High-resolution remote sensing image crop classification method based on deep learning

Abstract

The invention discloses a high-resolution remote sensing image crop classification method based on deep learning, and belongs to the technical field of satellite remote sensing image processing and application. The objective of the invention is to solve the problems of fuzzy ground object boundary, high algorithm complexity, neglected characteristic waveband information and the like in crop classification of an existing high-resolution remote sensing image, and the problem that crop classification cannot be carried out on the high-resolution remote sensing image by using an existing neural network method. According to the method, a 36-waveband combination mode is adopted, a VGG neural network result is designed, deep learning of the VGG neural network is utilized, and accurate classification of crop plots in a high-resolution remote sensing image with the resolution of 10 m is achieved through multiple iterations.

Description

technical field [0001] The invention belongs to the technical field of satellite remote sensing image processing and application. Background technique [0002] The acquisition of crop classification information is of great significance in many fields such as agricultural resource survey, land use status analysis, crop yield estimation and disaster assessment, and has become one of the research hotspots in the field of remote sensing in recent years. Among the classification methods of many remote sensing images, the image classification method based on deep learning has attracted much attention since it was proposed, and it has opened up a new direction for image recognition and classification. In recent years, researchers at home and abroad have proposed many image classification methods based on deep learning. Representative studies are as follows: [0003] Different from traditional image classification methods, the addition of neural network algorithms has significantl...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the shortcomings of the existing high-resolution remote sensing images in the classification of crops, such as blurred boundaries of ground objects, high algorithm complexity, and ignoring characteristic band information, and the current neural network method cannot classify crops in high-resolution remote sensing images. , the present invention adopts a remote sensing image crop classification algorithm based on deep learning, which can quickly and effectively obtain crop classification information in high-resolution remote sensing satellite images

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