Comparative algorithms for automatic detection of oil spill in multisar of RADARSAT-1 SAR and ENVISAT data

Author

Marghany, Maged ; Hashim, Mazlan

Author_Institution

Inst. of Geospatial Sci. & Technol. (INSTeG), Univ. Teknol. Malaysia, Skudai, Malaysia

fYear

2011

fDate

16-18 Nov. 2011

Firstpage

559

Lastpage

562

Abstract

This study presents a comparative algorithms for oil spill automatic detection from different RADARSAT-1 SAR different mode data and ENVISAT ASAR data. Three algorithms are involved: Entropy, Mahalanobis, and Artificial Neural Network (ANN) algorithms. The study shows that ANN provide automatically oil spill detection with error of standard deviation of 0.12 which is lower than Entropy and the Mahalanobis algorithms.

Keywords

entropy; geophysical image processing; marine pollution; neural nets; oil pollution; synthetic aperture radar; ANN; ENVISAT ASAR data; Mahalanobis algorithm; RADARSAT-1 SAR data; artificial neural network algorithm; automatic oil spill detection; entropy; multisar; Artificial neural networks; Classification algorithms; Conferences; Entropy; Sea measurements; Synthetic aperture radar; Training; Entropy; Mahalanobis neural net work (NN); RADARSAT-1 SAR; oil spill;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal and Image Processing Applications (ICSIPA), 2011 IEEE International Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4577-0243-3

Type

conf

DOI

10.1109/ICSIPA.2011.6144136

Filename

6144136

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3221999