Pansharking method for gray scale amplitude modulation based on spectrum mapping

Abstract

The invention discloses a pansharking method for gray scale amplitude modulation based on spectrum mapping. The method comprises: firstly, a spectral mapping conversion matrix T is solved by establishing a variational model, and a high spatial resolution image (P _ HCM) with a certain spectral resolution is obtained. Then, carrying out spatial filtering on the image obtained after spectrum mappingto generate spatial details with spectrum information, and injecting the spatial details into the up-sampled MS image to obtain an HR _ MS image with higher resolution; and finally, based on the brightness amplitude modulation (SFIM) principle of smooth filtering, obtaining a final fusion image (HRMS) with high spatial and spectral resolution. On the basis of a traditional pansharking method, thespectrum and spatial information of the HRPAN image and the LRMS image are fully mined, on the premise that the spatial structure information is well kept, good spectrum fidelity is achieved, and a fused image with a good visual effect can be obtained.

Description

technical field [0001] The invention belongs to the technical field of image fusion, in particular to a pansharpening method based on spectral mapping grayscale amplitude modulation. Background technique [0002] In the design of optical remote sensing systems, there is a critical trade-off between spatial resolution, spectral resolution, and signal-to-noise ratio due to the limited incident energy. For this reason, multispectral image sensors can usually only obtain multispectral images with lower spatial resolution or panchromatic images with high spatial resolution. But in many applications, such as image classification, object detection, scene interpretation, and spectral unmixing, multispectral images with high spatial resolution are required. Therefore, the goal of pansharpening methods is to fuse spatial and spectral information to produce multispectral images with the required high spatial resolution. [0003] At present, there are many methods for remote sensing i...

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Problems solved by technology

[0003] At present, there are many methods for remote sensing image pansharpening, including classic component replacement methods, such as PCA (Principal Component Analysis) and GS (Gram-Schmidt) methods, etc. These methods are easy to implement and simple, and finally can show better Spatial resolution and robustness, but significant spectral distortion due to spectral range mismatch of PAN and MS images

There are also Bayesian methods and matrix decomposition methods, such as HySure and Bayesian sparse representation, etc. The fusion images obtained by these methods can have better effects in space and spectrum, but the calculation amount is relatively large, which is not conducive to real-time sex

In addition, there are classic multi-resolution analysis methods, such as the MTF-GLP (modulation transfer function Generalized Laplacian Pyramid) method. Since the digital number of the reflection band of the visible image during the day is mainly composed of the solar radiation on the land surface and the land The spectral reflectance of the surface is determined by two factors. Liu J et al. proposed the SFIM (Smoothing Filter-based Intensity Modulation) algorithm (Liu J G, Smoothing Filter-based Intensity Modulation: aspect preserve image fusion technique for improving spatial details, International Journal of Remote Sensing, vol.21, no.18, pp.3461-3472, 2000), although this method can well maintain the spectral information in MS and integrate high spatial details, but the fusion image will appear excessive injection of details Case

The above methods basically use the spatial resolution of the PAN image to improve the spatial resolution of the MS image, but these algorithms will cause different degrees of spectral distortion.

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