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International Journal of Remote Sensing Volume 30, 2009 - Issue 4
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Original Articles

Application of the active learning method to the retrieval of pigment from spectral remote sensing reflectance data

H. Taheri Shahraiyni Institute for Space Sciences, Freie Universität Berlin, Carl‐Heinrich‐Becker‐Weg 6‐10, 12165 Berlin, Germany; Department of Civil Engineering, Sharif University of Technology, Azadi Avenue, Tehran, IranCorrespondence[email protected] [email protected]
,
S. Bagheri Shouraki Department of Computer Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran
,
F. Fell Informus GmbH, Gustav‐Meyer‐Allee 25, 13355 Berlin, Germany
,
M. Schaale Institute for Space Sciences, Freie Universität Berlin, Carl‐Heinrich‐Becker‐Weg 6‐10, 12165 Berlin, Germany
,
J. Fischer Institute for Space Sciences, Freie Universität Berlin, Carl‐Heinrich‐Becker‐Weg 6‐10, 12165 Berlin, Germany
,
A. Tavakoli Department of Electrical Engineering, Amir Kabir University of Technology, Tehran, Iran
,
R. Preusker Institute for Space Sciences, Freie Universität Berlin, Carl‐Heinrich‐Becker‐Weg 6‐10, 12165 Berlin, Germany
,
M. Tajrishy Department of Civil Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran
,
M. Vatandoust Inland Waters Aquaculture Institute, P.O. Box 66, Bandar Anzali, Iran
&
H. Khodaparast Inland Waters Aquaculture Institute, P.O. Box 66, Bandar Anzali, Iran
 show all
Pages 1045-1065 | Received 18 Sep 2006, Accepted 20 Jul 2008, Published online: 23 Mar 2009
 
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Abstract

Due to the noise that is present in remote sensing data, a robust method to retrieve information is needed. In this study, the active learning method (ALM) is applied to spectral remote sensing reflectance data to retrieve in‐water pigment. The heart of the ALM is a fuzzy interpolation method that is called the ink drop spread (IDS). Three datasets (SeaBAM, synthetic and NOMAD) are used for the evaluation of the selected ALM approach. Comparison of the ALM with the ocean colour 4 (OC4) algorithm and the artificial neural network (ANN) algorithm demonstrated the robustness of the ALM approach in retrieval of in‐water constituents from remote sensing reflectance data. In addition, the ALM identified and ranked the most relevant wavelengths for chlorophyll and pigment retrieval.

Acknowledgements

We acknowledge UNESCO for financial support of this study through the UNESCO/Keizo OBUCHI Research Fellowships Programme 2005.

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