Hyper-spectral image super-resolution method based on hyper-parameter fidelity and depth prior joint learning

Abstract

The invention discloses a hyper-spectral image super-resolution method based on hyper-parameter fidelity and depth prior joint learning. The method comprises the following steps: establishing a hyper-spectral and multi-spectral image fusion variational model based on depth prior regularization of a hyper-parameter fidelity model; optimizing a hyperspectral multispectral image fusion variation model; carrying out tensor representation on the model optimization iteration process; performing network expansion on the iterative process of variational model optimization, and executing the iterative process of optimization; and training the network by using the L1 norm as a loss function. The method has the capability of representing the hyperspectral image degradation model and the data prior in the network at the same time, and has excellent performance when being applied to hyperspectral multispectral image fusion.

Description

technical field [0001] The invention relates to hyperspectral-multispectral image fusion technology, in particular to a hyperspectral image super-resolution method for joint learning of hyperparameter fidelity and depth prior. Background technique [0002] Hyperspectral images contain rich spatial spectral information, which can distinguish the material properties of the scene at the pixel level, and have important application value in remote sensing. However, the low resolution of hyperspectral image restricts its application in high-resolution earth observation. In contrast, multispectral images have high resolution and can provide ground object information for hyperspectral images. At present, hyperspectral-multispectral image fusion has become an important research direction of the enhancement technology of hyperspectral image resolution. [0003] Convolutional neural network (CNN) can use the spatial structure of the image to extract features, and can naturally extrac...

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Problems solved by technology

Although the above methods have achieved good fusion performance, how to learn the degradation model and its data priors to represent hyperspectral images in CNN network has not been fully studied

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