A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery
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 bas...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sadeghi, Vahid [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
Automatic relative radiometric normalization |
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| Anmerkung: |
© SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Computational and applied mathematics - Berlin : Springer, 2003, 36(2015), 2 vom: 10. Juli, Seite 825-842 |
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| Übergeordnetes Werk: |
volume:36 ; year:2015 ; number:2 ; day:10 ; month:07 ; pages:825-842 |
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| DOI / URN: |
10.1007/s40314-015-0254-z |
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| Katalog-ID: |
SPR036738530 |
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| 245 | 1 | 2 | |a A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery |
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| 520 | |a 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). | ||
| 650 | 4 | |a Automatic relative radiometric normalization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Histogram modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multi-temporal satellite images |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Regression |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Thresholding |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ahmadi, Farshid Farnood |4 aut | |
| 700 | 1 | |a Ebadi, Hamid |4 aut | |
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10.1007/s40314-015-0254-z doi (DE-627)SPR036738530 (SPR)s40314-015-0254-z-e DE-627 ger DE-627 rakwb eng Sadeghi, Vahid verfasserin aut A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 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). Automatic relative radiometric normalization (dpeaa)DE-He213 Histogram modeling (dpeaa)DE-He213 Multi-temporal satellite images (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Thresholding (dpeaa)DE-He213 Ahmadi, Farshid Farnood aut Ebadi, Hamid aut Enthalten in Computational and applied mathematics Berlin : Springer, 2003 36(2015), 2 vom: 10. Juli, Seite 825-842 (DE-627)47617502X (DE-600)2171678-X 1807-0302 nnns volume:36 year:2015 number:2 day:10 month:07 pages:825-842 https://dx.doi.org/10.1007/s40314-015-0254-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2015 2 10 07 825-842 |
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10.1007/s40314-015-0254-z doi (DE-627)SPR036738530 (SPR)s40314-015-0254-z-e DE-627 ger DE-627 rakwb eng Sadeghi, Vahid verfasserin aut A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 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). Automatic relative radiometric normalization (dpeaa)DE-He213 Histogram modeling (dpeaa)DE-He213 Multi-temporal satellite images (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Thresholding (dpeaa)DE-He213 Ahmadi, Farshid Farnood aut Ebadi, Hamid aut Enthalten in Computational and applied mathematics Berlin : Springer, 2003 36(2015), 2 vom: 10. Juli, Seite 825-842 (DE-627)47617502X (DE-600)2171678-X 1807-0302 nnns volume:36 year:2015 number:2 day:10 month:07 pages:825-842 https://dx.doi.org/10.1007/s40314-015-0254-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2015 2 10 07 825-842 |
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10.1007/s40314-015-0254-z doi (DE-627)SPR036738530 (SPR)s40314-015-0254-z-e DE-627 ger DE-627 rakwb eng Sadeghi, Vahid verfasserin aut A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 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). Automatic relative radiometric normalization (dpeaa)DE-He213 Histogram modeling (dpeaa)DE-He213 Multi-temporal satellite images (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Thresholding (dpeaa)DE-He213 Ahmadi, Farshid Farnood aut Ebadi, Hamid aut Enthalten in Computational and applied mathematics Berlin : Springer, 2003 36(2015), 2 vom: 10. Juli, Seite 825-842 (DE-627)47617502X (DE-600)2171678-X 1807-0302 nnns volume:36 year:2015 number:2 day:10 month:07 pages:825-842 https://dx.doi.org/10.1007/s40314-015-0254-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2015 2 10 07 825-842 |
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10.1007/s40314-015-0254-z doi (DE-627)SPR036738530 (SPR)s40314-015-0254-z-e DE-627 ger DE-627 rakwb eng Sadeghi, Vahid verfasserin aut A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 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). Automatic relative radiometric normalization (dpeaa)DE-He213 Histogram modeling (dpeaa)DE-He213 Multi-temporal satellite images (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Thresholding (dpeaa)DE-He213 Ahmadi, Farshid Farnood aut Ebadi, Hamid aut Enthalten in Computational and applied mathematics Berlin : Springer, 2003 36(2015), 2 vom: 10. Juli, Seite 825-842 (DE-627)47617502X (DE-600)2171678-X 1807-0302 nnns volume:36 year:2015 number:2 day:10 month:07 pages:825-842 https://dx.doi.org/10.1007/s40314-015-0254-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2015 2 10 07 825-842 |
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10.1007/s40314-015-0254-z doi (DE-627)SPR036738530 (SPR)s40314-015-0254-z-e DE-627 ger DE-627 rakwb eng Sadeghi, Vahid verfasserin aut A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 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). Automatic relative radiometric normalization (dpeaa)DE-He213 Histogram modeling (dpeaa)DE-He213 Multi-temporal satellite images (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Thresholding (dpeaa)DE-He213 Ahmadi, Farshid Farnood aut Ebadi, Hamid aut Enthalten in Computational and applied mathematics Berlin : Springer, 2003 36(2015), 2 vom: 10. Juli, Seite 825-842 (DE-627)47617502X (DE-600)2171678-X 1807-0302 nnns volume:36 year:2015 number:2 day:10 month:07 pages:825-842 https://dx.doi.org/10.1007/s40314-015-0254-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2015 2 10 07 825-842 |
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Sadeghi, Vahid @@aut@@ Ahmadi, Farshid Farnood @@aut@@ Ebadi, Hamid @@aut@@ |
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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. 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Sadeghi, Vahid |
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Sadeghi, Vahid misc Automatic relative radiometric normalization misc Histogram modeling misc Multi-temporal satellite images misc Regression misc Thresholding A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery |
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A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery Automatic relative radiometric normalization (dpeaa)DE-He213 Histogram modeling (dpeaa)DE-He213 Multi-temporal satellite images (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Thresholding (dpeaa)DE-He213 |
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new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery |
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A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery |
| abstract |
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). © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2015 |
| abstractGer |
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). © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2015 |
| abstract_unstemmed |
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). © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2015 |
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A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery |
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https://dx.doi.org/10.1007/s40314-015-0254-z |
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Ahmadi, Farshid Farnood Ebadi, Hamid |
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10.1007/s40314-015-0254-z |
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| score |
7.3982677 |