Analyzing the Impact of Simulated Multispectral Images on Water Classification Accuracy by Means of Spectral Characteristics - Publication - Bridge of Knowledge

Your account

login

Search

Analyzing the Impact of Simulated Multispectral Images on Water Classification Accuracy by Means of Spectral Characteristics

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 absorp-tion of radiation by water and chlorophyll a and b.Multispectral sensor ALI has bands within the ranges of electromagnetic radia-tion: 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 hyper-spectral 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 data-set for the classification decreased the accuracy of water classification by 15% and 10%, respectively.

Citations

  • 1

    CrossRef

  • 0

    Web of Science

  • 3

    Scopus

Authors (2)

Cite as

Full text

download paper
downloaded 40 times
Publication version
Accepted or Published Version
License
Creative Commons: CC-BY open in new tab

Keywords

Details

Category:
Articles
Type:
artykuły w czasopismach
Published in:
Geomatics and Environmental Engineering no. 14, pages 47 - 58,
ISSN: 1898-1135
Language:
English
Publication year:
2020
Bibliographic description:
Michałowska K., Głowienka E.: Analyzing the Impact of Simulated Multispectral Images on Water Classification Accuracy by Means of Spectral Characteristics// Geomatics and Environmental Engineering -Vol. 14,iss. 1 (2020), s.47-58
DOI:
Digital Object Identifier (open in new tab) 10.7494/geom.2020.14.1.47
Bibliography: test
  1. 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. open in new tab
  2. 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.
  3. Koponen S., Pulliainen J., Kallio K., Hallikainen M.: Lake water quality classi- fication with airborne hyperspectral spectrometer and simulated MERIS data. Re- mote Sensing Environment, vol. 79, 2002, pp. 51-59. open in new tab
  4. Mularz S., Drzewiecki W., Hejmanowska B., Pirowski T.: Wykorzystanie te- ledetekcji satelitarnej do badania procesu akumulacji zanieczyszczeń w rejonie Zbiornika Dobczyckiego. Archiwum Fotogrametrii, Kartografii i Teledetekcji, vol. 16, 2006, pp. 425-435.
  5. 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.
  6. Schalles J., Yacobi Y.: Remote detection and seasonal patterns of phycocynin, carot- enoid and chlorophyll pigments in eutrophic waters. Archive for Hydrobiologie - Special Issues Advancements in Limnology, vol. 55, 2000, pp. 153-168. open in new tab
  7. Gitelson A.: The peak near 700 nm on radiance spectra of algae and water: relation- ships of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, vol. 13, 1992, pp. 3367-3373. open in new tab
  8. 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. open in new tab
  9. 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 Sympo- sium, IEEE International, vol. 3, 2002, pp. 1648-1651. open in new tab
  10. Börner M., Wiest L., Keller P., Reulke R., Richter R., Schläpfer D., Schaep- man 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. open in new tab
  11. Goetz A., Kindel B., Ferri M., Qu Z.: HATCH: Results from Simulated Radi- ances, AVIRIS and Hyperion. IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 6, 2003, pp. 1215-1222. open in new tab
  12. Jarecke P., Barry P., Pearlman J., Markham B.: Aggregation of Hyperion hyper- spectral 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. open in new tab
  13. Kruse F., Perry S.: Regional Mineral Mapping by Extending Hyperspectral Sig- natures Using Multispectral Data. [in:] 2007 IEEE Aerospace Conference, 2004. open in new tab
  14. 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. open in new tab
  15. Schläpfer D., Böerner A., Schaepman M.: The potential of spectral resampling techniques for the simulation of APEX imagery based on AVIRIS data. [in:] Sum- maries of the Eighth Annual JPL Airborne Earth Science Workshop, February 9-11, 1999, 1999, pp. 377-384.
  16. Steven D., Malthus T., Baret F., Xud H., Chopping M.: Intercalibration of veg- etation indices from different sensor systems. Remote Sensing of Environment, vol. 88, 2003, pp. 412-422. open in new tab
  17. Milton E., Choi K.: Estimating the spectral response function of the CASI-2. [in:] RSPSoc2004: Mapping and Resources Management (Annual Conference of the Re- mote Sensing and Photogrammetry Society, Aberdeen, Scotland, 7-10 Sep 2004), Remote Sensing and Photogrammetry Society, 2004, pp. 1-15. open in new tab
  18. Earth Observing 1 (EO-1), U.S. Geological Survey, https://archive.usgs.gov/ archive/sites/eo1.usgs.gov/hyperion.html [access: 2.10.2019]. open in new tab
  19. Głowienka E.: Przetwarzanie wstępne danych z hiperspektralnego sensora sateli- tarnego Hyperion. Archiwum Fotogrametrii, Kartografii i Teledetekcji, vol. 18, 2008, pp. 131-140.
  20. Głowienka E.: Analiza porównawcza metod przetwarzania danych hiperspektral- nych o zróżnicowanej dokładności. AGH 2014 [PhD thesis, unpublished]. open in new tab
Verified by:
Gdańsk University of Technology

seen 142 times

Recommended for you

Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification

  • A. Stateczny,
  • S. M. Bolugallu,
  • P. B. Divakarachari
  • + 2 authors
2022

The Application of Satellite Image Analysis in Oil Spill Detection

2022

Deep neural networks approach to skin lesions classification — A comparative analysis

2017

Contour Analysis of Bleeding Regions in Endoscopic Images

2012
Meta Tags