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

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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.
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Document Type: Research Article

Affiliations: 1: Institute for Space Sciences, Freie Universitat Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165 Berlin, Germany,Department of Civil Engineering, Sharif University of Technology, Tehran, Iran 2: Department of Computer Engineering, Sharif University of Technology, Tehran, Iran 3: Informus GmbH, Gustav-Meyer-Allee 25, 13355 Berlin, Germany 4: Institute for Space Sciences, Freie Universitat Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165 Berlin, Germany 5: Department of Electrical Engineering, Amir Kabir University of Technology, Tehran, Iran 6: Department of Civil Engineering, Sharif University of Technology, Tehran, Iran 7: Inland Waters Aquaculture Institute, Bandar Anzali, Iran

Publication date: January 1, 2009

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