A panchromatic and multispectral image fusion method based on two-step sparse coding and wavelet transformation

Abstract

The invention provides a panchromatic and multispectral image fusion method based on two-step sparse coding and wavelet transformation. The method comprises the following steps of data preprocessing,high-low resolution dictionary, one-step sparse coding, two-step sparse coding, two-step sparse coefficient merging, sparse coefficient reconstruction to obtain a fused image, wavelet transformation,high-frequency component fusion, low-frequency component fusion and inverse transformation. According to the invention, the algorithm efficiency can be improved through the two-step sparse coding algorithm, at the same time, the sparse coefficient is solved through two-step sparse coding, and the precision of sparse representation is improved; the problem of spectral distortion is solved, the fusion result can be improved through fusion according to the principle of maximum absolute value, the fusion result is clear in image and rich in detail information, the spectral information and the original image are the highest in consistency, the operation time is short, wavelet transformation is carried out through full-color and multispectral images, and the method is a rapid, efficient and effective fusion method.

Description

technical field [0001] The invention relates to the field of image fusion, in particular to a panchromatic and multispectral image fusion method based on two-step sparse coding and wavelet transform. Background technique [0002] Due to the limitation of sensor technology, usually panchromatic image sensors can only collect panchromatic images with high spatial resolution and low spectral resolution, which are extremely rich in detail and texture information, but lack spectral information; It can collect multi-spectral images with high spectral resolution and low spatial resolution. Its spectral information is extremely rich, but it lacks detailed information; the fusion of panchromatic images and multi-spectral images can make full use of the complementarity of their information, making it more comprehensive and efficient. Accurately describing scene information has important applications in military, geology, national defense, aviation, remote sensing and other fields. ...

AI Technical Summary

Problems solved by technology

[0002] Due to the limitation of sensor technology, usually panchromatic image sensors can only collect panchromatic images with high spatial resolution and low spectral resolution, which are extremely rich in detail and texture information, but lack spectral information; It can collect multi-spectral images with high spectral resolution and low spatial resolution. Its spectral information is extremely rich, but it lacks detailed information; the fusion of panchromatic images and multi-spectral images can make full use of the complementarity of their information, making it more comprehensive and efficient. Accurately describe scene information, which has important applications in military, geology, national defense, aviation, remote sensing and other fields

[0003] The existing fusion methods of panchromatic and multispectral images can be summarized into two categories: one is classical fusion methods, such as IHS transform, PCA transform, wavelet transform, and fusion methods based on high-pass filtering, etc. These methods are simple in calculation, The image details are rich, but there will be spectral distortion; the other is the image fusion method based on sparse representation. The ideas of this type of method can be summarized into two categories: the traditional idea based on sparse representation and the idea based on sparse reconstruction. , as one of the methods based on the traditional sparse representation, the existing image fusion method based on the combination of sparse representation and wavelet transform can effectively enrich the spatial detail information, so it can achieve a better fusion effect, but the traditional sparse representation method needs to train the dictionary , and solve the sparse coefficients for the two images separately, so the calculation is complex and the time consumption is large. In addition, when the number of layers of wavelet decomposition is large, the image fusion method based on the combination of traditional sparse representation and wavelet transform will appear spectral distortion

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