TY - JOUR AU - Taravat, Alireza AU - Del Frate, Fabio PY - 2012 DA - 2012/05/10 TI - Development of band ratioing algorithms and neural networks to detection of oil spills using Landsat ETM+ data JO - EURASIP Journal on Advances in Signal Processing SP - 107 VL - 2012 IS - 1 AB - Accurate knowledge of the spatial extents and distributions of an oil spill is very impor-tant for efficient response. This is because most petroleum products spread rapidly on the water surface when released into the ocean, with the majority of the affected area becoming covered by very thin sheets. This article presents a study for examining the feasibility of Landsat ETM+ images in order to detect oil spills pollutions. The Landsat ETM+ images for 1st, 10th, 17th May 2010 were used to study the oil spill in Gulf of Mexico. In this article, an attempt has been made to perform ratio operations to enhance the feature. The study concluded that the bands difference between 660 and 560 nm, division at 660 and 560 and division at 825 and 560 nm, normalized by 480 nm provide the best result. Multilayer perceptron neural network classifier is used in order to perform a pixel-based supervised classification. The result indicates the potential of Landsat ETM+ data in oil spill detection. The promising results achieved encourage a further analysis of the potential of the optical oil spill detection approach. SN - 1687-6180 UR - https://doi.org/10.1186/1687-6180-2012-107 DO - 10.1186/1687-6180-2012-107 ID - Taravat2012 ER -