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


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).


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 


  1. Biday SG, Bhosle U (2010) Radiometric correction of multitemporal satellite imagery. J Comput Sci 6:1027CrossRefGoogle Scholar
  2. Chavez PS Jr (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24:459–479CrossRefGoogle Scholar
  3. Crist EP, Cicone RC (1984) A physically-based transformation of Thematic Mapper data–The TM Tasseled Cap. Geosci Remote Sens IEEE Trans:256–263Google Scholar
  4. Elvidge CD, Yuan D, Weerackoon RD, Lunetta RS (1995) Relative radiometric normalization of Landsat Multispectral Scanner (MSS) data using a automatic scattergram-controlled regression. Photogramm Eng Remote Sens 61:1255–1260Google Scholar
  5. Hall FG, Strebel DE, Nickeson JE, Goetz SJ (1991) Radiometric rectification: toward a common radiometric response among multidate, multisensor images. Remote Sens Environ 35:11–27CrossRefGoogle Scholar
  6. Jensen JR, Cowen DC (1999) Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogramm Eng Remote Sens 65:611–622Google Scholar
  7. Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11:23–27Google Scholar
  8. Richards JA (2013) Remote sensing digital image analysis: an introduction. Springer, New YorkCrossRefGoogle Scholar
  9. Salvaggio C (1993) Radiometric scene normalization utilizing statistically invariant features. Proceedings of the workshop on atmospheric correction of Landsat imagery. Torrance, California, pp 155–159Google Scholar
  10. Schott JR, Salvaggio C, Volchok WJ (1988) Radiometric scene normalization using pseudoinvariant features. Remote Sens Environ 26:1–16CrossRefGoogle Scholar
  11. Sezgin M (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13:146–168CrossRefGoogle Scholar
  12. Ya’allah SM, Saradjian MR (2005) Automatic normalization of satellite images using unchanged pixels within urban areas. Inf Fusion 6:235–241CrossRefGoogle Scholar
  13. Yang X, Lo C (2000) Relative radiometric normalization performance for change detection from multi-date satellite images. Photogramm Eng Remote Sens 66:967–980Google Scholar

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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