Comparison of five relative radiometric normalization techniques for remote sensing monitoring
Relative radiometric normalization (RRN) minimized radiometric differences among images caused by inconsistencies of acquisition conditions (such as weather, season, sensor, etc.) rather than change in surface reflectance. Five methods of RRN, i. e. image regression (IR), pseudo-invariant features (...
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| Format: | Article |
| Language: | English |
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Zhejiang University Press
2005-05-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
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| Online Access: | https://www.academax.com/doi/10.3785/1008-9209.2005.03.0269 |
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| _version_ | 1849412739301113856 |
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| author | DING Li-xia ZHOU Bin WANG Ren-chao |
| author_facet | DING Li-xia ZHOU Bin WANG Ren-chao |
| author_sort | DING Li-xia |
| collection | DOAJ |
| description | Relative radiometric normalization (RRN) minimized radiometric differences among images caused by inconsistencies of acquisition conditions (such as weather, season, sensor, etc.) rather than change in surface reflectance. Five methods of RRN, i. e. image regression (IR), pseudo-invariant features (PIF), dark set-bright set normalization (DB), no-change set radiometric normalization (NC), and histogram matching (HM), were applied to 1993 and 2001 Landsat TM/ETM+image of Jiashan County for evaluating their performance in relation to land cover detection. No other parameters and variables but image pixel digital values were used, so these methods were very easy to apply, especially for historical remote sensing images. The root-mean-square error and the dynamic range were employed in comparing and evaluating the images normalized by five methods. A change detection algorithm, i. e., image subtraction, was applied to compare the effects on change detection. The results showed that DB worked best among the five methods at the study area, the PIF worked better. Finally, factors affecting the performance of relative radiometric normalization and the conditions of applying these methods were identified and discussed. |
| format | Article |
| id | doaj-art-286013c5e4594767a26a635dcac7ea6e |
| institution | Kabale University |
| issn | 1008-9209 2097-5155 |
| language | English |
| publishDate | 2005-05-01 |
| publisher | Zhejiang University Press |
| record_format | Article |
| series | 浙江大学学报. 农业与生命科学版 |
| spelling | doaj-art-286013c5e4594767a26a635dcac7ea6e2025-08-20T03:34:21ZengZhejiang University Press浙江大学学报. 农业与生命科学版1008-92092097-51552005-05-013126927610.3785/1008-9209.2005.03.026910089209Comparison of five relative radiometric normalization techniques for remote sensing monitoringDING Li-xiaZHOU BinWANG Ren-chaoRelative radiometric normalization (RRN) minimized radiometric differences among images caused by inconsistencies of acquisition conditions (such as weather, season, sensor, etc.) rather than change in surface reflectance. Five methods of RRN, i. e. image regression (IR), pseudo-invariant features (PIF), dark set-bright set normalization (DB), no-change set radiometric normalization (NC), and histogram matching (HM), were applied to 1993 and 2001 Landsat TM/ETM+image of Jiashan County for evaluating their performance in relation to land cover detection. No other parameters and variables but image pixel digital values were used, so these methods were very easy to apply, especially for historical remote sensing images. The root-mean-square error and the dynamic range were employed in comparing and evaluating the images normalized by five methods. A change detection algorithm, i. e., image subtraction, was applied to compare the effects on change detection. The results showed that DB worked best among the five methods at the study area, the PIF worked better. Finally, factors affecting the performance of relative radiometric normalization and the conditions of applying these methods were identified and discussed.https://www.academax.com/doi/10.3785/1008-9209.2005.03.0269relative radiometric normalizationremote sensing monitoringTM/ETM+ |
| spellingShingle | DING Li-xia ZHOU Bin WANG Ren-chao Comparison of five relative radiometric normalization techniques for remote sensing monitoring 浙江大学学报. 农业与生命科学版 relative radiometric normalization remote sensing monitoring TM/ETM+ |
| title | Comparison of five relative radiometric normalization techniques for remote sensing monitoring |
| title_full | Comparison of five relative radiometric normalization techniques for remote sensing monitoring |
| title_fullStr | Comparison of five relative radiometric normalization techniques for remote sensing monitoring |
| title_full_unstemmed | Comparison of five relative radiometric normalization techniques for remote sensing monitoring |
| title_short | Comparison of five relative radiometric normalization techniques for remote sensing monitoring |
| title_sort | comparison of five relative radiometric normalization techniques for remote sensing monitoring |
| topic | relative radiometric normalization remote sensing monitoring TM/ETM+ |
| url | https://www.academax.com/doi/10.3785/1008-9209.2005.03.0269 |
| work_keys_str_mv | AT dinglixia comparisonoffiverelativeradiometricnormalizationtechniquesforremotesensingmonitoring AT zhoubin comparisonoffiverelativeradiometricnormalizationtechniquesforremotesensingmonitoring AT wangrenchao comparisonoffiverelativeradiometricnormalizationtechniquesforremotesensingmonitoring |