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).
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References
Biday SG, Bhosle U (2010) Radiometric correction of multitemporal satellite imagery. J Comput Sci 6:1027
Chavez PS Jr (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24:459–479
Crist EP, Cicone RC (1984) A physically-based transformation of Thematic Mapper data–The TM Tasseled Cap. Geosci Remote Sens IEEE Trans:256–263
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 61:1255–1260
Hall FG, Strebel DE, Nickeson JE, Goetz SJ (1991) Radiometric rectification: toward a common radiometric response among multidate, multisensor images. Remote Sens Environ 35:11–27
Jensen JR, Cowen DC (1999) Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogramm Eng Remote Sens 65:611–622
Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11:23–27
Richards JA (2013) Remote sensing digital image analysis: an introduction. Springer, New York
Salvaggio C (1993) Radiometric scene normalization utilizing statistically invariant features. Proceedings of the workshop on atmospheric correction of Landsat imagery. Torrance, California, pp 155–159
Schott JR, Salvaggio C, Volchok WJ (1988) Radiometric scene normalization using pseudoinvariant features. Remote Sens Environ 26:1–16
Sezgin M (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13:146–168
Ya’allah SM, Saradjian MR (2005) Automatic normalization of satellite images using unchanged pixels within urban areas. Inf Fusion 6:235–241
Yang X, Lo C (2000) Relative radiometric normalization performance for change detection from multi-date satellite images. Photogramm Eng Remote Sens 66:967–980
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Communicated by José Mario Martínez.
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Sadeghi, V., Ahmadi, F.F. & Ebadi, H. A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery. Comp. Appl. Math. 36, 825–842 (2017). https://doi.org/10.1007/s40314-015-0254-z
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DOI: https://doi.org/10.1007/s40314-015-0254-z
Keywords
- Automatic relative radiometric normalization
- Histogram modeling
- Multi-temporal satellite images
- Regression
- Thresholding