Deep belief active contours (DBAC) with its application to oil spill segmentation from remotely sensed sea surface imagery
Albalooshi F.A., Sidike P., Sagan V., Albalooshi Y., Asari V.K.
PublisherAmerican Society for Photogrammetry and Remote Sensing
SourcetitlePhotogrammetric Engineering and Remote Sensing
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.