Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression
Radiometric precision is difficult to maintain in orbital images due to several factors (atmospheric conditions, Earth-sun distance, detector calibration, illumination, and viewing angles). These unwanted effects must be removed for radiometric consistency among temporal images, leaving only land-le...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-05-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/5/6/2763 |
| _version_ | 1818500222304649216 |
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| author | Ana Paula Ferreira de Carvalho Cristiano Rosa Silva Alan R. Gillespie Roberto Arnaldo Trancoso Gomes Osmar Abílio de Carvalho Renato Fontes Guimarães Nilton Correia Silva |
| author_facet | Ana Paula Ferreira de Carvalho Cristiano Rosa Silva Alan R. Gillespie Roberto Arnaldo Trancoso Gomes Osmar Abílio de Carvalho Renato Fontes Guimarães Nilton Correia Silva |
| author_sort | Ana Paula Ferreira de Carvalho |
| collection | DOAJ |
| description | Radiometric precision is difficult to maintain in orbital images due to several factors (atmospheric conditions, Earth-sun distance, detector calibration, illumination, and viewing angles). These unwanted effects must be removed for radiometric consistency among temporal images, leaving only land-leaving radiances, for optimum change detection. A variety of relative radiometric correction techniques were developed for the correction or rectification of images, of the same area, through use of reference targets whose reflectance do not change significantly with time, i.e., pseudo-invariant features (PIFs). This paper proposes a new technique for radiometric normalization, which uses three sequential methods for an accurate PIFs selection: spectral measures of temporal data (spectral distance and similarity), density scatter plot analysis (ridge method), and robust regression. The spectral measures used are the spectral angle (Spectral Angle Mapper, SAM), spectral correlation (Spectral Correlation Mapper, SCM), and Euclidean distance. The spectral measures between the spectra at times t1 and t2 and are calculated for each pixel. After classification using threshold values, it is possible to define points with the same spectral behavior, including PIFs. The distance and similarity measures are complementary and can be calculated together. The ridge method uses a density plot generated from images acquired on different dates for the selection of PIFs. In a density plot, the invariant pixels, together, form a high-density ridge, while variant pixels (clouds and land cover changes) are spread, having low density, facilitating its exclusion. Finally, the selected PIFs are subjected to a robust regression (M-estimate) between pairs of temporal bands for the detection and elimination of outliers, and to obtain the optimal linear equation for a given set of target points. The robust regression is insensitive to outliers, i.e., observation that appears to deviate strongly from the rest of the data in which it occurs, and as in our case, change areas. New sequential methods enable one to select by different attributes, a number of invariant targets over the brightness range of the images. |
| first_indexed | 2024-12-10T20:39:52Z |
| format | Article |
| id | doaj.art-57a0c412aa164121a9b5d6e641ee6039 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T20:39:52Z |
| publishDate | 2013-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-57a0c412aa164121a9b5d6e641ee60392022-12-22T01:34:24ZengMDPI AGRemote Sensing2072-42922013-05-01562763279410.3390/rs5062763Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust RegressionAna Paula Ferreira de CarvalhoCristiano Rosa SilvaAlan R. GillespieRoberto Arnaldo Trancoso GomesOsmar Abílio de CarvalhoRenato Fontes GuimarãesNilton Correia SilvaRadiometric precision is difficult to maintain in orbital images due to several factors (atmospheric conditions, Earth-sun distance, detector calibration, illumination, and viewing angles). These unwanted effects must be removed for radiometric consistency among temporal images, leaving only land-leaving radiances, for optimum change detection. A variety of relative radiometric correction techniques were developed for the correction or rectification of images, of the same area, through use of reference targets whose reflectance do not change significantly with time, i.e., pseudo-invariant features (PIFs). This paper proposes a new technique for radiometric normalization, which uses three sequential methods for an accurate PIFs selection: spectral measures of temporal data (spectral distance and similarity), density scatter plot analysis (ridge method), and robust regression. The spectral measures used are the spectral angle (Spectral Angle Mapper, SAM), spectral correlation (Spectral Correlation Mapper, SCM), and Euclidean distance. The spectral measures between the spectra at times t1 and t2 and are calculated for each pixel. After classification using threshold values, it is possible to define points with the same spectral behavior, including PIFs. The distance and similarity measures are complementary and can be calculated together. The ridge method uses a density plot generated from images acquired on different dates for the selection of PIFs. In a density plot, the invariant pixels, together, form a high-density ridge, while variant pixels (clouds and land cover changes) are spread, having low density, facilitating its exclusion. Finally, the selected PIFs are subjected to a robust regression (M-estimate) between pairs of temporal bands for the detection and elimination of outliers, and to obtain the optimal linear equation for a given set of target points. The robust regression is insensitive to outliers, i.e., observation that appears to deviate strongly from the rest of the data in which it occurs, and as in our case, change areas. New sequential methods enable one to select by different attributes, a number of invariant targets over the brightness range of the images.http://www.mdpi.com/2072-4292/5/6/2763change-detectionspectral correlation mapperspectral angle mapperMahalanobis distanceEuclidean distancebi-temporal |
| spellingShingle | Ana Paula Ferreira de Carvalho Cristiano Rosa Silva Alan R. Gillespie Roberto Arnaldo Trancoso Gomes Osmar Abílio de Carvalho Renato Fontes Guimarães Nilton Correia Silva Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression Remote Sensing change-detection spectral correlation mapper spectral angle mapper Mahalanobis distance Euclidean distance bi-temporal |
| title | Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression |
| title_full | Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression |
| title_fullStr | Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression |
| title_full_unstemmed | Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression |
| title_short | Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression |
| title_sort | radiometric normalization of temporal images combining automatic detection of pseudo invariant features from the distance and similarity spectral measures density scatterplot analysis and robust regression |
| topic | change-detection spectral correlation mapper spectral angle mapper Mahalanobis distance Euclidean distance bi-temporal |
| url | http://www.mdpi.com/2072-4292/5/6/2763 |
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