Multispectral image fusion imaging method and system based on fuzzy kernel estimation

Abstract

The invention discloses a multispectral image fusion imaging method and system based on fuzzy kernel estimation. The method comprises the steps that a multispectral image and a panchromatic image areacquired; inputting the multispectral image and the panchromatic image into a pre-trained panchromatic and multispectral image depth fusion network to obtain a fused multispectral image, and pre-training the panchromatic and multispectral image depth fusion network to establish a mapping relationship between the multispectral image, the panchromatic image and the fused multispectral image, and a training data set adopted when the panchromatic and multispectral image deep fusion network is trained being a fusion training data set constructed based on a spatial fuzzy kernel and a spectral fuzzykernel. According to the invention, a high-spatial-resolution multispectral image can be reconstructed efficiently; the method can be applied to the practical application fields of remote sensing satellite natural disaster monitoring, crop detection, mineral exploration, land resource investigation and the like.

Description

technical field [0001] The invention relates to a multispectral image fusion method, in particular to a multispectral image fusion imaging method and system based on fuzzy kernel estimation. Background technique [0002] Remote sensing satellite multispectral images contain multiple spectral bands from visible light to near-infrared, and have rich spectral information of ground features. They have been widely used in natural disaster monitoring, crop detection, mineral exploration, and land resource investigation. However, due to the limitation of imaging sensor manufacturing process, hardware cost and satellite volume and weight, the current imaging devices of multispectral remote sensing satellites can only acquire low spatial resolution multispectral images and high spatial resolution panchromatic (grayscale) images . In order to obtain high spatial resolution multispectral images, fusing low spatial resolution multispectral images and high spatial resolution panchromati...

AI Technical Summary

Problems solved by technology

However, the fixed spatial blur kernel often does not match the actual point spread function of the satellite, and the wrong spatial blur kernel will greatly weaken the image fusion performance of the depth model, resulting in unclear details and low spectral fidelity of the fused multispectral image.

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