Hyperspectral oil spill image segmentation using improved region-based active contour model

Abstract

Nowadays, the accidents of oil spill become more and more frequent, causing pollution to the natural resources, marine environment and lives in the sea. As a result, the detection of oil spill draws more and more attentions. One of the most popular region-based active contour models proposed by Chan and Vese, is widely used to image segmentation. But it can't segment hyperspectral oil spill image well, which has blurry boundaries, low distinction, and noise and so on. In order to segment oil spill region from the hyperspectral oil spill image accurately, we improved the region-based active contour model in this paper. For the energy functional, we firstly bring the thought of Fisher criterion into the fitting term to get a better classification result faster. Secondly, a new stop function based on gradient of spectral angle measurement is added into the length term, so as to take advantage of the edge information fully even it is blurry. At last, the model is extended to be able to segment desired material from the complex image with several classes in it. We take some experiments on synthetic and real hyperspectral images to verify the effectiveness of our model, and apply it to the airborne hyperspectral oil spill image. Results of the proposed model on synthetic and testing hyperspectral images show that it outperforms the CV model greatly, and does better than several other segmentation and classification algorithms. Results on hyperspectral oil spill images show that it improves the ability of distinguishing oil spills from sea water, even there are boats and flats in the image.