Image optimum fusing method based on fuzzy integral

Abstract

The invention discloses an optimized fusion method based on fuzzy integral, in the HIS space, the intensity component of the spectrum image is decomposed with wavelet multi-layer and acquires the low frequency base band coefficients, acquires the low frequency base band coefficient by decomposing the high resolution image with multi-layer wavelet, integrates the two single factor indexes of spectrum information and space resolution are synthesized with fuzzy integral, carries on pixel fusion with iteration, and carries on character fusion to the high frequency detail of the high frequency sub-band coefficient after being decomposed with wavelet, then carries on correspondent wavelet reverse conversion to the sub-band coefficient and low frequency coefficient, and acquires the new intensity component I', then the fused image may be acquired through HIS reverse conversion. The invention combines the character of HIS fusion method and wavelet fusion method, it realizes high space resolution, and it reduces the deformation of color greatly, and it improves the spectrum information index of fused image.

Description

technical field [0001] The present invention relates to an image optimization fusion method based on fuzzy integral, combined with the time-frequency characteristics of wavelet multi-resolution decomposition and IHS (Intensity-Chroma Hue-Saturation) transformation fusion method, using fuzzy integral to synthesize spectral information and The two single-factor indicators of spatial resolution are used to optimize the fusion of remote sensing images and effectively improve the spectral information indicators of fused images. They can be widely used in various military or civilian remote sensing information processing systems and digital city spatial information systems. . Background technique [0002] Effectively fusing high-resolution panchromatic remote sensing images and low-resolution multispectral remote sensing images, and balancing the two characteristic indicators of spatial detail information and spectral information in the fusion results, is one of the research hotsp...

AI Technical Summary

Problems solved by technology

However, this classic IHS fusion method has certain defects. Because the data of different bands have different spectral characteristic curves, the IHS fusion method distorts the original spectral characteristics and produces different degrees of spectral degradation, which is not conducive to image The correct identification and classification, especially for the image fusion of multi-sensor remote sensing images of different time phases, the IHS fusion method cannot make the color tone of the fusion image consistent with the color tone of the original multispectral image, which is caused by the transformation of spectral information. Images cannot be used for object recognition and inversion

Te-Ming et al. conducted a mathematical proof in the IHS space, discussed the defects of the IHS fusion method, and concluded that although it is used to replace the intensity component I 0 High resolution panchromatic image of I new Before the replacement, the statistical characteristics of the image were matched, but the matching error δ=I new -I causes color distortion

At present, there is no report on the method of using fuzzy integral for image optimization fusion

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