Adaptive variation remotely sensed image fusion method

Abstract

The invention provides an adaptive variation remotely sensed image fusion method. In the method, multi-spectrum image and full-color image observation models are respectively established by analyzing the image degrading process, an inverse problem corresponding to the image fusion is described by utilizing a maximum posteriori estimation theoretical frame, and a variation image fusion model consisting of a multi-spectrum image data consistency constraint, a full-color image data consistency constraint and an image transcendental constraint is established; and in the resolving process, iterative solving is carried out by a gradient degressive algorithm, regularization parameters are adaptively solved by establishing a proper function, and an adjustable parameter is reserved simultaneously so as to meet the requirements of different users. The method can improve the original multi-spectrum image space resolution, effectively maintain the conventional spectrum information, and automatically select the proper regularization parameters adaptively according to different image data, so the method has the characteristics of high fidelity, high adaptation degree and the like.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image information processing, and relates to an adaptive variational remote sensing image fusion method. Background technique [0002] At present, many satellite remote sensing systems can simultaneously acquire panchromatic images and multispectral images of the same scene on the ground. Panchromatic images have high spatial resolution but low spectral resolution. Although multispectral images have multiple bands, their spatial resolution often cannot meet the application requirements. Therefore, using image fusion technology can make full use of the complementary information in multi-source images to obtain a high spatial resolution multispectral image, which has important research significance and application value. [0003] The current image fusion methods are mainly divided into the following categories: The first category is based on arithmetic operations, using interpolated multispe...

AI Technical Summary

Problems solved by technology

However, because these methods lack a strict theoretical framework and do not take into account the correlation between adjacent pixels during fusion processing, the processing results often cannot effectively maintain the original spectral information

Although some researchers have proposed variational fusion methods for some basic models, the models are not robust enough, and parameters need to be selected manually, which is not only complicated and time-consuming, but also easy to miss the optimal solution.

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