Remote sensing image fusion sharpening method based on progressive cross-scale neural network

A remote sensing image, cross-scale technology, applied in the field of remote sensing image processing, can solve problems such as hindering performance, and achieve the effect of improving accuracy, excellent overall effect, and good fusion ability.

Active Publication Date: 2022-03-04
UNIV OF SCI & TECH OF CHINA
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However, using only image-level loss places limited constraints o

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  • Remote sensing image fusion sharpening method based on progressive cross-scale neural network
  • Remote sensing image fusion sharpening method based on progressive cross-scale neural network
  • Remote sensing image fusion sharpening method based on progressive cross-scale neural network

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[0050] In this example, if figure 1 As shown, a remote sensing image fusion and sharpening method based on a progressive cross-scale neural network can better mine spatial-spectral correlation information and capture long-term At the same time, the reconstructed image is more in line with the visual effect, and the pan-sharpening effect of the remote sensing image is improved. Specifically, the method includes the following steps:

[0051] Step 1: Construct input sample data, including data acquisition and preprocessing;

[0052] Step 1.1: Obtain a high-resolution multispectral image and its corresponding panchromatic image and perform cropping operations to construct an image dataset; where the high-resolution multispectral image set in the image dataset is denoted as H∈R M×N×B , like the set of panchromatic images in the data set is denoted as P∈R M×N , M represents the length of the image, N represents the width of the image, and B represents the number of frequency band...

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Abstract

The invention discloses a remote sensing image pan-sharpening method based on a progressive cross-scale attention network. The method comprises the steps that 1, panchromatic images and multispectral images with different resolutions serve as input of sub-networks in all stages to be fused step by step; 2, constructing an intermediate reasoning layer for the pyramid in each stage, wherein the intermediate reasoning layer comprises a feature extraction stage, a feature fusion stage based on a cross-scale attention module and an image reconstruction stage; and 3, obtaining an optimal remote sensing image fusion sharpening network through training and optimization of the progressive cross-scale attention network, wherein the optimal remote sensing image fusion sharpening network is used for fusing any low-resolution multispectral image and panchromatic image to obtain a high-resolution multispectral image. According to the method, the correlation among the features on a plurality of specific scales can be captured, and the reconstructed image with a better visual effect is obtained progressively, so that the cross-scale feature related information is better mined, and the effect of the reconstructed image is improved.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing image fusion and sharpening method based on a progressive cross-scale neural network. Background technique [0002] High-resolution multispectral (HRMS) images have been widely used in many fields such as digital mapping, mining and environmental monitoring. However, due to the physical limitations of satellite sensors, there is a critical trade-off between spatial and spectral resolution, which means that only high-resolution panchromatic (PAN) images and low-resolution multispectral (MS) images can only be captured separately. image. The purpose of pan-sharpening is to obtain high-resolution multispectral images by fusing multispectral images and panchromatic images. Traditional pan-sharpening algorithms can be classified according to one of three approaches: component substitution methods, multiresolution analysis methods, and variati...

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Application Information

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IPC IPC(8): G06T5/00G06T5/50G06T3/40G06N3/04G06N3/08
CPCG06T5/003G06T5/50G06T3/4007G06N3/08G06T2207/10036G06T2207/20081G06T2207/20084G06T2207/20221G06N3/045
Inventor 傅雪阳查正军刘爱萍杨子禾
Owner UNIV OF SCI & TECH OF CHINA
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