Remote sensing image super resolution reconstruction method and system based on depth convolution network

Abstract

The invention provides a remote sensing image super resolution reconstruction method and a system based on a depth convolution network. The method comprises steps: a to-be-processed remote sensing image is converted to YCbCr space from RGB space, and brightness space and chromaticity space are separated; a multilayer depth convolution network is built, a super resolution reconstruction model is built based on the multilayer depth convolution network, the super resolution reconstruction model is used for reconstructing the brightness space, and brightness space after reconstruction is obtained; with the brightness space after reconstruction as a guide graph, the chromaticity space is guided for joint bilateral filtering, and chromaticity space after reconstruction is obtained; the brightness space after reconstruction and the chromaticity space after reconstruction are integrated, the to-be-processed remote sensing image after integration is returned to the RGB space from the YCbCr space, a super resolution image is obtained, and the super resolution image has a higher resolution than the to-be-processed remote sensing image. The above method and the system, in a condition of not relying on a multi-temporal remote sensing image sequence in the same scene, realize super resolution reconstruction in view of the remote sensing image, and the image resolution is enhanced.

Description

technical field [0001] The present invention relates to the technical field of remote sensing image processing, and more specifically, to a remote sensing image super-resolution reconstruction method and system based on a deep convolutional network. Background technique [0002] With the in-depth development of remote sensing technology in the fields of ground object observation and target recognition, people's demand for high-resolution remote sensing images is increasing day by day. Improving hardware equipment is the most direct way to obtain high-resolution remote sensing images, but its cost is high, the development period is long, and it will cause problems such as noise and slow transmission rate, and it is not easy to maintain and has poor flexibility. ) reconstruction starts from the image information itself, and reconstructs a high-resolution image from one or more low-resolution images, which is an economical and convenient technology to improve image resolution. ...

AI Technical Summary

Problems solved by technology

In China, Xu Ran and others proposed an SR reconstruction algorithm based on dual-channel convolution, which improved the reconstruction signal-to-noise ratio at the expense of model structure complexity and efficiency, but there are still overfitting and edge problems

Images