DEPTH RETRIEVAL FROM A RESERVOIR USING A CONDITIONAL-BASED MODEL
Water depth is an important measure for nautical charts. Accurate methods to provide water depth information are expensive and time costing. For this reason, since late 70’s, it started to be estimate by multispectral sensors with empirical models. In the literature there is no investigation using e...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-11-01
|
| 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/XLII-3-W12-2020/231/2020/isprs-archives-XLII-3-W12-2020-231-2020.pdf |
| _version_ | 1818301190709968896 |
|---|---|
| author | M. B. Nunes A. P. Dal Poz E. Alcântara M. Curtarelli |
| author_facet | M. B. Nunes A. P. Dal Poz E. Alcântara M. Curtarelli |
| author_sort | M. B. Nunes |
| collection | DOAJ |
| description | Water depth is an important measure for nautical charts. Accurate methods to provide water depth information are expensive and time costing. For this reason, since late 70’s, it started to be estimate by multispectral sensors with empirical models. In the literature there is no investigation using empirical models partitioned in depth intervals, for this reason, we evaluated the accuracy of partitioned and single bathymetric models. The results have shown that to retrieve depth in from 0 to 15 m the single model provided an RMSE of 3.57 m, with a bias of about −0.83 m; while the RMSE for the partitioned model was 2.29 m with a bias of 0.41 m. For updating nautical charts using multispectral sensors it was concluded that the partitioned model can provide a better result than using a single model. |
| first_indexed | 2024-12-13T05:19:05Z |
| format | Article |
| id | doaj.art-048311ad0f584185867ffef01cc0c7f0 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-13T05:19:05Z |
| publishDate | 2020-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-048311ad0f584185867ffef01cc0c7f02022-12-21T23:58:21ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342020-11-01XLII-3-W12-202023123510.5194/isprs-archives-XLII-3-W12-2020-231-2020DEPTH RETRIEVAL FROM A RESERVOIR USING A CONDITIONAL-BASED MODELM. B. Nunes0A. P. Dal Poz1E. Alcântara2M. Curtarelli3São Paulo State University (UNESP), BrazilSão Paulo State University (UNESP), BrazilSão Paulo State University (UNESP), BrazilFederal University of Santa Catarina (UFSC), BrazilWater depth is an important measure for nautical charts. Accurate methods to provide water depth information are expensive and time costing. For this reason, since late 70’s, it started to be estimate by multispectral sensors with empirical models. In the literature there is no investigation using empirical models partitioned in depth intervals, for this reason, we evaluated the accuracy of partitioned and single bathymetric models. The results have shown that to retrieve depth in from 0 to 15 m the single model provided an RMSE of 3.57 m, with a bias of about −0.83 m; while the RMSE for the partitioned model was 2.29 m with a bias of 0.41 m. For updating nautical charts using multispectral sensors it was concluded that the partitioned model can provide a better result than using a single model.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W12-2020/231/2020/isprs-archives-XLII-3-W12-2020-231-2020.pdf |
| spellingShingle | M. B. Nunes A. P. Dal Poz E. Alcântara M. Curtarelli DEPTH RETRIEVAL FROM A RESERVOIR USING A CONDITIONAL-BASED MODEL The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | DEPTH RETRIEVAL FROM A RESERVOIR USING A CONDITIONAL-BASED MODEL |
| title_full | DEPTH RETRIEVAL FROM A RESERVOIR USING A CONDITIONAL-BASED MODEL |
| title_fullStr | DEPTH RETRIEVAL FROM A RESERVOIR USING A CONDITIONAL-BASED MODEL |
| title_full_unstemmed | DEPTH RETRIEVAL FROM A RESERVOIR USING A CONDITIONAL-BASED MODEL |
| title_short | DEPTH RETRIEVAL FROM A RESERVOIR USING A CONDITIONAL-BASED MODEL |
| title_sort | depth retrieval from a reservoir using a conditional based model |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W12-2020/231/2020/isprs-archives-XLII-3-W12-2020-231-2020.pdf |
| work_keys_str_mv | AT mbnunes depthretrievalfromareservoirusingaconditionalbasedmodel AT apdalpoz depthretrievalfromareservoirusingaconditionalbasedmodel AT ealcantara depthretrievalfromareservoirusingaconditionalbasedmodel AT mcurtarelli depthretrievalfromareservoirusingaconditionalbasedmodel |