Content awareness deep learning network-based remote sensing image super-resolution reconstruction method

Abstract

The invention discloses a content awareness deep learning network-based remote sensing image super-resolution reconstruction method. A comprehensive measurement index and a calculation method for content complexity of images are proposed; based on this, the sample images are classified by the content complexity; deep GAN models with low, medium and high complexity are built and trained; and according to the content complexity of the to-be-classified super-resolution input images, corresponding networks are selected for performing reconstruction. In order to improve learning performance of a GAN, an optimized loss function definition is given. The method overcomes the over-fitting and under-fitting contradiction ubiquitous in machine learning-based super-resolution reconstruction, and effectively improves the super-resolution reconstruction precision of the remote sensing images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image super-resolution reconstruction method, in particular to a remote sensing image super-resolution reconstruction method based on a content-aware deep learning network. technical background [0002] Remote sensing images with high spatial resolution can describe the ground objects more finely and provide rich detail information. Therefore, people often hope to obtain images with high spatial resolution. With the rapid development of space detection theory and technology, remote sensing images with meter-level or even sub-meter-level spatial resolution (such as IKNOS and QuickBird) have been gradually applied, but their temporal resolution is generally relatively low. On the contrary, some sensors with lower spatial resolution (such as MODIS) have high temporal resolution, and they can acquire a large-scale remote sensing image in a short time. If high-spatial-resol...

AI Technical Summary

Problems solved by technology

For complex images, more sample training is needed so that more image features can be learned, but such a network is prone to overfitting to images with simple content, resulting in blurred super-resolution results; on the contrary, reduce the training intensity, It can avoid the over-fitting phenomenon of images with simple content, but it will cause under-fitting problems of images with complex content, reducing the naturalness and fidelity of reconstructed images

How to ensure that the trained network can take into account the needs of high-quality reconstruction of complex and simple images at the same time is an unavoidable problem for deep learning-based methods in actual super-resolution applications.

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