Application of the wavelet transform for feature extraction in the analysis of hyperspectral laser-induced fluorescence data

Abstract

Remote-sensing approaches for environmental protection and exploration have evolved rapidly in the last decade. Among the new operational tools, hyperspectral Fluorescent LiDAR System (FLS®) lidar has demonstrated a high sensitivity and the ability to function in complex environments for real-time, robust oil-spill monitoring on airborne or ship-borne analytical platforms. The capabilities of such analytical platforms include real-time analysis of laser-induced fluorescence (LIF) data. Although numerous examples of the application of signal theory to the analysis of hyperspectral data appear in the remote-sensing literature, the conventional data analysis strategies are not well adapted to the practical issues of the LIF applications. The aim of this article is to provide a new approach for LIF lidar analytical platforms, which is focused on the specifics of hyperspectral LIF data. The approach is based on structural data analysis and interpretation, through which more detailed spectral matching is performed. This article is based on a simulated experiment in which the spectra of actual seawater and well-known types of petroleum products were combined to demonstrate the wavelet-transform-based analysis of LIF data. The final part of the article demonstrates the application of the wavelet transform to the structural analysis of LIF data from field experiments for the detection and identification of oil products in difficult environmental conditions.