Extraction of winter wheat area information based on the improved NDVI density slicing method
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Abstract:
The planting structure information extracted by high-resolution remote sensing imaging can be more intuitive than traditional statistical data to present the spatial distribution and area information of crops. These data can provide important reference for water resources management. In order to eliminate the influence of mixed pixels in GF-1 WFV sensor images on wheat information extraction, we introduced the high-resolution GF-1 PMS sensor images, and established samples in the two images. We used the superior resolution of PMS images to provide real wheat area weight to the training samples of wheat mixed pixels in the WFV image, and obtained the relationship between wheat NDVI and wheat area weight. Then we used the interval normalization method to solve the problem in which one NDVI value corresponded to different wheat area weights, and thus obtained the true area information of wheat in mixed pixels, and extracted the winter wheat information of Jizhou City. It was verified that the method can accurately extract information of winter wheat area under the condition of insufficient samples.
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