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Deep Belief Active Contours (DBAC) with Its Application to Oil Spill Segmentation from Remotely Sensed Sea Surface Imagery

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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: 01 July 2018

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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