###### Abstract  
This work presents a method for oil-spill detection on Spanish coasts using aerial Side-Looking Airborne Radar (SLAR) images, which are captured using a Tema sensor. The proposed method uses grayscale image processing techniques to identify the dark spots that represent oil slicks on the sea. The approach is based on two steps. First, the noise regions caused by aircraft movements are detected and labeled in order to avoid the detection of false-positives. Second, a segmentation process guided by a map saliency technique is used to detect image regions that represent oil slicks. The results show that the proposed method is an improvement on the previous approaches for this task when employing SLAR images. m aritime surveillance; oil spill detection; Side-Looking Airborne Radar; radar detection 2018 18 151 10.3390/s18010151 10. 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. Some disadvantages of the methods presented in the state of the art are related to the difficulty involved in automating the oil slick detection task, as mentioned in [26], in which a hybrid system that integrates several automatic and semi-automatic procedures (with human-supervision) is presented. Moreover, the methods are usually tested in an offline process and this may make the performance of the detection task in an onboard detection system difficult, because they are not always designed for input images with sufficient variability (i.e., the acquisition conditions are always chosen in the same way, signifying that the background produced is usually homogeneous or has a specific texture pattern). Another drawback of some of the state-of-the-art methods is the use of cropped images as inputs rather than using images representing a whole mission or full scan (i.e., an image represents a close-up of the oil spill with some background) [9; 12; 27; 28; 29]. The input images also frequently show only two types of targets (i.e., the sea and oil slick zones) [30] and when the image shows others, such as land, then pre-processing filters are manually configured to hide or eliminate the pixels in those zones [10; 13]. All of these problems still require a solution. Moreover, detection from SLAR images requires finding a solution to these problems, and particularly the last two obstacles, such as working with a full image and using images with other false targets, such as artifacts and noise caused by the flight of the aircraft. Some authors have previously presented a segmentation method based on a boundary [31] or the Markov Random Field (MRF) energy function [32] to reduce or suppress the effect of random noise, low contrast and dayglow contamination. 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. A recent example in this respect is [22], in which a solution based on a Recurrent Neural Network (RNN) is proposed. The method detects the oil spill using the row of SLAR image as inputs, in which each row represents a scanning of the sea surface in a time t. Other recent works were presented in [23; 33].