Remote sensing image fusion method and system based on dual-branch depth learning network

Abstract

The invention provides a remote sensing image fusion method and a remote sensing image fusion system based on a dual-branch depth learning network. The method and the system comprise the following steps that panchromatic images and multi-spectral images as sample data are respectively downsampled by corresponding multiples to obtain training samples; the dual-branch convolution neural network is constructed and trained by using stochastic gradient descent algorithm to obtain the trained dual-branch convolution neural network; the panchromatic image and multispectral image to be fused are inputted into the trained two-branch convolution neural network, and the fused multispectral image with high spatial resolution is obtained. Aiming at the fusion of panchromatic and multi-spectral images in remote sensing images, the invention utilizes the deep-level depth convolution network to more fully extract the characteristics of the images, integrates the complementary information between the two images, and generates multi-spectral images with high spatial resolution.

Description

technical field [0001] The invention belongs to the field of remote sensing image processing and relates to a technical proposal for fusing remote sensing images. Background technique [0002] Remote sensing imagery is an information carrier for satellite sensors to detect and record electromagnetic waves reflected from the surface, and can be used for environmental monitoring, object classification, climate monitoring, etc. Many earth observation satellites (such as Landsat, GeoEye-1, QuickBird, etc.) take a panchromatic image of the same area while shooting multispectral images. Since ground features have different reflection values ​​for electromagnetic waves in different spectral regions, multispectral images can record more information about targets than panchromatic images with a single spectrum. The spectral resolution of the multispectral image is high, but limited by the physical characteristics of the sensor and considering the signal-to-noise ratio, its spatial r...

AI Technical Summary

Problems solved by technology

The spectral resolution of multispectral imagery is high, but limited by the physical characteristics of the sensor and considering the signal-to-noise ratio, its spatial resolution is not as good as panchromatic imagery

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