Remote sensing panchromatic and multispectral image distributed fusion method based on residual network

Abstract

The invention provides a remote sensing panchromatic and multispectral image distributed fusion method based on a residual network, which mainly solves the problems of spectrum distortion, low spatial resolution and low fusion quality in the prior art, and comprises the following steps: collecting an original image of a target area through a satellite, and preprocessing the original image; constructing a simulation training set and a test set by using the preprocessed panchromatic image and the preprocessed multispectral image according to a Wald criterion, constructing a residual network-based distributed fusion model consisting of three branches, and fully training a network by taking the panchromatic image and the multispectral image of the training set as input of the network; and inputting panchromatic and multispectral images to be fused into the trained fusion network to obtain a fused image. According to the method, the features of different scales of different branches are used for fusion, more spectral information and spatial information are reserved, the method has better performance in the aspects of improving the spatial resolution and reserving the spectral information, and the fusion quality is improved.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing panchromatic and multi-spectral image distributed fusion method based on a residual network. Background technique [0002] Remote sensing images are widely used in various industries, such as agricultural output prediction, detection of agricultural and forestry diseases and insect pests, disaster prediction, geological exploration, national defense, vegetation cover and land use, environmental change detection and so on. However, due to the limitations of satellite sensor technology, images with high spatial resolution and high spectral resolution cannot be obtained at the same time, and only panchromatic images (PAN) with high spatial resolution and low spectral resolution and images with low spatial resolution and high spectral resolution can be obtained. Multispectral imagery (MS). However, various fields need to use images with both ...

AI Technical Summary

Problems solved by technology

Although component replacement methods such as Brovey transform, Schmidt orthogonal transform (GS), principal component analysis (PCA) and variants of these methods are widely used in various software, they have problems of spectral distortion and spatial over-sharpening. question

Multi-resolution analysis methods such as wavelet transformation, Laplacian pyramid decomposition, contourlet transform, curvelet transform, non-subsampled shearlet transform and their variants reduce spectral distortion, but the spatial resolution is relatively low, and it is possible ringing phenomenon

With the development of deep learning technology, various deep learning networks have begun to be applied in the direction of panchromatic sharpening, among which the convolutional neural network is the most used, although networks such as PNN, DRPNN, PanNet, and PanGAN are proposed for panchromatic sharpening Some effects have been achieved, but there are still problems of spectral distortion, low spatial resolution, and low fusion quality

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