VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING
Anthropocene is featured with increasing human population and global changes that strongly affect landscapes at an unprecedented pace. As a flagship, the coastal fringe is subject to an accelerated conversion of natural areas into agricultural ones, in turn, into urban ones, generating hazardous soi...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/675/2020/isprs-archives-XLIII-B2-2020-675-2020.pdf |
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| author | D. James A. Collin A. Collin A. Mury S. Costa |
| author_facet | D. James A. Collin A. Collin A. Mury S. Costa |
| author_sort | D. James |
| collection | DOAJ |
| description | Anthropocene is featured with increasing human population and global changes that strongly affect landscapes at an unprecedented pace. As a flagship, the coastal fringe is subject to an accelerated conversion of natural areas into agricultural ones, in turn, into urban ones, generating hazardous soil artificialization. Very high resolution (VHR) technologies such as airborne LiDAR or UAV imageries are good assets to model the topography and classify the land use/land cover (LULC), helping local management. Even if their spatial resolution suits with the management scale, their extent covers a few km<sup>2</sup>, making large-scale monitoring complex and time-consuming. VHR spaceborne imagery has a great potential to address this spatial challenge given its regional acquisition. This research proposes to evaluate the capabilities of a Pleiades-1 stereo-satellite multispectral imagery (blue, green, red, BGR, and near-infrared, NIR) to both model the surface topography and classify LULC. Horizontal and vertical accuracies of the photogrammetry-driven digital surface model (DSM) attain 0.53 m and 0.65 m, respectively. Nine LULC generic classes are studied using the maximum likelihood (ML) and support vector machine (SVM) algorithms. The classification accuracy of the basic BGR (reaching 84.64 % and 76.13 % with ML and SVM, respectively) is improved by the DSM contribution (5.49 % and 2.91 % for ML and SVM, respectively), and the NIR contribution (6.78 % and 3.89 % for ML and SVM, respectively). The gain of the DSM-NIR combination totals 8.91 % and 8.40 % for ML and SVM, respectively, making the ML-based full combination the best performance (93.55 %). |
| first_indexed | 2024-12-19T03:46:10Z |
| format | Article |
| id | doaj.art-98e951ce9a244d22bcb9345f1c9346b8 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-19T03:46:10Z |
| publishDate | 2020-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-98e951ce9a244d22bcb9345f1c9346b82022-12-21T20:37:06ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342020-08-01XLIII-B2-202067568210.5194/isprs-archives-XLIII-B2-2020-675-2020VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNINGD. James0A. Collin1A. Collin2A. Mury3S. Costa4EPHE, PSL Université Paris, CNRS UMR 6554 LETG, 35800 Dinard, FranceEPHE, PSL Université Paris, CNRS UMR 6554 LETG, 35800 Dinard, FranceLabEx CORAIL, Moorea, French PolynesiaEPHE, PSL Université Paris, CNRS UMR 6554 LETG, 35800 Dinard, FranceNormandie Univ, UNICAEN, CNRS, LETG, F-14000 Caen, FranceAnthropocene is featured with increasing human population and global changes that strongly affect landscapes at an unprecedented pace. As a flagship, the coastal fringe is subject to an accelerated conversion of natural areas into agricultural ones, in turn, into urban ones, generating hazardous soil artificialization. Very high resolution (VHR) technologies such as airborne LiDAR or UAV imageries are good assets to model the topography and classify the land use/land cover (LULC), helping local management. Even if their spatial resolution suits with the management scale, their extent covers a few km<sup>2</sup>, making large-scale monitoring complex and time-consuming. VHR spaceborne imagery has a great potential to address this spatial challenge given its regional acquisition. This research proposes to evaluate the capabilities of a Pleiades-1 stereo-satellite multispectral imagery (blue, green, red, BGR, and near-infrared, NIR) to both model the surface topography and classify LULC. Horizontal and vertical accuracies of the photogrammetry-driven digital surface model (DSM) attain 0.53 m and 0.65 m, respectively. Nine LULC generic classes are studied using the maximum likelihood (ML) and support vector machine (SVM) algorithms. The classification accuracy of the basic BGR (reaching 84.64 % and 76.13 % with ML and SVM, respectively) is improved by the DSM contribution (5.49 % and 2.91 % for ML and SVM, respectively), and the NIR contribution (6.78 % and 3.89 % for ML and SVM, respectively). The gain of the DSM-NIR combination totals 8.91 % and 8.40 % for ML and SVM, respectively, making the ML-based full combination the best performance (93.55 %).https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/675/2020/isprs-archives-XLIII-B2-2020-675-2020.pdf |
| spellingShingle | D. James A. Collin A. Collin A. Mury S. Costa VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING |
| title_full | VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING |
| title_fullStr | VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING |
| title_full_unstemmed | VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING |
| title_short | VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING |
| title_sort | very high resolution land use and land cover mapping using pleiades 1 stereo imagery and machine learning |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/675/2020/isprs-archives-XLIII-B2-2020-675-2020.pdf |
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