A hyperspectral image sharpening method based on a spectral prediction residual convolutional neural network

Abstract

The invention discloses a hyperspectral image sharpening method based on a spectral prediction residual convolutional neural network. The method comprises the steps of reading an original hyperspectral image; Synthesizing a corresponding panchromatic image by utilizing the visible light wave band of the hyperspectral image; Preprocessing the image data to obtain a training sample pair; Constructing a spectral prediction residual convolutional neural network structure; Inputting the training sample pair into a spectrum prediction residual convolutional neural network, and reducing a training error to a minimum value by using an adaptive moment estimation algorithm, thereby obtaining an optimal network structure parameter; And inputting the same preprocessed test sample pair into the optimalspectral prediction residual convolutional neural network structure, and outputting a high-resolution hyperspectral image. The invention can effectively alleviate the phenomenon of spectrum distortion and enhance the sharpening effect.

Description

technical field [0001] The invention relates to the field of remote sensing images, in particular to a hyperspectral image sharpening method based on a spectral prediction residual convolutional neural network. Background technique [0002] With the increasing maturity of imaging spectroscopy technology, the field of remote sensing image processing has gradually transitioned from the era of multispectral images to the era of hyperspectral images. Compared with multispectral images, hyperspectral images not only contain richer ground object information, but also provide data support for more detailed spectral analysis. Although hyperspectral images play an indispensable role in many remote sensing applications, their low spatial resolution has always been criticized. In order to improve its spatial resolution, one of the strategies is to fuse the hyperspectral image with its registered high-spatial-resolution panchromatic image, and use the rich spatial detail information in...

AI Technical Summary

Problems solved by technology

For example, although principal component analysis can better restore the lost spatial detail information of hyperspectral images, there are obvious spectral distortions in the processing results; in addition, the Bayesian method is relatively good at spatial detail restoration and spectral maintenance. , but it has a large amount of computation and needs to rely on extremely strong prior information to achieve the best results, so there are certain limitations in practical applications

Images