotton for different stages using pixel-based classification methods"

Fig. 5

Maps of crop classification from the image of September 7, 2016 using four classification methods at pixel and object levels"

Table 3

Comparison of user’s accuracies and producer’s accuracies of jujube and cotton for single stages using pixel-based and object-oriented classification methods"

空间尺度
Spatial scale		 光谱角制图SAM 支持向量机SVM 决策树DTs 随机森林RF
枣树Jujube 棉花Cotton 枣树Jujube 棉花Cotton 枣树Jujube 棉花Cotton 枣树Jujube 棉花Cotton
UA PA UA PA UA PA UA PA UA PA UA PA UA PA UA PA
面向对象
Object-oriented		 69.17 50.44 63.81 54.40 96.17 60.61 64.47 96.09 97.65 67.75 67.12 92.49 98.29 62.19 65.76 92.29
基于像素
Pixel-based		 70.23 52.45 44.21 49.27 93.24 43.90 52.15 87.86 96.48 47.48 54.55 91.91 96.29 52.54 57.95 90.87

Fig. 6

False color composites of the study area generated with satellite imagery acquired at different stages"

Fig. 7

Importance of features based on the gain ratio"

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