Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1473-1480, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1473-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1473-1480, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1473-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

EXTRACTION AND ANALYSIS OF MAJOR AUTUMN CROPS IN JINGXIAN COUNTY BASED ON MULTI - TEMPORAL GF - 1 REMOTE SENSING IMAGE AND OBJECT-ORIENTED

B. Ren1, Q. Wen1, H. Zhou2,1, F. Guan1, L. Li1, H. Yu1, and Z. Wang3 B. Ren et al.
  • 1Twenty-first Century Space Technology Applications Co., Ltd,Beijing 100096, China
  • 2State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijng Normal University, Beijing, China
  • 3Beijing Engineering Research Center of Small Satellite Remote Sensing Information, Beijing, China

Keywords: Remote Sensing, Autumn Crops, Object-oriented, Information Extraction, Phenological Characteristics

Abstract. The purpose of this paper is to provide decision support for the adjustment and optimization of crop planting structure in Jingxian County. The object-oriented information extraction method is used to extract corn and cotton from Jingxian County of Hengshui City in Hebei Province, based on multi-period GF-1 16-meter images. The best time of data extraction was screened by analyzing the spectral characteristics of corn and cotton at different growth stages based on multi-period GF-116-meter images, phenological data, and field survey data. The results showed that the total classification accuracy of corn and cotton was up to 95.7 %, the producer accuracy was 96 % and 94 % respectively, and the user precision was 95.05 % and 95.9 % respectively, which satisfied the demand of crop monitoring application. Therefore, combined with multi-period high-resolution images and object-oriented classification can be a good extraction of large-scale distribution of crop information for crop monitoring to provide convenient and effective technical means.