Marine oil spills cause great pollution to the marine environment and require development of efficient cleaning plans. Accurate identification of the oil type involved in the spill is of great significance for rapid and effective treatment. Hyperspectral remote sensing plays an important role in oil spill detection and oil type identification. We designed an outdoor oil spill experiment to simulate an oil spill in a marine environment. Five common oil types were selected as the experimental starting materials: crude oil, fuel oil, diesel oil, gasoline, and palm oil. Hyperspectral data of the five oils were collected from different solar times by Analytical Spectral Devices (ASD) FieldSpec4. The relationship between the spectral absorption baseline height of the different oil types and solar time is investigated. The characteristic analysis method of spectral standard deviation was used to obtain characteristic bands of the different oil types. Using both full spectrum and selected characteristic bands, oil type identification experiments were performed using the Support Vector Machine (SVM) model, respectively. The results show that oil type identification using selected characteristic bands is 3.70% more accurate compared with that using the full spectrum, reaching 83.33%.

Marine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characterization using a single classifier and therefore difficult to accurate monitoring of marine oil spills. In this paper, we develop a decision fusion algorithm to integrate deep learning methods and shallow learning methods based on multi-scale features for improving oil spill detection accuracy in the case of limited samples. Based on the multi-scale features after wavelet transform, two deep learning methods and two classical shallow learning algorithms are used to extract oil slick information from hyperspectral oil spill images. The decision fusion algorithm based on fuzzy membership degree is introduced to fuse multi-source oil spill information. The research shows that oil spill detection accuracy using the decision fusion algorithm is higher than that of the single detection algorithms. It is worth noting that oil spill detection accuracy is affected by different scale features. The decision fusion algorithm under the first-level scale features can further improve the accuracy of oil spill detection. The overall classification accuracy of the proposed method is 91.93%, which is 2.03%, 2.15%, 1.32%, and 0.43% higher than that of SVM, DBN, 1D-CNN, and MRF-CNN algorithms, respectively.

Yang, J.-F.; Wan, J.-H.; Ma, Y.; Zhang, J.; Hu, Y.-B., and Jiang, Z.-C., 2019. Oil spill hyperspectral remote sensing detection based on DCNN with multi-scale features. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 332-339. Coconut Creek (Florida), ISSN 0749-0208. In this paper, a deep convolutional neural network (DCNN) model is developed for sea surface oil spill accurate detection using multi-scale features with AISA+ airborne hyperspectral remote sensing image. Based on multi-scale features after wavelet transform (WT), a deep convolution neural network classification algorithm with seven-layer network structure is proposed to detect oil spill of the Penglai 19-3C platform in 2011, and the accuracy evaluation is conducted on the overall situation. The detection results of proposed method are compared with those of the classical SVM, RF and DBN method. The results show that the accuracies of DCNN for oil spill detection based on different-scale features are all more than 85 %, which are much better than those of SVM, RF and DBN method, and the detection results can maintain the continuity of oil film at sea. Among them, the detection result of DCNN model based on spectral feature information combined with low-frequency component of 1-level wavelet transform has the best effect and highest detection accuracy, reaching 87.51 %.

The types of marine oil spill pollution are closely related to source tracing and pollution disposal, which is an important basis for oil spill pollution punishment. The types of marine oil spill pollution generally include different types of oil products as well as crude oil and its emulsions in different states. This paper designed and implemented two outdoor oil spill simulation experiments, obtained the hyperspectral and thermal infrared remote sensing data of different oil spill pollution types, constructed a hyperspectral recognition algorithm of oil spill pollution type based on classical machine learning, ensemble learning and deep learning models, and explored to improve the identification ability of hyperspectral oil spill pollution type by adding thermal infrared features. The research shows that hyperspectral combined with thermal infrared remote sensing can effectively improve the recognition accuracy of different oils, but thermal infrared remote sensing cannot be used to distinguish crude oil and high concentration water-in-oil emulsion. On this basis, the recognition ability of hyperspectral combined with thermal infrared for different oil film thicknesses is also discussed. The combination of hyperspectral and thermal infrared remote sensing can provide important technical support for emergency response to maritime emergencies and oil spill monitoring business of relevant departments.

In recent years, marine oil spill accidents have occurred frequently, seriously endangering marine ecological security. It is highly important to protect the marine ecological environment by carrying out research on the estimation of sea oil spills based on remote sensing technology. In this paper, we combine deep learning with remote sensing technology and propose an oil thickness inversion generative adversarial and convolutional neural network (OG-CNN) model for oil spill emergency monitoring. The model consists of a self-expanding module for the oil film spectral feature data and an oil film thickness inversion module. The feature data self-expanding module can automatically select spectral feature intervals with good spectral separability based on the measured spectral data and then expand the number of samples using a generative adversarial network (GAN) to enhance the generalization of the model. The oil film thickness inversion module is based on a one-dimensional convolutional neural network (1D-CNN). It extracts the characteristics of the spectral feature data of oil film with different thicknesses, and then accurately inverts the oil film’s absolute thickness. In this study, emulsification was not a factor considered, the results show that the absolute oil thickness inversion accuracy of the OG-CNN model proposed in this paper can reach 98.12%, the coefficient of determination can reach 0.987, and the mean deviation remains within ±0.06% under controlled experimental conditions. In the model stability test, the model maintains relatively stable inversion results under the interference of random Gaussian noise. The accuracy of the oil film thickness inversion result remains above 96%, the coefficient of determination can reach 0.973, and the mean deviation is controlled within ±0.6%, which indicates excellent robustness.