Assessment and Enhancement of {SAR} non-coherent Change Detection Techniques Following Oil Spills

TL;DR

This paper evaluates SAR-based change detection algorithms for oil spill identification, introducing a dual-map method to improve accuracy and reduce false alarms, with findings favoring the intensity ratio change statistic.

Contribution

It presents a novel double final change map method that enhances oil spill detection accuracy and reduces false alarms in SAR imagery analysis.

Findings

Intensity ratio change statistic outperforms correlation coefficient change statistic.

The double change map method reduces false alarms.

The approach is validated on 2010 Gulf of Mexico oil spill data.

Abstract

In this study the detection of the oil spill using synthetic aperture radar (SAR) imagery is considered. Detection of the oil spill is performed using change detection algorithms between imagery acquired at different times. The specific algorithms used are the correlation coefficient change statistic and the intensity ratio change statistic algorithms. Therefore these algorithms and the probabilistic selection of the threshold criteria is reviewed and discussed. A recently offered change detection method which depends on the idea of generating two different final change maps of two images in a sequence, is used. First final change map is obtained by cumulatively adding the sequences of change maps in such a manner that common change areas are excluded and uncommon change areas are included. The second final change map is obtained by comparing the first and the last images in the temporal sequence. This method requires at least three images to be employed and can be generalized to longer temporal image sequences. The purpose of this approach is to provide a double check mechanism to the conventional approach and thus to reduce the probability of false alarm and enhance the change detection. The algorithms mentioned are applied to a 2010 Gulf of Mexico oil spill imagery together with the method described. It is shown that intensity ratio change statistic is a better tool for identification of the changes due to the oil spill compared to the correlation coefficient change statistic. It is also shown that two final change map method can reduce the probability of false alarm. The data used in this study is acquired by the Japanese Aerospace Agency's Advanced Land Observing Satellite (ALOS) through Alaska SAR Facility (ASF) at the University of Alaska, Fairbanks, AK.