Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

Safe and efficient berthing is essential to ensure maritime transportation and the safety of ships and ports. Three-dimensional (3D) light detection and ranging (LiDAR) can monitor and support ship berthing because it provides abundant target information and offers excellent advantages in measuring accuracy. Hence, a berthing information extraction system has been developed based on 3D LiDAR. Principal component analysis is used to calculate a ship’s heading and the normal vector, and the feature points of the bow and stern are determined. The segments passing through the points are obtained via region growing. The bow and stern are recognized by the similarity of the normal vector of the segments and ship’s heading according to the positions of the ship relative to the berth through visibility analysis. Qualitative and quantitative calculated analyses of the distance, velocity, and approach angle of the dynamic ship’s bow and stern relative to the dock are performed based on the feature points of 3D LiDAR data. A laser scanner, used as the detection unit, efficiently monitored the Ro–Ro ship Ocean Island berthing at Lushun Port in field experiments. On-site applications demonstrated the feasibility and effectiveness of the proposed method for the recognition of dynamic ship target and ensuring safe ship berthing.

The routine surveillance of oil spills in major ports is important. However, existing techniques and sensors are unable to trace oil and micron-thin oil films on the surface of seawater. Therefore, we designed and studied a coastal-mounted sensor, using ultraviolet-induced fluorescence and fluorescence-filter systems (FFSs), to monitor oil spills and overcome the disadvantages of traditional surveillance systems. Using seawater from the port of Lingshui (Yellow Sea, China) and six oil samples of different types, we found that diesel oil’s relative fluorescence intensity (RFI) was significantly higher than those of heavy fuel and crude oils in the 180–300 nm range—in the 300–400 nm range, the RFI value of diesel is far lower. The heavy fuel and crude oils exhibited an opposite trend in their fluorescence spectra. A photomultiplier tube, employed as the fluorescence detection unit, efficiently monitored different oils on seawater in field experiments. On-site tests indicated that this sensor system could be used as a coastal-mounted early-warning detection system for oil spills.