In order to be able to effectively determine the planting area of ​​winter wheat, there are many shortcomings in the traditional manual survey methods. The advantages of remote sensing technology are many. The application of area meters is also relatively common in modern times. The jointing period of winter wheat is the most suitable period for extracting wheat area using remote sensing images. The application of the area meter compensates for the defects of manual measurement, and the measured results are more accurate and reliable.
In the past, the image factors based on the pixel classification method had only spectral features, were relatively simple, and had low accuracy. The object-oriented classification method is used in this study. The object is the processing unit and it is combined with NDVI, texture features, etc. to assist in the classification. The results are far better than the traditional classification methods, and the "homologous spectrum" and "foreign matter spectrum" are solved well. Problems affecting the classification result have achieved high accuracy.
The application of area meter, object-oriented classification in the winter wheat area extraction there are still some deficiencies, the key lies in the choice of optimal image segmentation, the need for further improvement and experimental research. Image segmentation is the basis and core problem of classification and directly affects the classification accuracy. However, at present, the lack of applicable quantitative methods to obtain the segmentation scale can only be determined manually through continuous experimentation and adjustment of scales. However, the same segmentation scale is difficult to apply to large-scale image segmentation, and the under-segmentation and over-segmentation often occur in local regions. Therefore, the self-adaptiveness of the segmentation scale is also a question worth studying in the future.
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