Vol. 12, No. 2, 113-123, 2013

Analysis of the performances of hyperspectral lidar for water pollution diagnostics
Innokenti Sobolev, and Sergey Babichenko

The paper is aimed at the analysis of the performances of hyperspectral lidar for detection and classification of oil pollution in water in comparison with a laser fluorosensor having a few discrete detection channels only. It is demonstrated that hyperspectral laser-induced fluorescence (HLIF) spectra include all relevant spectral information about the target in contrast to discrete detection channel sensor data. In order to extract significant features from HLIF data, a multi-resolutional analysis, namely the discrete wavelet transform (DWT), is applied. The feature extraction is automated using the sparsity-norm optimization method. The resulting features have a clear spectral representation and are used in automatic object classification. The classification results and selectivity are compared with discrete detection channel sensor data on a number of oil pollutants. The results of simulation experiments demonstrate the high value of classification accuracy and the ability to sub-classify similar organic compounds from single groups of objects. A comparison with discrete channel sensor data shows a significant increase in the overall performance of oil pollution detection and classification.

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DOI: 10.12760/01-2013-2-04

Submitted: 27 June 2013
Revised: 23 Sept 2013
Accepted: 23 Sept 2013
Published: 08 Oct 2013
Responsible editor: Rainer Reuter

Sobolev I & S Babichenko, 2013. Analysis of the performances of hyperspectral lidar for water pollution diagnostics. EARSeL eProceedings, 12(2): 113-123


EARSeL European Association of Remote Sensing Laboratories, Strasbourg, France


BIS Library and Information System, Carl von Ossietzky University of Oldenburg


ISSN 1729-3782