An improved Otsu method for oil spill detection from SAR images

Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation

“…One example of this is the automatic estimation of seeds from the saliency map, as described in Section 3.2 and Section 3.3 . Unlike others, such as the works shown in [ 9 , 10 , 11 , 12 , 13 , 27 , 28 , 29 , 30 ], the proposed approach always uses full images of the mission as input without using pre-processing masks to eliminate both land and noise pixels. The processing of the full image complicates the detection process but helps achieve a high level of automation.…”

“…There are two ways in which to approach the oil-spill detection problem: studying the characterization of the slick by means of the multi-polarization features of SAR techniques, as occurs in [ 5 , 6 , 7 ], or using the brightness image obtained from the backscatter signal without considering the parameters of the processes of image acquisition and formation, as in [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Our work falls within the second category.…”

“…Our work falls within the second category. Most of the approaches based on image processing techniques focus on segmenting SAR images in order to identify the dark-spot region that represents the oil slick [ 8 , 9 , 10 , 11 , 12 ], on extracting features so as to recognize the oil slick from previously segmented images [ 13 , 14 , 15 ], or on classifying dark spots using machine learning techniques [ 16 ], statistical classifiers [ 17 , 18 , 19 ] or/and artificial neural networks [ 20 , 21 , 22 , 23 ]. State-of-the-art methods sometimes use a combination of several approaches, as in [ 24 ], and on other occasions, the image processing methods are combined with Geographic Information Systems (GIS), as in [ 25 ], to provide both the detection and precise location of marine spills.…”

“…In particular, the proposed method is based on the segmentation technique category, without considering the multi-polarization features of the radar. But unlike the works in [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], the input is a SLAR and not a SAR image. It is very difficult to find works in the state of the art that address oil-spill detection using SLAR images.…”

Mapping Oil Spills from Dual-Polarized SAR Images Using an Artificial Neural Network: Application to Oil Spill in the Kerch Strait in November 2007

“…The most commonly used method for oil spill detection is to divide the image into two parts by applying the threshold value which is determined in a bimodal histogram created by oil-free surface and oil-covered surface. Since it is simple and accurate, many studies have proposed an oil spill detection algorithm based on the simple or adaptive thresholding techniques [ 10 , 11 , 12 , 13 ]. Further improved oil spill detection algorithms, which are based on the intensity difference between the target and background pixels, have been proposed by exploiting (i) the texture information of sea surface cover types [ 14 , 15 , 16 , 17 ]; (ii) the statistical value of oil spills [ 17 , 18 , 19 ] or (iii) the edge detection between oil spill and the oil-free surface [ 20 , 21 , 22 , 23 ].…”