(Retracted) Application of remote sensing image ecological land classification method in crop planting area extraction under the background of data fusion

Abstract

Data mining is a common basic function in humans and many other intellectual systems. The popularization of computers has created a good research environment for the advanced application of remote sensing satellites. With China’s economic and population growth, the demand for food is also increasing. Obtaining the planting area is one of the most important tasks in calculating harvest costs. However, traditional cost analysis methods lack information. The traditional remote sensing image classification method is based on the pixel classification method, which cannot effectively extract the spatial texture information in an image. The pixel-based classification method also has the problem of salt and pepper phenomenon in the classification results, which generates a large number of invalid broken patches and ultimately leads to low classification accuracy. Here, we investigated the application of remote state system mapping for the extraction of the planting area. We analyzed the basic principles of remote image processing and remote image sharing methods and introduced an audio system and optical algorithm. Here, two separation methods are selected, long-distance traction measurements are performed in three training areas, and the two results are compared and analyzed. Experimental results show that the method of objective orientation increases the working speed compared with the method of manuscript translation. The benefit of speed measurement becomes more apparent in both seasons as the measuring range increases. Computer automatic classification and the method of artificial visual interpretation are tested sequentially, and the total time taken is 49 and 13.5 days, respectively.