Wavelet transformation and multi-channel PCNN-based hyperspectral image fusion method

Abstract

The invention relates to a wavelet transformation and multi-channel PCNN-based hyperspectral image fusion method, which comprises the following steps: firstly, performing preprocessing of registering and grey level adjustment on hyperspectral images of N wave bands to be fused, and performing the wavelet transformation to obtain low-frequency sub-band images and high-frequency sub-band images; secondly, performing primary nonlinear fusion processing on the low-frequency sub-band images and the high-frequency sub-band images respectively by using a multi-channel PCNN model, obtaining corresponding ignition frequency map, performing linear mapping of corresponding coefficient range on the ignition frequency map for the low-frequency sub-band images, and taking a mapping result as a fusion result; thirdly, performing the region segmentation on the high-frequency sub-band images in each direction by using the ignition frequency map, and performing the fusion processing on different regions by using different fusion rules; and finally, processing wavelet reconstruction and obtaining a final result image. The method can realize the hyperspectral image fusion of a plurality of hyperspectral wave bands and can achieve a better fusion effect.

Description

technical field [0001] The invention relates to a hyperspectral image fusion method based on wavelet transform and multi-channel PCNN, which can be applied to various military or civilian hyperspectral remote sensing information processing systems. Background technique [0002] The development of imaging spectroscopy technology provides people with rich multispectral or hyperspectral remote sensing data. In particular, hyperspectral remote sensing can simultaneously provide images of dozens or even hundreds of bands of the same scene, and has been widely used in various civilian and military fields. However, it also brings problems such as a sharp increase in the amount of data and high redundancy between adjacent band data, and image fusion technology is to reduce the dimensionality of hyperspectral images, improve the quality of hyperspectral images, and increase the utilization of spectral data. one of the effective ways. [0003] Although there are many traditional ima...

AI Technical Summary

Problems solved by technology

[0003] Although there are many traditional image fusion methods, most of these methods are developed for two images; moreover, generally speaking, there are more complementary information between the two images to be fused.

However, hyperspectral images have many bands and redundant information between images, so directly applying traditional fusion methods to hyperspectral images will cause problems such as low computational efficiency and unsatisfactory fusion results. research on fusion methods

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