Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

Abstract

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.

Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data

“…The classification-accuracy assessment using these three 2-by-2 matrix domains (diagonal, horizontal, and vertical) differs from other published investigations exploring oil-slick LDA classifiers, which do not report their accuracies in such a succinct manner as we do here. Some papers ignore the vertical-analysis metrics (e.g., [35]) or even both, horizontal and vertical (e.g., [34,36]).…”

“…These authors confirmed that LDAs were effective with 81% to 87% success rates depending on the choice of accuracy metric; but three other methods were more effective. In an attempt to use LDAs, among three other techniques for detecting oil spills, Liu et al [36] explored three different marine radar images to build a semi-automatic adaptive thresholding detection method. Their LDAs were capable of flagging about 80% of the spills visually identified by human interpretation, with LDAs being the second-best technique.…”

Offshore Oil Slicks Detection From SAR Images Through The Mask-RCNN Deep Learning Model

“…Several works [33] investigate the use of the capabilities of Convolutional Neural Networks (CNNs) in many steps of the classical detection process that outperform conventional approaches. For example, a text classifier based on a neural network algorithm is used for the detection of dark features by [21]. The work of [14] employs a stacked auto-encoder (SAE), and use a Deep Belief Network (DBN) to optimize the polarimetric feature sets.…”

Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image

“…However, due to the confidentiality policy of commercial companies, the core technologies have not yet been published. Since 2010, based on the shipboard radar images collected in the oil spill accident of Dalian on July 16, 2010, the researchers of Dalian Maritime University have successively published the achievements of oil spill monitoring by using adaptive threshold methods, active contour models, and machine learning methods [16][17][18][19][20][21][22][23].…”