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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 12(4) (2020)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 4587-4602
S&M2430 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.3135 Published: December 29, 2020 Improved Slanted Edge Methods of Measuring Modulation Transfer Function Based on Structured Total Least L1-, L2-norm Edge Fitting for Urban Remote Sensing Images [PDF] Yanmin Jin, Yifeng Li, Xiaohua Tong, Chao Wang, and Sicong Liu (Received September 30, 2020; Accepted December 16, 2020) Keywords: MTF, structured total least L1-, L2-norm, slanted edge approach, urban remote sensing image
In this paper, we present improved slanted edge methods of measuring the modulation transfer function (MTF) based on structured total least L1-, L2-norm edge fitting for urban remote sensing images. The structured total least L1-, L2-norm methods are used to establish slanted edge fitting models, which take the errors in both the design matrix and observation vector in the fitting model into consideration. The slanted edge fitting parameters are estimated under the two norm criteria of L1 and L2. The proposed methods are applied to both simulated and actual images. The results showed that the edge fitting parameters and MTF values calculated by the proposed methods are closer to the true values than those obtained by the traditional slanted edge method based on classical least-squares fitting. It is also found that when the data contain a large amount of noise, the structured total least L1-norm edge fitting has the greatest robustness.
Corresponding author: Chao Wang  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Yanmin Jin, Yifeng Li, Xiaohua Tong, Chao Wang, and Sicong Liu, Improved Slanted Edge Methods of Measuring Modulation Transfer Function Based on Structured Total Least L1-, L2-norm Edge Fitting for Urban Remote Sensing Images , Sens. Mater., Vol. 32, No. 12, 2020, p. 4587-4602. |
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