Deep Belief Active Contours (DBAC) with Its Application to Oil Spill Segmentation from Remotely Sensed Sea Surface Imagery
In this paper, we propose a machine learning-based oil spill segmentation using aerial images. In detail, a novel deep neural network-based object segmentation, named Deep Believe Active Contours (DBAC), is introduced, where a pre-trained deep belief neural network is utilized to guide the moments of active contours. Results show that (1) Unsupervised pre-trained deep neural network can efficiently control the evolution of active contour segmentation of oil spill regions; and (2) When applying the proposed DBAC algorithm on the test data from an oil spill image database, it produced a recall rate of 66% and a precision rate of 60%, which outperformed the state-of-the-art methods in the range of 4% ∼ 18% and 1% ∼ 10%, respectively. Moreover, DBAC produced a better Hausdorff distance (an amount of 13.34) compared to the competing methods. These results show the promises of DBAC for the task of oil spill segmentation in ocean environment.
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Document Type: Review Article
Publication date: July 1, 2018
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