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Computational and Applied Mathematics

, Volume 36, Issue 2, pp 825–842 | Cite as

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

  • Vahid Sadeghi
  • Farshid Farnood Ahmadi
  • Hamid Ebadi
Article

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).

Keywords

Automatic relative radiometric normalization Histogram modeling Multi-temporal satellite images Regression Thresholding 

Mathematics Subject Classification

62J05: Linear regression 91C20: Clustering 93E24: Least squares 62H25: Principal components 

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Copyright information

© SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2015

Authors and Affiliations

  • Vahid Sadeghi
    • 1
  • Farshid Farnood Ahmadi
    • 2
  • Hamid Ebadi
    • 1
  1. 1.Faculty of Geodesy and Geomatics EngineeringK.N.Toosi University of TechnologyTehranIran
  2. 2.Department of Geomatics Engineering, Faculty of Civil EngineeringUniversity of TabrizTabrizIran

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