A remote sensing image super-resolution reconstruction method based on a convolutional neural network of channel attention

Abstract

The invention discloses a remote sensing image super-resolution reconstruction method based on a convolutional neural network of channel attention. According to the method, a channel attention mechanism is introduced into the proposed convolutional neural network, and the characteristic expression capability of the network can be improved by utilizing the mutual dependency relationship among the characteristic channels. The method mainly comprises the following steps: designing and building a convolutional neural network model based on channel attention; Constructing a training sample by usinga smaller remote sensing database, and pre-training the deep convolutional neural network model constructed in the step 1; 2, when model training in the step 2 reaches convergence, a larger remote sensing database is added for fine adjustment, and the performance of the network is further improved; In the remote sensing image reconstruction stage, a low-resolution image is used as input, and themodel trained in the step 3 is used for reconstructing a final high-resolution image. According to the method, a better reconstruction effect can be obtained, and the method is an effective remote sensing image super-resolution reconstruction method.

Description

technical field [0001] The invention relates to a remote sensing image super-resolution reconstruction technology, in particular to a remote sensing image super-resolution reconstruction method based on channel attention convolutional neural network, which belongs to the field of digital image processing. Background technique [0002] With the rapid development of modern aerospace technology, remote sensing images are more and more widely used in various fields, and their resolution requirements are also getting higher and higher. However, in the process of acquiring remote sensing images, the resolution of remote sensing images decreases due to atmospheric disturbance, frequency aliasing, imaging, transmission and many other factors. Since relying on hardware to increase the resolution is limited by the manufacturing process and production cost, the method of using software to increase the resolution of the image is called super-resolution reconstruction technology. [000...

AI Technical Summary

Problems solved by technology

However, many super-resolution reconstruction methods based on convolutional neural networks treat each feature channel equally, which ignores the weight of each feature channel and reduces the feature expression ability of the network. There is still room for improvement in the performance of the reconstruction network

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