Fine-Grained Oil Types Identification Based on Reflectance Spectrum: Implication for the Requirements on the Spectral Resolution of Hyperspectral Remote Sensors

Abstract

Effectively obtaining the information about the types of oil pollutants in a spill event can help determine the source of spill and formulate the plan of emergency responses. Researchers have reported the feasibility of fine-grained oil types identification using reflectance spectra, but its requirements on spectra resolution were seldom considered. To answer this question, this study examined the oil types identification accuracies using the reflection spectra with different number of bands. The reflectance spectra of various types of oil samples were collected with a high-resolution hyperspectral remote sensor, and then resampled to coarser spectral resolution. Some of the coarse-resolution spectra were built based on the central wavelengths of AVIRIS and Landsat 8 in visible bands, so as to evaluates the potential of fine-grained oil types identification using these sensors. Three kinds of machine learning algorithms were introduced as the classifier. The identification results using the reflection spectra at different resolution showed that the machine learning models could identify oil types based on high-resolution reflectance spectra. In term of the spectra at coarser resolution, the models were still able to provide accurate predictions until the number of bands was reduced to about 16 in the visible range. Therefore, the high-resolution hyperspectral sensors (e.g., AVIRIS) have the potential of fine-grained oil types identification, but that may not be accomplished by satellite-based hyperspectral sensors with less than 4 visible bands.