Classification of oil spill in the Krishna-Godavari offshore using ERS-1 SAR images with a fuzzy logic approach

In the present study new features are extracted from the dark spot and fuzzy based techniques were utilized for classifications of the dark spot into oil spill and look alike. Threshold is defined for detection of dark spot, which is obtained from histogram analysis, where the image histograms consist of two peaks. For scenes devoid of two peaks in the histogram, an empirical formula is developed from the scene statistics, which designate the threshold for segmentation of the dark spot. A centerline concept is introduced which bisects the slick along the major axis. This is further used to determine the width, shape and orientation of the suspected slick. Abrupt turn and curving of the slick is also monitored from the centerline. According to the characteristic of each feature, separate membership functions were assigned to obtain its fuzzy set. Few features were identified to have high discriminatory power, whose values were having marked contrast for oil spill and look alike. Five classes were defined: 1) oil spill, 2) tending to oil spill, 3) uncertain, 4) tending to look alike and 5) look alike. From 30 dark spots obtained from six ERS-1 SAR scenes, six were classified as oil spill and four dark spots as look alike. Six dark spots were classified as tending to oil spill and five as tending to look alike, whereas classification of nine dark spots was found to be uncertain.