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Computational and Applied Mathematics : Volume 36

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A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery

Design of new $$4\times 4$$ S-box from finite commutative chain rings

Incompatible modes with Cartesian coordinates and application in quadrilateral finite element formulation

On the preconditioned AOR iterative method for Z-matrices

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Computational and Applied Mathematics : Volume 35

Computational and Applied Mathematics : Volume 34

Computational and Applied Mathematics : Volume 33

Computational and Applied Mathematics : Volume 32

A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery

Content Provider	Springer Nature Link
Author	Sadeghi, Vahid Ebadi, Hamid Ahmadi, Farshid Farnood
Copyright Year	2015
Abstract	Relative radiometric normalization (RRN) of multi-temporal satellite images minimizes the radiometric discrepancies between two images caused by inequalities in the acquisition conditions rather than changes in surface reflectance. In this paper, a new automatic RRN method was developed based on regression theory comprising the following techniques: Automatic detection of unchanged pixels, Histogram modeling of subject images, and Calculation of linear transformation coefficients for various categories of pixels according to their gray values in each band. The proposed method applies a new idea for unchanged pixels selection which increases the accuracy and automation level of the detection process. Also, a new idea is proposed for categorizing pixels according to their gray values. In this method, the number and interval of the categories are determined automatically and independently based on the histogram of subject images for each band. Thus, divergent influences of effective parameters such as atmosphere on different gray values are modeled. The method was implemented on two images taken by the TM sensor. Normalization results acquired by the proposed method were compared with the six conventional methods including: histogram matching, haze correction, minimum-maximum, mean-standard deviation, simple regression, no-change and modified regression using unchanged pixels. Experimental results confirmed the effectiveness of the proposed method in the automatic detection of unchanged pixels and minimizing any imaging condition effects (i.e., atmosphere and other effective parameters).
Starting Page	825
Ending Page	842
Page Count	18
File Format	PDF
ISSN	01018205
Journal	Computational and Applied Mathematics
Volume Number	36
Issue Number	2
e-ISSN	18070302
Language	English
Publisher	Springer International Publishing
Publisher Date	2015-07-10
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computational Mathematics and Numerical Analysis Mathematical Applications in Computer Science Thresholding Automatic relative radiometric normalization Regression Applications of Mathematics Histogram modeling Mathematical Applications in the Physical Sciences Multi-temporal satellite images
Content Type	Text
Resource Type	Article

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