Abstract
The quantity and location of offshore platforms are of great significance for marine oil spill monitoring and offshore oil-gas development. In the past, multiphase medium- and low-resolution optical or radar images have been used to remove the interference of ship targets based on the static position of a platform to extract the offshore platform, resulting in large demands and high image data costs. According to the difference in shape between offshore platforms (not elongated) and ships (elongated shapes) in SAR (synthetic aperture radar) images, this paper proposes an automatic extraction method for offshore platforms in single SAR images based on a dual-step-modified model. First, the two-parameter CFAR (constant false alarm rate) algorithm was used to detect the possible offshore platform targets; then, the Hough transform was introduced to detect and eliminate ship targets with linear structures. Finally, the final offshore platform was obtained. Experiments were carried out in four study areas in the Beibu Gulf basin and the Pearl River estuary basin in the northern South China Sea. The results show that the method has a good extraction effect in the above research area, and the extraction accuracy rate of offshore platforms is 86.75%. A single SAR image can obtain satisfactory extraction results, which greatly saves on image data cost.
Highlights
With the evolution of offshore drilling technology, the exploitation of offshore oil-gas resources by countries around the world has increased, and the number of offshore platforms has increased each year
Focusing on the above problems, this paper proposes a method for extracting offshore platforms using a single SAR image based on a dual-step-modified model
The offshore platform extraction method based on a dual-step-modified model was applied to tiles of GF-3 SAR images covering the four selected study areas in the Beibu Gulf basin and the Pearl
Summary
With the evolution of offshore drilling technology, the exploitation of offshore oil-gas resources by countries around the world has increased, and the number of offshore platforms has increased each year. The locations and quantities of offshore platforms reflect the strength and capability of offshore oil-gas development in a country. Knowledge on the distribution of offshore platforms is of great value for the detection of oil spills. Obtaining the quantity and spatial distribution information of offshore platforms has important practical significance for the sustainable development and utilization of marine oil-gas resources and marine environmental monitoring. The use of remote sensing images for the extraction of large-scale offshore platforms has become an economic and efficient method to do so. Liu Yongxue et al (2016) adopted time-series and multi-refinement strategies to extract offshore platforms using landsat-8 Operational Land Imager (OLI) imagery with relatively complete datasets that largely overcome the influence of the maritime.
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