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11 October 2019 Super-resolution reconstruction of remote sensing images based on convolutional neural network
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Abstract

A method of super-resolution reconstruction of remote sensing images based on convolutional neural network is proposed to address the problems of low-resolution and poor visual quality of remote sensing images. In this method, a sample database with high-resolution (HR) and low-resolution (LR) remote sensing images was constructed. A convolutional neural network was then obtained by determining the intrinsic relationship between HR and LR remote sensing images in the sample database. Multiple pairs of HR and LR images were selected from the sample database and sent into a six-layer convolutional neural network. The experimental results showed that compared with other learning-based methods, such as the fast super-resolution convolutional neural network (FSRCNN), the image quality obtained by our method is improved both subjectively and objectively. Moreover, the training time was ∼17  %   less than in the FSRCNN method.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$28.00 © 2019 SPIE
Yu Tian, Rui-Sheng Jia, Shao-Hua Xu, Rong Hua, and Meng-Di Deng "Super-resolution reconstruction of remote sensing images based on convolutional neural network," Journal of Applied Remote Sensing 13(4), 046502 (11 October 2019). https://doi.org/10.1117/1.JRS.13.4.046502
Received: 19 June 2019; Accepted: 19 September 2019; Published: 11 October 2019
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