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. © 2018 American Society for Photogrammetry and Remote Sensing.