Quantitative Inversion of Oil Film Thickness Based on Airborne Hyperspectral Data Using the 1DCNN_ GRU Model

Oil film thickness (OFT) is an important indicator for estimating the amount of oil spill, and accurately quantifying the OFT is of great significance for loss assessment. In this article, hyperspectral images (HSIs) of different OFTs (0.01-3.04 mm) through a ground experiment were obtained, and the spectral characteristics were analyzed. To address the issue of poor spectral separability for different OFTs, the 1-D convolutional neural network_gate recurrent unit (1DCNN_GRU) model was developed for the quantitative inversion of OFT. It was validated through experiments on airborne Cubert-S185 HSI. The experimental results indicated that: 1) the proposed 1DCNN_GRU model effectively addressed the issue of reduced quantitative inversion accuracy resulting from poor spectral separability. The inversion results of it outperformed those of the support vector regression (SVR), convolutional neural network (CNN), and gate recurrent unit (GRU) models. Moreover, the optimal time for hyperspectral sensor to monitor OFT was at noon. 2) The proposed model using airborne hyperspectral data exhibited excellent inversion performance for OFT greater than 0.07 mm, especially with the best performance in 0.60-0.90 mm. 3) The accuracy of HSI-based OFT inversion assisted by brightness temperature (BT) data was superior to that of OFT inversion using single-source data. In particular, the proposed model had advantages in the feature level and decision level inversion of OFT in the ranges of 0.01-0.30 and 1.00-3.04 mm, respectively. This research provides technical support for the detection of OFT.

Automated summary

In this study, a 1DCNN_GRU model was developed to quantitatively invert the oil film thickness (OFT) by analyzing spectral characteristics. Experimental results showed that the proposed model effectively addressed the issue of poor spectral separability and outperformed other models in terms of inversion accuracy. Additionally, airborne hyperspectral data performed well in OFT inversion, especially in certain ranges, and the use of brightness temperature data improved the inversion accuracy.