Single infrared remote sensing image super-resolution via supervised deep learning

Cong Zhang; Haopeng Zhang; Zhiguo Jiang

doi:10.1117/12.2573359

20 September 2020 Single infrared remote sensing image super-resolution via supervised deep learning

Cong Zhang, Haopeng Zhang, Zhiguo Jiang

Proceedings Volume 11533, Image and Signal Processing for Remote Sensing XXVI; 1153314 (2020) https://doi.org/10.1117/12.2573359
Event: SPIE Remote Sensing, 2020, Online Only

Abstract

The infrared observation sensors aboard remote sensing satellites play an important role in the applications of crop yield estimation, environmental protection, land resources survey, disaster monitoring, etc. But infrared remote sensing data is always in low resolution due to hardware limitations. It is a high cost-effective choice to improve the spatial resolution of infrared remote sensing data through super-resolution (SR) algorithm. Deep learning methods have made great breakthroughs in super-resolution of natural images. In this paper, we comparably study five recently popular supervised-deep-learning-based single image SR models for the purpose of super-resolving infrared images, including SRGAN, ESRGAN, LapSRN, RCAN, and SRFBN. We first test the performance of models trained by natural images on infrared remote sensing images to obtain a benchmark, and then specially fine-tune the SR models using infrared images of Landsat8 in a transfer-learning manner. We evaluate the performance of all these fine-tuned models on infrared images with three indicators including PSNR, SSIM, and NIQE. The experimental results show that the SRFBN model achieves the best generalization ability and SR performance. Therefore, we suggest using SRFBN for super-resolution reconstruction of single infrared remote sensing image in applications.

Conference Presentation

Citation Download Citation

Cong Zhang, Haopeng Zhang, and Zhiguo Jiang "Single infrared remote sensing image super-resolution via supervised deep learning", Proc. SPIE 11533, Image and Signal Processing for Remote Sensing XXVI, 1153314 (20 September 2020); https://doi.org/10.1117/12.2573359

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available