Satellite panchromatic and multispectral image fusion method based on multi-scale convolutional neural network

Abstract

The present invention proposes a multi-scale convolutional neural network satellite panchromatic and multispectral image fusion method, including the following steps: constructing a basic convolutional neural network, constructing required training sets and test sets, performing sharpening processing on multispectral images, and Perform data enhancement processing on the training set and test set, construct a multi-scale convolutional neural network model, and average the fusion results of the multi-scale convolutional neural network model to finally obtain a fusion result image; The construction method of the convolutional neural network improves the probability of realizing the convolutional neural network and improves the fusion efficiency. By first sharpening the multispectral image when forming the network input, it can make full use of The spatial detail information in the neglected multispectral image improves the integration effect of the spatial detail information.

Description

technical field [0001] The invention relates to the field of image fusion, in particular to a multi-scale convolutional neural network satellite panchromatic and multi-spectral image fusion method. Background technique [0002] In recent years, satellites equipped with panchromatic and multispectral image sensors have been launched one after another, and the fusion of panchromatic and multispectral images has become an important and active research direction in the field of satellite remote sensing. This is because in the field of remote sensing, multispectral images with high spatial resolution and high spectral resolution are often required. Image fusion technology is to integrate the characteristics and advantages of panchromatic and multispectral images, and use high spatial resolution panchromatic Image to improve the spatial resolution of multispectral images, while keeping the spectral characteristics of multispectral images as much as possible, so as to overcome the ...

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Problems solved by technology

[0003] Regarding the fusion method of panchromatic and multispectral images, the component replacement method is one of the simple and effective classical methods. Common component replacement methods include HIS transformation method, PCA transformation method, HCS transformation method and GS transformation method, etc. This type of method is simple to calculate, has real-time and high efficiency, and can improve the spatial resolution of multispectral images well, but when the panchromatic image has little correlation with the components or components to be replaced, it is easy to generate spectra in the fusion image Distortion phenomenon; Another classic fusion method is multi-resolution analysis method, such as Laplacian pyramid, wavelet transform, Contourlet transform, etc. Multi-resolution analysis method is due to the detailed information of panchromatic image and ideal high spatial resolution multispectral The detail components of the image in each channel are not exactly the same, so it is often necessary to further adjust it during integration. Compared with the component replacement method, this method can better maintain the spectral information of the image, but in the process of detail information extraction and integration Among them, it is easy to produce blur or distortion, resulting in the loss of spatial information; there is another kind of classical fusion method based on sparse reconstruction, which randomly samples from other known high-spatial-resolution multispectral images to construct a High spatial resolution multispectral image dictionary, however, in practical applications, the high spatial resolution multispectral image itself is the desired fusion result image, which is not easy to obtain, and there is a problem of huge calculation

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