Hyperspectral Data and High Resolution Image Fusion Method

Abstract

This application discloses a hyperspectral data and high-resolution image fusion method, which relates to the field of hyperspectral remote sensing technology, including: collecting multi-band hyperspectral data with latitude and longitude coordinates, and finding the corresponding multi-band hyperspectral data in the high-resolution image According to the pixel value corresponding to each pixel, establish the quadratic polynomial constraint condition, calculate the coefficient of the quadratic polynomial, calculate the multi-band spectral value of each pixel according to the coefficient and the pixel value, and then according to the pixel value Obtain images with both high spectral resolution and high spatial resolution with multiband spectral values. By emphasizing the correspondence between spectral values ​​and pixel values, errors such as pixel resampling are avoided, and pixel values ​​and multi-band spectral values ​​are associated with each other, so that the judgment of characteristic attributes has higher credibility and accuracy , which is conducive to improving the fusion effect. In addition, it also makes the auxiliary decision-making more targeted, so that the amount of data processing is greatly reduced, which is conducive to real-time processing.

Description

Technical field [0001] The present application relates to the field of high spectroscopy, and in particular, there is a high spectroscopy data and a high resolution image fusion method. Background technique [0002] High-spectral remote sensing data can accurately reactively reactively spectroscopy spectroscopy, and thus can accurately extract the degree of change and the range of occurrence of the ground object spectrum in remote sensing monitoring. However, high-spectral remote sensing technology has a large amount of data volume, long calculation cycle, limited spatial resolution, and it is difficult to directly apply to actual remote sensing monitoring. Digital images can effectively increase spatial resolution, which is conducive to the fast classification of ground object information. Image fusion techniques based on high spectrum data and high resolution digital images can effectively improve the speed and accuracy of remote sensing monitoring. [0003] At present, image f...

AI Technical Summary

Problems solved by technology

The spectral resolution of hyperspectral data is high, and the spatial resolution of high spatial resolution remote sensing images is high. When the two are fused, the amount of data is large, the calculation time is long, and it takes a huge amount of time

In addition, pixel-level fusion depends on the accuracy of image registration. If the registration accuracy is not high, the fusion result is difficult to be guaranteed.

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