Active contour segmentation for hyperspectral oil spill remote sensing

Oil spills could occur in many conditions, which results in pollution of the natural resources, marine environment and economic health of the area. Whenever we need to identify oil spill, confirm the location or get the shape and acreage of oil spill, we have to get the edge information of oil slick images firstly. Hyperspectral remote sensing imaging is now widely used to detect oil spill. Active Contour Models (ACMs) is a widely used image segmentation method that utilizes the geometric information of objects within images. Region based models are less sensitive to noise and give good performance for images with weak edges or without edges. One of the popular Region based ACMs, active contours without edges Models, is implemented by Chan-Vese. The model has the property of global segmentation to segment all the objects within an image irrespective of the initial contour. In this paper, we propose an improved CV model, which can perform well in the oil spill hyper-spectral image segmentation. The energy function embeds spectral and spatial information, introduces the vector edge stopping function, and constructs a novel length term. Results of the improved model on airborne hyperspectral oil spill images show that it improves the ability of distinguishing between oil spills and sea water, as well as the capability of noise reduction.