Extraction model of winter wheat planting information based on unsupervised classification

doi:10.13474/j.cnki.11-2246.2019.0254

Abstract

Abstract: There are some problems of supervised learning algorithm in remote sensing extraction of regional winter wheat planting information, such as heavy dependence on ground sample data, complex process, too many artificial interference factors and low degree of automation, etc. In order to solve those problems, this paper proposed a model of winter wheat extraction which took unsupervised classification technology as the core and combined with multi-scale technology. It verified the accuracy and validity of the model proposed in this paper that took Xinji city of Hebei province as a typical experimental area and used the GF-1 remote sensing data in 2014. The experimental results show that the overall accuracy of the model is 94.00%, and the Kappa coefficients is 0.88. For winter wheat in the study area, the model can achieve the supervised classification extraction accuracy without training sample data and less artificial interference factors. So, the model can meet the requirements of ground remote sensing monitoring for winter wheat planting information.

Key words: unsupervised classification, winter wheat, vegetation index, GF-1, multi-scale

CLC Number:

P237

WANG Dongli, ZHANG Anbing, ZHAO Anzhou, LI Jing. Extraction model of winter wheat planting information based on unsupervised classification[J]. 测绘通报, 2019, 0(8): 68-71,77.

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