Winter wheat planting area image extraction method combining GF-6 and Sentinel-2

A GF-6, extraction method technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve the problem that the extraction technology of remote sensing images in winter wheat planting areas is difficult to meet the needs of actual use, and achieve smoothness and integrity. The effect of high extraction accuracy and good scalability

Pending Publication Date: 2022-08-02
ANHUI UNIVERSITY
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Problems solved by technology

[0010] The purpose of the present invention is to solve the defect that the remote sensing image extraction technology of winter wheat planting area in the prior art is diffi

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  • Winter wheat planting area image extraction method combining GF-6 and Sentinel-2
  • Winter wheat planting area image extraction method combining GF-6 and Sentinel-2
  • Winter wheat planting area image extraction method combining GF-6 and Sentinel-2

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[0048] In order to have a further understanding and understanding of the structural features of the present invention and the effects achieved, the preferred embodiments and accompanying drawings are used in conjunction with detailed descriptions, and the descriptions are as follows:

[0049] like figure 1 As shown, a method for extracting images of winter wheat planting areas in combination with GF-6 and Sentinel-2 according to the present invention, comprises the following steps:

[0050] The first step is the creation of remote sensing image datasets. The remote sensing images of Gaofen-6 with 8m resolution and Sentinel-2 with 10m resolution were acquired and preprocessed to form a remote sensing image dataset.

[0051] In practical applications, the 2m panchromatic image of Gaofen-6 and the 8m multispectral data image are first fused to obtain 2m multispectral data, which is used as a reference for manual annotation. For example, Zhengding County in Shijiazhuang City can be

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Abstract

The invention relates to a winter wheat planting area image extraction method combining GF-6 and Sentinel-2. Compared with the prior art, the winter wheat planting area image extraction method solves the defect that a winter wheat planting area remote sensing image extraction technology is difficult to meet actual use requirements. The method comprises the following steps: creating a remote sensing image data set; constructing a winter wheat planting area image extraction network; training a winter wheat planting area image extraction network; obtaining a remote sensing image to be extracted; and extracting an image result of the winter wheat planting area. According to the method, the winter wheat planting area can be extracted from the remote sensing image more accurately, the model structure is designed and improved according to crop space distribution information extraction tasks and high-resolution remote sensing data characteristics, and good effects are achieved in the aspects of efficiency, speed, applicability and accuracy.

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Owner ANHUI UNIVERSITY
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