Oil Spill Detection by CP SAR Based on the Power Entropy Decomposition

Oil spill detection by satellite SAR

Oil spill detection from SAR image using SVM based classification

In this paper, the potential of fully polarimetric L-band SAR data for detecting sea oil spills is investigated using polarimetric decompositions and texture analysis based on SVM classifier. First, power and magnitude measurements of HH and VV polarization modes and, Pauli, Freeman and Krogager decompositions are computed and applied in SVM classifier. Texture analysis is used for identificati...

Texture entropy algorithm for automatic detection of oil spill from RADARSAT-1 SAR data

This work presents a method based on the utilisation of texture algorithms for the discrimination of oil spill areas from the surrounding features, e.g. sea surface and look-alikes, using RADARSAT-1 SAR Wide beam mode (W1), Standard beam mode (S2) and Standard beam mode (S1) data acquisition under different wind speeds. The results show that entropy texture algorithm is able to discriminate bet...

Neural networks for oil spill detection using ERS-SAR data

A neural network approach for semi-automatic detection of oil spills in European remote sensing satellite-synthetic aperture radar (ERS-SAR) imagery is presented. The network input is a vector containing the values of a set of features characterizing an oil spill candidate. The classification performance of the algorithm has been evaluated on a data set containing verified examples of oil spill...

Dual-polarized Sar Data for Oil Spill Detection

In this study the capability of the co-polarized phase difference (CPD) for oil spill observation has been investigated. A simple and effective filtering technique, based on the standard deviation (σ) of the CPD Synthetic Aperture Radar (SAR) image, has been implemented. First experiment, accomplished over SIR-C/X-SAR C-Band data, have shown different sensitivity of the filtering technique with...