Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark

Singha, Suman; Vespe, Michele; Trieschmann, Olaf

doi:10.1016/j.marpolbul.2013.05.022

Cited by 49 publications

(43 citation statements)

References 26 publications

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“…The class label of a test sample is predicted by applying the decision criteria from the root to the leaves to determine which leaf it falls in. Because of its capability of easily providing an intelligible model of the data, decision tree is very popular and widely used for classification purpose either directly [3,15] or as the elemental classifier of state-of-the-art ensemble techniques such as bagging, bundling and boosting for achieving better generality performance [8,22,[54][55][56]. Here, we use the DT supported by the classification and regression tree (CART) algorithm [53].…”

Section: Decision Treementioning

confidence: 99%

“…Based on the SAR systems, many commercial or governmental agencies have been building SAR oil-spill detection service, such as the multi-mission maritime monitoring services of Kongsberg Satellite Services (KSAT), Airbus defense and space's oil spill detection service, CleanSeaNet [3] and Integrated Satellite Tracking of Pollution (ISTOP). To be more operational, automatic oil spill classification system with real-time, fully operational and wider water coverage capability is needed [1], as Solberg et al state [4] "The currently manual services is just a first step toward a fully operational system covering wider waters".…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data

Cao

Clausi

2017

Remote Sensing

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This paper intends to find a more cost-effective way for training oil spill classification systems by introducing active learning (AL) and exploring its potential, so that satisfying classifiers could be learned with reduced number of labeled samples. The dataset used has 143 oil spills and 124 look-alikes from 198 RADARSAT images covering the east and west coasts of Canada from 2004 to 2013. Six uncertainty-based active sample selecting (ACS) methods are designed to choose the most informative samples. A method for reducing information redundancy amongst the selected samples and a method with varying sample preference are considered. Four classifiers (k-nearest neighbor (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and decision tree (DT)) are coupled with ACS methods to explore the interaction and possible preference between classifiers and ACS methods. Three kinds of measures are adopted to highlight different aspect of classification performance of these AL-boosted classifiers. Overall, AL proves its strong potential with 4% to 78% reduction on training samples in different settings. The SVM classifier shows to be the best one for using in the AL frame, with perfect performance evolving curves in different kinds of measures. The exploration and exploitation criterion can further improve the performance of the AL-boosted SVM classifier but not of the other classifiers.

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Section: Decision Treementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data

Cao

Clausi

2017

Remote Sensing

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“…Singha et al [19] presented a method with which to classify oil spills and look-alikes. It was tested using images from two satellites, ENVISAT (35 images) and RADARSAT-2 (83 images), captured between 2009 and 2012 in CleanSeaNet.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images

Nieto-Hidalgo

Gallego

Gil

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Abstract-This paper presents a system for the detection of ships and oil spills using side-looking airborne radar (SLAR) images. The proposed method employs a two-stage architecture composed of three pairs of convolutional neural networks (CNNs). Each pair of networks is trained to recognize a single class (ship, oil spill, and coast) by following two steps: a first network performs a coarse detection, and then, a second specialized CNN obtains the precise localization of the pixels belonging to each class. After classification, a postprocessing stage is performed by applying a morphological opening filter in order to eliminate small look-alikes, and removing those oil spills and ships that are surrounded by a minimum amount of coast. Data augmentation is performed to increase the number of samples, owing to the difficulty involved in obtaining a sufficient number of correctly labeled SLAR images. The proposed method is evaluated and compared to a single multiclass CNN architecture and to previous state-of-the-art methods using accuracy, precision, recall, F-measure, and intersection over union. The results show that the proposed method is efficient and competitive, and outperforms the approaches previously used for this task.

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“…For example, the first crucial step for mesoscale oceanographic phenomena detection and classification (e.g. oil spills, eddies, currents, or fronts) is the identification of dark objects in SAR images [4,9,10]. This step is extremely difficult in wide swath images due to backscatter decrease from near to far range.…”

Section: Introductionmentioning

confidence: 99%

Incidence angle normalization of Wide Swath SAR data for oceanographic applications

2016

Self Cite

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A backscattering trend in the range direction of the signal received by Synthetic Aperture Radar (SAR) in Wide Swath (WS) mode results in a progressive reduction of brightness over images from near to far range, which affects the detection and classification of sea surface features on wide swath SAR images. The aim of the present paper is to investigate methods for limiting the issue of Normalized Radar Cross-Section (NRCS or σ 0 ) variation due to the incidence angle. Two sensor independent functions are investigated: a theoretical backscattering shape function derived from a minimum wind speed and an empirical range fit of NRCS against incidence angle θ. The former method exploits only the modeled NRCS values while the latter only the image content. The results were compared with the squared cosine correction, the most widely applied method for normalization, using six newly developed comparison factors. The results showed that the cosine squared normalization has the lowest efficiency while the proposed methods have similar behaviors and comparable results. Nevertheless, after the log-transformation and summation of the comparison factors, it was clearly shown that theoretical normalization performance is superior to the empirical one since it has the highest accuracy and requires less computational time.

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Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark

Cited by 49 publications

References 26 publications

Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data

Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data

Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images

Incidence angle normalization of Wide Swath SAR data for oceanographic applications

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