Remote-sensing image super-resolution reconstruction method based on generative adversarial network

Abstract

The invention relates to the technical field of computer image processing, specifically to a remote-sensing image super-resolution reconstruction method based on a generative adversarial network. Theremote-sensing image super-resolution reconstruction method comprises the following steps: constructing a remote-sensing image super-resolution reconstruction model consisting of a generator network and a discriminator network; introducing a scene constraint sub-network into the generator network to solve the problem of scene change, introducing an edge enhancement sub-network to solve the problemof edge transition smoothness of a generated image, introducing TV loss for noise suppression, and introducing a content fidelity to deal with the problems of instability and gradient disappearance in the training process; and introducing spectrum normalization into a discriminator network to control the performances of a discriminator so as to promoting better learning of the generator. The remote-sensing image super-resolution reconstruction method provided by the invention has the following advantages: a high-quality high-resolution remote-sensing image can be generated based on a low-resolution remote-sensing image; the precision of the low-resolution remote-sensing image in classification detection is effectively improved; the problems of edge transition smoothness and scene change in super-resolution of the remote-sensing image are solved; meanwhile, the problems of training instability and gradient disappearance under the GAN network are solved.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a remote sensing image super-resolution reconstruction method based on a generative confrontation network. Background technique [0002] The resolution of remote sensing images is an important factor affecting the interpretation of remote sensing images. High-resolution remote sensing images contain more details and are more conducive to interpretation tasks such as remote sensing image classification and target detection. Therefore, it is more desirable to obtain higher-resolution images. Remote Sensing Image. Due to the limitation of hardware cost and technology such as sensors, the acquisition of high-resolution remote sensing images has always been difficult and costly, which seriously limits the application of remote sensing images. Super-resolution (RS) reconstruction can use computer software to generate corresponding high-resolution images from one or m...

AI Technical Summary

Problems solved by technology

[0005] The technical problem solved by the present invention is that the existing remote sensing image super-resolution reconstruction method based on the GANs method will cause problems such as scene changes, excessive edge smoothing and training instability.

The super-resolution reconstruction method of remote sensing images based on generative confrontation network provided by the present invention reconstructs low-resolution remote sensing images into high-resolution remote sensing images through super-resolution, which solves the problems of scene changes, excessive edge smoothing and unstable training

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