Intelligent hybrid system for dark spot detection using SAR data. (15th September 2017)

• Record Type: Journal Article
• Authors: Genovez, Patrícia; Ebecken, Nelson; Freitas, Corina; Bentz, Cristina; Freitas, Ramon
• Is Part Of: Expert systems with applications. Volume 81(2017)
• Page Start: 384
• Page End: 397
• Publication Date: 2017-09-15
• Subjects: Synthetic Aperture Radar -- Digital Image Processing -- Oil spills detection -- Feature selection -- Cluster analysis -- Computational Intelligence; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.03.037
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: 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.