Intelligent hybrid system for dark spot detection using SAR data

• Published in: Expert systems with applications Vol. 81; pp. 384 - 397
• Main Authors: Genovez, Patrícia, Ebecken, Nelson, Freitas, Corina, Bentz, Cristina, Freitas, Ramon
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.09.2017; Elsevier BV
• Subjects: Backscattering; Cluster analysis; Clustering; Computational Intelligence; Decision analysis; Digital Image Processing; False alarms; Feature extraction; Feature selection; Hybrid systems; Image detection; Image segmentation; Intelligent systems; Oil slicks; Oil spills; Oil spills detection; Spots; Synthetic Aperture Radar; Cluster analysis; Feature selection; Synthetic Aperture Radar; Computational Intelligence; Oil spills detection; Digital Image Processing
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.03.037

Synthetic Aperture Radars (SAR) are the main instrument used to support oil detection systems. In the microwave spectrum, oil slicks are identified as dark spots, regions with low backscatter at sea surface. Automatic and semi-automatic systems were developed to minimize processing time, the occurrence of false alarms and the subjectivity of human interpretation. This study presents an intelligent hybrid system, which integrates automatic and semi-automatic procedures to detect dark spots, in six steps: (I) SAR pre-processing; (II) Image segmentation; (III) Feature extraction and selection; (IV) Automatic clustering analysis; (V) Decision rules and, if needed; (VI) Semi-automatic processing. The results proved that the feature selection is essential to improve the detection capability, keeping only five pattern features to automate the clustering procedure. The semi-automatic method gave back more accurate geometries. The automatic approach erred more including regions, increasing the dark spots area, while the semi-automatic method erred more excluding regions. For well-defined and contrasted dark spots, the performance of the automatic and the semi-automatic methods is equivalent. However, the fully automatic method did not provide acceptable geometries in all cases. For these cases, the intelligent hybrid system was validated, integrating the semi-automatic approach, using compact and simple decision rules to request human intervention when needed. This approach allows for the combining of benefits from each approach, ensuring the quality of the classification when fully automatic procedures are not satisfactory.