COMPARATIVE ALGORITHMS FOR OIL SPILL AUTOMATIC DETECTION USING MULTIMODE RADARSAT-1 SAR DATA

被引：0

作者：

Marghany, Maged ^{[1
]}

Hashim, Mazlan ^{[1
]}

机构：

[1] Univ Teknol Malaysia, Inst Geospatial Sci & Technol INSTeG, Skudai 81310, Johor Bahru, Malaysia

来源：

2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2011年

关键词：

RADARSAT-1; SAR; oil spill; Entropy; Mahalanobis neural net work (NN); IMAGES;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This study is utilized comparative algorithms for automatic detection of oil spill from different RADARSAT-1 SAR mode data (Standard beam S2, Wide beam W1 and fine beam F1). In doing so, three algorithms are implemented: Co-occurrence textures; post supervised classification, and neural net work (NN). The study shows that the standard deviation of the estimated error for neural net work of value 0.12 is lower than Entropy and the Mahalanobis algorithms. In conclusion, ANN performed accurately as automatic detection tool for oil spill in RADARSAT data.

引用

页码：2173 / 2176

页数：4

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