Extraction method of crop planted area based on GF-1 WFV image

被引：0
|
作者：
Huang, Jianxi [1 ]
Jia, Shiling [1 ]
Wu, Hongfeng [2 ]
Su, Wei [1 ]
机构：
[1] College of Information and Electrical Engineering, China Agricultural University, Beijing
[2] The Institute of Scientific and Technical Information, Heilongjiang Academy of Land Reclamation Region, Harbin
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2015年 / 46卷
关键词：
Crop planted area; Extraction method; GF-1; Satellite image;
D O I：
10.6041/j.issn.1000-1298.2015.S0.041
中图分类号：
学科分类号：
摘要：
Obtaining planted area of crop has important significance for guaranteeing nation grain safety. The Farm NO. 597, located in Baoqing County, Shuangyashan City, Heilongjiang Province was selected as an example to extract rice and maize planted area by taking WFV (Wide field view) sensor carried on GF-1 satellite with the spatial resolution of 16 m as data source, using the image produced on October 30, 2014, and calculating different characteristic bands. Firstly, the multi-characteristic data set was established based on the NDVI (Normalized difference vegetation index) calculated from the source image and the first three principal components analyzed by PCA (Principal component transform). Then, using the difference between different surface features in each characteristic band, the decision tree was built based on CART (Classification and regression trees) to classify rice and maize. The results showed that the overall classification accuracy was 96.15% and the Kappa coefficient was 0.94. Producer accuracy of rice was 98.41% and user accuracy was 97.64%. Producer accuracy of maize was 95.38% and user accuracy was 97.89%. This method provides the reference value for crop type mapping using GF-1 data in other agricultural areas. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
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页码：253 / 259
页数：6
相关论文
共 18 条
  • [1] Li Z., Liu Z., Li Z., Et al., Progress and prospect of application of romete sending to rice spatial distribution, Chinese Journal of Agricultural Resources and Regional Planning, 35, 6, pp. 9-18, (2014)
  • [2] Chen X., Xu Z., Xu S., Multi-level forest classification of QuickBird remote sensing image based on objects, Remote Sensing Technology and Application, 24, 1, pp. 22-26, (2009)
  • [3] Chen J., Tian Q., Vegetation classification based on high-resolution satellite image, Journal of Remote Sensing, 11, 2, pp. 221-227, (2007)
  • [4] Adelabu S., Dube T., Employing ground and satellite-based QuickBird data and random forest to discriminate five tree species in a Southern African Woodland, Geocarto International, 30, 4, pp. 457-471, (2015)
  • [5] Li Z., Li Z., Liu Z., Et al., Contrasting two classification methods in mapping paddy rice using the mid-resolution TM images, Chinese Journal of Agricultural Resources and Regional Planning, 35, 1, pp. 27-33, (2014)
  • [6] Liu W., Wang C., Zhao E., Et al., Extraction of small river information based on object-oriented classification, Transactions of the Chinese Society for Agricultural Machinery, 45, 7, pp. 237-244, (2014)
  • [7] Li C., Shao G., Using Landsat7 ETM+images as ancillary data for forest cover classification of SPOT5 images, Journal of Beijing Forestry University, 32, 4, pp. 1-5, (2010)
  • [8] Xu Q., Yang G., Long H., Et al., Crop information identification based on MODIS NDVI time-series data, Transactions of the CSAE, 30, 11, pp. 134-144, (2014)
  • [9] Damien A., Milton J., Vincent D., Et al., Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil, International Journal of Remote Sensing, 32, 22, pp. 7847-7871, (2011)
  • [10] Chen J., Liu Y., Yu Z., Planting information extraction of winter wheat based on the time-series MODIS-EVI, Chinese Agricultural Science Bulletin, 27, 1, pp. 446-450, (2011)
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