Quantitative analysis of petroleum hydrocarbon contaminated soils using spectroscopy, spectral unmixing and deep neural networks

Oil spill can be described as a global issue, which causes serious concern to human life and the environment, therefore early identification and remedial measures taken at an early stage is very important. Spectral Unmixing is the process of identifying the constituent spectra of a mixed pixel referred to as endmembers and computing the corresponding proportions or abundances within each pixel in a given image. Many spectral unmixing methods have being proposed in the literature based on linear or nonlinear models. Deep neural networks allow computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstractions. Deep neural networks have shown excellent performance on various task on image processing with better accuracy compared to shallow learning networks and are increasingly gaining popularity with Hyperspectral imaging. Here we propose to use deep neural network to quantify different Hydrocarbon (HCs) substances in sandy clay loam soil type. Hyperspectral data sets have been acquired using mixtures of different HCs with the soil type. Vertex Component Analysis (VCA) algorithm was used to identify the endmembers and deep neural network was used to predict the quantity of each endmember. Experimental result shows the effectiveness of the proposed method with high accuracy.