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Analyzing the Impact of Simulated Multispectral Images on Water Classification Accuracy by Means of Spectral Characteristics
Corresponding Author(s) : Ewa Głowienka
Geomatics and Environmental Engineering,
Vol. 14 No. 1 (2020): Geomatics and Environmental Engineering
Abstract
Remote sensing is widely applied in examining the parameters of the state and quality of water. Spectral characteristics of water are strictly connected with the dispersion of electromagnetic radiation by suspended matter and the absorption of radiation by water and chlorophyll a and b.
Multispectral sensor ALI has bands within the ranges of electromagnetic radiation: blue and infrared, absent in sensors such as Landsat, SPOT, or Aster. The main goal of the article was to examine the influence of the presence of these bands on water classification accuracy carried out for simulated images ALI, Landsat, Spot, and Aster. The simulation of images was based on the hyperspectral image from a Hyperion sensor. Due to the spectral properties of water, all the operations on the images were carried out for the set of bands in visible and near-infrared (VNIR) spectral range. In the framework of these studies, the impact of removing individual bands or sets of bands on the classification results was tested. Tests were carried out for the area of the water body of the Dobczyce Reservoir. It was observed that the lack of a spectral response in the infrared range of ALI image can reduce the accuracy of a classification by as much as 60%. On the other hand, the lack of blue and red bands in the dataset for the classification decreased the accuracy of water classification by 15% and 10%, respectively.
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- Brando V., Dekker A.: Satellite hyperspectral remote sensing for estimating estu‑ arine and coastal water quality. IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 6, 2003, pp. 1378–1387.
- Hejmanowska B., Drzewiecki W., Głowienka E., Mularz S., Zagajewski B., Sanecki J.: Próba integracji satelitarnych obrazów hiperspektralnych z nieobrazo‑ wymi naziemnymi danymi spektrometrycznymi na przykładzie Zbiornika Dob‑ czyckiego. Archiwum Fotogrametrii, Kartografii i Teledetekcji, vol. 16, 2006, pp. 207–216.
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- Kruse F., Perry S.: Regional Mineral Mapping by Extending Hyperspectral Signatures Using Multispectral Data. [in:] 2007 IEEE Aerospace Conference, 2004.
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- Earth Observing 1 (EO‑1), U.S. Geological Survey, https://archive.usgs.gov/archive/sites/eo1.usgs.gov/hyperion.html [access: 2.10.2019].
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References
Brando V., Dekker A.: Satellite hyperspectral remote sensing for estimating estu‑ arine and coastal water quality. IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 6, 2003, pp. 1378–1387.
Hejmanowska B., Drzewiecki W., Głowienka E., Mularz S., Zagajewski B., Sanecki J.: Próba integracji satelitarnych obrazów hiperspektralnych z nieobrazo‑ wymi naziemnymi danymi spektrometrycznymi na przykładzie Zbiornika Dob‑ czyckiego. Archiwum Fotogrametrii, Kartografii i Teledetekcji, vol. 16, 2006, pp. 207–216.
Koponen S., Pulliainen J., Kallio K., Hallikainen M.: Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. Remote Sensing Environment, vol. 79, 2002, pp. 51–59.
Mularz S., Drzewiecki W., Hejmanowska B., Pirowski T.: Wykorzystanie teledetekcji satelitarnej do badania procesu akumulacji zanieczyszczeń w rejonie Zbiornika Dobczyckiego. Archiwum Fotogrametrii, Kartografii i Teledetekcji, vol. 16, 2006, pp. 425–435.
Rundquist D., Han L., Schalles J., Peake J.: Remote measurement of algal chlorophyll in surface waters: the case for the first derivative of reflectance near 690 nm. Photogrammetric Engineering and Remote Sensing, vol. 62, 1996, pp. 195–200.
Schalles J., Yacobi Y.: Remote detection and seasonal patterns of phycocynin, carotenoid and chlorophyll pigments in eutrophic waters. Archive for Hydrobiologie – Special Issues Advancements in Limnology, vol. 55, 2000, pp. 153–168.
Gitelson A.: The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, vol. 13, 1992, pp. 3367–3373.
Kavzoglu T.: Simulating Landsat ETM imagery using DAIS 7915 hyperspectral scanner data. International Journal of Remote Sensing, vol. 25, no. 22, 2004, pp. 5049–5067.
Barry P., Mendenhall J., Jarecke P., Folkman M., Pearlman J., Markham B.: EO‑1 Hyperion hyperspectral aggregation and comparison with EO‑1 Advanced Land Imager and Landsat 7 ETM+. Geoscience and Remote Sensing Symposium, IEEE International, vol. 3, 2002, pp. 1648–1651.
Börner M., Wiest L., Keller P., Reulke R., Richter R., Schläpfer D., Schaepman M.: SENSOR: a tool for the simulation of hyperspectral remote sensing sys‑ tems. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 55, no. 6, 2001, pp. 299–312.
Goetz A., Kindel B., Ferri M., Qu Z.: HATCH: Results from Simulated Radiances, AVIRIS and Hyperion. IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 6, 2003, pp. 1215–1222.
Jarecke P., Barry P., Pearlman J., Markham B.: Aggregation of Hyperion hyperspectral spectral bands into Landsat 7 ETM+ spectral bands. [in:] IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 Interna‑ tional Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), Sydney, NSW, Australia, 2001, vol. 6, pp. 2822–2824.
Kruse F., Perry S.: Regional Mineral Mapping by Extending Hyperspectral Signatures Using Multispectral Data. [in:] 2007 IEEE Aerospace Conference, 2004.
Jacquemoud S., Bacour C., Poilvé H., Frangi J.: Comparison of Four Radiative Transfer Models to Simulate Plant Canopies Reflectance: Direct and Inverse Mode. Remote Sensing of Environment, vol. 74, 2000, pp. 471–481.
Schläpfer D., Böerner A., Schaepman M.: The potential of spectral resampling techniques for the simulation of APEX imagery based on AVIRIS data. [in:] Summaries of the Eighth Annual JPL Airborne Earth Science Workshop, February 9–11, 1999, 1999, pp. 377–384.
Steven D., Malthus T., Baret F., Xud H., Chopping M.: Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment, vol. 88, 2003, pp. 412–422.
Milton E., Choi K.: Estimating the spectral response function of the CASI‑2. [in:] RSPSoc2004: Mapping and Resources Management (Annual Conference of the Remote Sensing and Photogrammetry Society, Aberdeen, Scotland, 7–10 Sep 2004), Remote Sensing and Photogrammetry Society, 2004, pp. 1–15.
Earth Observing 1 (EO‑1), U.S. Geological Survey, https://archive.usgs.gov/archive/sites/eo1.usgs.gov/hyperion.html [access: 2.10.2019].
Głowienka E.: Przetwarzanie wstępne danych z hiperspektralnego sensora satelitarnego Hyperion. Archiwum Fotogrametrii, Kartografii i Teledetekcji, vol. 18, 2008, pp. 131–140.
Głowienka E.: Analiza porównawcza metod przetwarzania danych hiperspektralnych o zróżnicowanej dokładności. AGH 2014 [PhD thesis, unpublished].