Extracting Oil Slick Features From VIIRS Nighttime Imagery Using a Gaussian Filter and Morphological Constraints

• Published in: IEEE geoscience and remote sensing letters Vol. 12; no. 10; pp. 2051 - 2055
• Main Authors: Wang, Mengqiu, Hu, Chuanmin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clouds; Day-night band (DNB); Feature extraction; Image segmentation; Meteorological satellites; moderate resolution imaging spectroradiometer (MODIS); MODIS; moon glint; morphological operator; Night; Noise; noise reduction; Oceans; Oil slicks; Oil spills; Remote sensing; Satellite imagery; Synthetic aperture radar; visible infrared imager radiometer suite (VIIRS); moderate resolution imaging spectroradiometer (MODIS); moon glint; Day–night band (DNB); feature extraction; visible infrared imager radiometer suite (VIIRS); noise reduction; oil slicks; morphological operator
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2015.2444871

Satellite images of reflected sunlight have been used to detect and monitor oil spills in oceans. However, such a capacity is often hindered by the image noise due to either a low signal-to-noise ratio or other image features such as clouds or cloud shadows. The problem is particularly severe for nighttime images captured by the Visible Infrared Imager Radiometer Suite (VIIRS). This letter proposes a practical method to extract oil slick features in a semiautomatic fashion from VIIRS nighttime images and other noisy optical remote sensing images. The method is based on statistical information and morphological operators, and it is demonstrated to be able to effectively remove the noise and identify line features with the appropriate selection of threshold values. Testing this method over VIIRS nighttime images shows the preliminary success of oil slick feature extraction. Experiments on daytime data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) also suggest the applicability of this method to other optical remote sensing images. However, the requirement of human intervention to determine optimal parameters points to the need for improved automation in future works.