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Proceedings Paper

A new method to estimate SNR of remote sensing imagery
Author(s): Bo Zhu; Chuanrong Li; Xinhong Wang; Chaoliang Wang
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Paper Abstract

The signal-to-noise ratio (SNR) of a remote sensing image is one of the most important indicators to evaluate the quality of the image, and also can reflect the SNR performance of a remote sensing payload to a great extent. Meanwhile, the SNR determines the information precision of a remote sensing image by which researchers could use the spectral characteristics to identify the surface features. Optical remote sensing images are usually contaminated by Gaussian white noise. Surface features often interfere with each other when imaging, which increases the difficulty of SNR evaluation. For heterogeneous region, the interference between different features is stronger and could not be removed easily. For homogeneous region, same features present the same or similar characteristics, showing as similar digital number (DN) values, so the interference between same features could be removed in some way. One of the ways to remove the interference between same features is to do subtraction operation between the adjacent row DNs or column DNs in homogeneous region. And the residuals, due to subtraction, are more indicative to the noises. This paper presents a novel method for SNR estimation of optical remote sensing images. Firstly, calculating the column residuals between the same features in homogeneous region. Secondly, doing subtraction operation to calculate the row residuals between the same features in homogeneous region. Thirdly, integrating the column and row residuals to evaluate the SNR. In this paper, the new method and a traditional typical method are used to estimate the SNRs of measured images. By analyzing the results of the two methods, we can find the new one is more stable and accurate. This method provides a new way to evaluate the SNR performance of optical remote sensing payload onboard.

Paper Details

Date Published: 24 October 2017
PDF: 6 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 1046209 (24 October 2017); doi: 10.1117/12.2281522
Show Author Affiliations
Bo Zhu, Academy of Opto-Electronics (China)
Chuanrong Li, Academy of Opto-Electronics (China)
Xinhong Wang, Academy of Opto-Electronics (China)
Chaoliang Wang, Academy of Opto-Electronics (China)

Published in SPIE Proceedings Vol. 10462:
AOPC 2017: Optical Sensing and Imaging Technology and Applications
Yadong Jiang; Haimei Gong; Weibiao Chen; Jin Li, Editor(s)

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