Multi-objective entropy evolutionary algorithm for marine oil spill detection using cosmo-skymed satellite data

Abstract

Abstract. Oil spill pollution has a substantial role in damaging the marine ecosystem. Oil spill that floats on top of water, as well as decreasing the fauna populations, affects the food chain in the ecosystem. In fact, oil spill is reducing the sunlight penetrates the water, limiting the photosynthesis of marine plants and phytoplankton. Moreover, marine mammals for instance, disclosed to oil spills their insulating capacities are reduced, and so making them more vulnerable to temperature variations and much less buoyant in the seawater. This study has demonstrated a design tool for oil spill detection in SAR satellite data using optimization of Entropy based Multi-Objective Evolutionary Algorithm (E-MMGA) which based on Pareto optimal solutions. The study also shows that optimization entropy based Multi-Objective Evolutionary Algorithm provides an accurate pattern of oil slick in SAR data. This shown by 85 % for oil spill, 10 % look-alike and 5 % for sea roughness using the receiver-operational characteristics (ROC) curve. The E-MMGA also shows excellent performance in SAR data. In conclusion, E-MMGA can be used as optimization for entropy to perform an automatic detection of oil spill in SAR satellite data.

Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

“…The genetic algorithm was able to automatically extract oil spill pixels from the surrounding pixels without using a separate segmentation algorithm, as was done by Skrunes et al (2012). Further, the GA algorithm is able to identify a small oil spill from large ones which has not been shown in previous studies (Fiscella et al 2000, Marghany 2001, 2015Topouzelis et al 2009a, Marghany andHashim 2011). The receiver-operating characteristic (ROC) curve presented in Fig. 12 indicates a significant difference in discrimination power among oil spill, look-alike and sea surface roughness pixels.…”

Case study on wavelet choice based on statistical image quality measures

“…In the Results section, we will consider two cases: w 1 = w 2 = w 3 = w 4 = 1 and w 1 = w 2 = w 3 = 1/3 with w 4 = 1 . Definition 2.3 A video surveillance system will perform optimally if the following goals are obtained with appropriate weight (7):…”

“…Therefore, a multiobjective approach can be used. The popularity of this approach is emphasized in some recent publications, such as in [2][3][4][5][6][7]. Recent studies are from the field of medical imaging, such as image registration [5] or magnetic resonance imaging [6].…”

“…Recent studies are from the field of medical imaging, such as image registration [5] or magnetic resonance imaging [6]. Another trend is its application in oil spill detection [7]. Pareto optimum threshold can also be used for wavelet denoising purposes [8].…”

Introductory Chapter: Automatic Detection of Ice Covers in Airborne Radar Data Using Genetic Algorithm

“…Recent algorithms are exploited in glacier classifications based on the deep learning machine. Particularly, the support vector machine (SVM), a well-liked binary classification deep learning technique, has been focused on the identification of ocean glacier water (hence shortened to ice water) from spaceborne SAR data [1,9,11]. For numerous spaceborne SAR data, pixel-based and region-based glacier categorization methods have been invented [1,4,10,12].…”