Journal of Geo-information Science >
Remote Sensing Investigation Method of Object-oriented Crops with Special Characteristics
Received date: 2018-02-28
Request revised date: 2018-07-19
Online published: 2018-10-17
Supported by
Key Research and Development Project of the Ningxia Hui Autonomous Region, No.2016KJHM130.
Copyright
The advantages of Ningxia Hui Autonomous Region is land, light energy, and irrigation of Yellow River, which provides an inherent condition for the growth of characteristic crops of Ningxia (e.g. watermelon, Chinese wolfberry and jujube). Accessing to planting structure information of characteristic crops quicklf and accurately is not only an important basis for regional crop monitoring, yield estimation and disaster assessment, but also an important evidence for analyzing the spatial pattern changes of characteristic crop and assessing the impact of regional characteristics on agricultural production. In recent years, with the continuous development of space technology and remote sensing satellites, more and more scholars have applied remote sensing technology to the extraction of crop planting structure information. However, the traditional remote sensing survey model is only applicable to low resolution and medium resolution remote sensing data, and domestic and abroad scholars have relatively few studies on information extraction of similar Ningxia characteristic crops (e.g. watermelon, Chinese wolfberry and jujube), and the selection of classification models and strategies is difficult to meet the demand of rapid monitoring, accurate acquisition and real-time decision-making. Based on this, this paper calculate and analyze the spectral characteristics and texture features of the three types of specialty crops under the support of GF-2 remote sensing data, and establish an SVM of object-oriented classification model, the overall classification accuracy is 94.94% and the Kappa coefficient is 0.9174. And compare the classification results with the traditional SVM classification results, The study found that the SVM of object-oriented model established in this paper has the highest accuracy and best results, the texture information makes it easier to distinguish the Chinese wolfberry and jujube,whice has theless difference in spectral characteristics, and the texture information effectively reducing the model error and missing error, and improving the model classification results.
SHAN Zhibin , KONG Jinling , ZHANG Yongting , LI Huan , GUAN Hong , HU Yongxin , LI Jianfeng , ZHANG Wenbo . Remote Sensing Investigation Method of Object-oriented Crops with Special Characteristics[J]. Journal of Geo-information Science, 2018 , 20(10) : 1509 -1519 . DOI: 10.12082/dqxxkx.2018.180116
Fig. 1 Study area location diagram图1 研究区位置示意图(NIR-Red-Green假彩色合成) |
Tab. 1 Statistics of the ground quadrat data表1 地面样方数据统计 |
作物类型 | 训练数据 | 检验数据 | |||||
---|---|---|---|---|---|---|---|
样方数 | 样方总面积/km2 | 面积比/% | 样方数 | 样方总面积/km2 | 面积比/% | ||
硒砂瓜 | 100 | 83.56 | 39.43 | 70 | 53.31 | 39.47 | |
枸杞 | 100 | 69.38 | 32.74 | 70 | 46.95 | 34.76 | |
大枣 | 60 | 58.99 | 27.83 | 40 | 34.82 | 25.77 |
Fig. 2 Characteristic curve of crop characteristic图2 特色农作物特征曲线 |
Fig. 3 Characteristic curve of crop texture feature图3 特色农作物纹理特征曲线 |
Fig. 4 Texture feature results of different windows图4 不同窗口的纹理特征结果 |
Fig. 5 Different segmentation combined threshold results图5 不同分割合并阈值结果 |
Fig. 6 Remote sensing classification map of special crop planting structure图6 特色农作物种植结构遥感分类图 |
Tab. 2 Confusion matrix results表2 混淆矩阵结果 |
地物类型 | 硒砂瓜 | 枸杞 | 大枣 | 合计 |
---|---|---|---|---|
未分类 | 0 | 0 | 0 | 0 |
硒砂瓜 | 39 557 | 361 | 0 | 39 918 |
枸杞 | 439 | 13 190 | 116 | 13 745 |
大枣 | 0 | 2986 | 20 492 | 23 478 |
合计 | 39 996 | 16 537 | 20 608 | 77 141 |
Tab. 3 Accuracy evaluation result表3 精度评定结果 |
地物类型 | 硒砂瓜 | 枸杞 | 大枣 | |
---|---|---|---|---|
制图精度/% | 98.90 | 79.76 | 99.44 | |
用户精度/% | 99.10 | 95.96 | 87.28 | |
错分误差/% | 0.90 | 4.04 | 12.72 | |
漏分精度/% | 1.10 | 20.24 | 0.56 | |
总体精度/% | 94.94 | |||
Kappa系数 | 0.917 |
Tab. 4 Accuracy comparison results of different classification methods表4 不同分类方法精度对比表 |
类别 | 光谱单数据源SVM法 | 光谱结合纹理SVM法 | 面向对象的SVM法 | |||||
---|---|---|---|---|---|---|---|---|
制图精度/% | 用户精度/% | 制图精度/% | 用户精度/% | 制图精度/% | 用户精度/% | |||
硒砂瓜 | 90.02 | 95.84 | 92.3 | 99.58 | 98.90 | 99.10 | ||
枸杞 | 76.30 | 66.72 | 78.52 | 72.84 | 79.76 | 95.96 | ||
大枣 | 80.78 | 76.94 | 91.24 | 80.74 | 99.44 | 87.28 | ||
总体精度/% | 84.82 | 89.17 | 94.94 | |||||
Kappa系数 | 0.769 | 0.815 | 0.917 |
The authors have declared that no competing interests exist.
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