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

Abstract
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.

ISSN 1729-3782