Improved Oil Slick Detection and Classification with Polarimetric Sar
نویسنده
چکیده
A study on the potential of space-borne polarimetric synthetic aperture radar (SAR) imagery for an improved detection and classification of oil spills is presented. An image data set consisting of five SIR-C/X-SAR acquisitions over the North Sea, English Channel, and Southern Italy is used. Results show that two land surface roughness indicators (i.e., the circular polarization coherence (CPC) and the polarimetric anisotropy) can also be used to identify oil spills both in L and C-Band imagery. The polarimetric anisotropy is shown to be a quite robust indicator of oil slicks on the sea surface.
منابع مشابه
Sea Oil Slick Observation by Means of Fully-polarimetric Alos Palsar Data
A study on sea oil slick observation by means of Lband polarimetric synthetic aperture radar (SAR) data is accomplished. It is based on the different sea surface scattering mechanism expected with and without surface slicks. Polarimetric measurements are processed by means of a simple and very effective filtering technique which is electromagnetically based on the Mueller scattering matrix. Mor...
متن کاملFully Polarimetric Sar Data for Oil Slick Observation
In this study a fully polarimetric electromagnetic model for sea surface Mueller matrix is exploited, under low to moderate wind conditions, to design and experiment a Synthetic Aperture Radar (SAR) oil slick filter. The latter is simple and computer time effective. Experiments, accomplished over C-band Multi Look Complex (MLC) SIR-C/X-SAR data and L-band Single Look Complex (SLC) ALOS PalSAR d...
متن کاملSynthetic Aperture Radar for oil spill monitoring: a brief review
In this paper, a review of Synthetic Aperture Radar (SAR)-based techniques, for oil slick at sea observation is proposed, focusing in particular on polarimetric approaches. In fact, marine oil pollution monitoring is a topic of great applicative and scientific relevance and in such a context, among all remote sensing sensors, SAR represents a fundamental tool due to its almost all-weather and a...
متن کاملClassification of polarimetric radar images based on SVM and BGSA
Classification of land cover is one of the most important applications of radar polarimetry images. The purpose of image classification is to classify image pixels into different classes based on vector properties of the extractor. Radar imaging systems provide useful information about ground cover by using a wide range of electromagnetic waves to image the Earthchr('39')s surface. The purpose ...
متن کاملComparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
In this paper, we present a comparison between several algorithms for oil spill classifications using fully and compact polarimetric SAR images. Oil spill is considered as one of the most significant sources of marine pollution. As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and compa...
متن کامل