Remote sensing image fusion method based on adaptive multi-scale residual convolution and medium

Abstract

The invention requests to protect a remote sensing image fusion method based on adaptive multi-scale residual convolution and a storage medium. The method comprises the steps: S1, carrying out the down-sampling of corresponding multiples on a low-resolution multispectral image and a high-resolution panchromatic image according to a Wald protocol, and constructing a data set needed by a model; s2, adaptively matching the multispectral image and the panchromatic image, and stacking the feature map as input; s3, constructing a multi-scale deep residual convolutional neural network by means of the property of the convolutional neural network; s4, training the network by adopting an Adam optimization algorithm to obtain a trained deep convolutional neural network model; and S5, inputting a multispectral image and a panchromatic image to be fused into the trained deep convolutional neural network model to obtain a fused multispectral image with high spatial resolution. According to the invention, the deep convolutional network is utilized to more fully extract the spatial features of the image, and the fusion effect of the multispectral remote sensing image fusion method is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a remote sensing image fusion method based on adaptive multi-scale residual convolution. Background technique [0002] In recent years, with the development of society and technology, remote sensing images have been widely used in various applications. Because remote sensing imaging technology is limited by different satellite sensors, only high spatial resolution panchromatic (pan) and high spectral resolution multispectral (ms) images can be acquired separately. For example, earth observation satellites such as QuickBird, GeoEye, Ikonos and WorldView-3 can only take two different types of remote sensing images, but the quality of remote sensing images obtained by a single sensor often cannot meet the diverse needs of modern society. This problem has led to the rapid development of multi-source information fusion technology. For example, the multispectral (ms) image a...

AI Technical Summary

Problems solved by technology

Compared with the traditional pan-sharpening method, the convolutional neural network-based method can automatically learn the upsampling method of different bands end-to-end, and better preserve the spectral information of the image. However, the existing network structure is simple and there are still many problems. big room for improvement

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