Hyperspectral ground object classification identification method

Abstract

The invention relates to a hyperspectral ground object classification identification method. The method includes the following steps: carrying out atmospheric correction and geometric correction on remote-sensing images of known classification conditions in sequence to obtain remote-sensing images after correction; denoising the remote-sensing images after correction to obtain remote-sensing images after denoising; carrying out recombination and batch dividing on the remote-sensing images after denoising to obtain remote-sensing images after recombination; using a constructed multi-layer deepnetwork to carry out training on the remote-sensing images after recombination to extract deep-layer features of the remote-sensing images after recombination; and using an image, on which classification identification is to be carried out, to carry out training through the constructed multi-layer deep network to obtain a deep-layer feature of the image on which classification identification is tobe carried out, and comparing the deep-layer feature of the image, on which classification identification is to be carried out, with the deep-layer features, which are obtained by training, to obtaina final classification result of the image on which classification identification is to be carried out. According to the ground object classification identification method of the invention, data of the hyperspectral remote-sensing image can be quickly preprocessed, and ground object classification can be highly precisely carried out on the hyperspectral remote-sensing image.

Description

technical field [0001] The invention relates to the technical field of object classification and recognition, in particular to a hyperspectral object classification and recognition method. Background technique [0002] Hyperspectral remote sensing technology uses advanced spectral imaging technology to bring remote sensing data with hundreds of bands up to decimeter-level resolution, and provides extremely rich spectral band information, which can be used in many civilian fields, such as forest land resource investigation and natural disaster observation. , weather forecast, etc. provide a new solution. [0003] However, compared with ordinary data, hyperspectral remote sensing data has a greatly increased data volume, which consumes a lot of time and server resources in the processing process; Spectrum, the phenomenon of different objects in the same spectrum makes the classification of surface features prone to misclassification and omission. Contents of the invention ...

AI Technical Summary

Problems solved by technology

[0003] However, compared with ordinary data, hyperspectral remote sensing data has a greatly increased data volume, which consumes a lot of time and server resources in the processing process; Spectrum, the phenomenon of "same spectrum and foreign objects" makes the classification of ground objects prone to misclassification and omission.

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