Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis
AbstractCrude oil spills have negative consequences on the economy, environment, health and society in which they occur, and the severity of the consequences depends on how quickly these spills are detected once they begin. Several methods have been employed for spill detection, including real time remote surveillance by flying aircrafts with surveillance teams. Other methods employ various sensors, including visible sensors. This paper presents an algorithm to automatically detect the presence of crude oil spills in images acquired using visible light sensors. Images of crude oil spills used in the development of the algorithm were obtained from the Shell Petroleum Development Company (SPDC) Nigeria website The major steps of the detection algorithm are image preprocessing, crude oil color segmentation, sky elimination segmentation, Region of Interest (ROI) extraction, ROI texture feature extraction, and ROI texture feature analysis and classification. The algorithm was developed using 25 sample images containing crude oil spills and demonstrated a sensitivity of 92% and an FPI of 1.43. The algorithm was further tested on a set of 56 case images and demonstrated a sensitivity of 82% and an FPI of 0.66. This algorithm can be incorporated into spill detection systems that utilize visible sensors for early detection of crude oil spills. View Full-Text
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Ejofodomi, O.; Ofualagba, G. Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis. J. Imaging 2017, 3, 47.
Ejofodomi O, Ofualagba G. Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis. Journal of Imaging. 2017; 3(4):47.Chicago/Turabian Style
Ejofodomi, O’tega; Ofualagba, Godswill. 2017. "Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis." J. Imaging 3, no. 4: 47.