Hyperspectral and multispectral image rapid fusion method

Abstract

The present invention discloses a hyperspectral and multispectral image rapid fusion method. The method comprises the implementation steps of: (1) establishing a training set and a test set; (2) inputting a spectrum image; (3) establishing a probability generation model; (4) training a probability generation model; and (5) obtaining a fusion spectrum image. The hyperspectral and multispectral image rapid fusion method overcomes the problems that a representational capacity is weak, the model is not stable and the operation time is long in the prior art, generated hyperspectral image with highresolution comprises more information, a space transfer matrix without priori conditions is employed to allow the model to be more stable and allow the fusion speed to be faster, and therefore the hyperspectral and multispectral image rapid fusion method is a high-speed and efficient hyperspectral image fusion processing method.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a fast fusion method of hyperspectral and multispectral images in the technical field of image fusion. The invention can be used for low-resolution hyperspectral images and high-resolution multi-spectral images, and uses image fusion technology to generate high-resolution hyperspectral images. Background technique [0002] The hyperspectral image integrates image information and spectral information. The image information can reflect the external quality characteristics such as the size, shape, and defects of the sample. Due to the difference in the absorption of the spectrum by different components, the image at a specific wavelength will be sensitive to a defect. These characteristics determine the unique advantages of hyperspectral images in detection. Compared with multispectral images, hyperspectral images not only have a great improvement in information rich...

AI Technical Summary

Problems solved by technology

However, the shortcomings of this method are: this method does not take into account the information of the low-resolution hyperspectral image, and there is no connection between the spectral dictionary and the width-height dictionary, so that the hyperspectral image obtained by this method cannot be very accurate. The information of the image dimension is well preserved, and this method needs continuous iteration to realize the fusion problem of the image, resulting in the deterioration of the speed of the fusion image

However, the shortcomings of this method are: this method only uses a linear model to represent the image, and only uses a shallow probability model to fuse the image, which cannot reflect all the information in the original image. The method involved Limit the information contained in the final image

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