A Gaussian Process Regression Approach for Fusion of Remote Sensing Images for Oil Spill Segmentation

被引:0
|
作者
Longman, Fodio S. [1 ]
Mihaylova, Lyudmila [1 ]
Yang, Le [2 ]
机构
[1] Univ Sheffield, Dept Automat Control & Syst Engn, Mappin St, Sheffield S1 3JD, S Yorkshire, England
[2] Univ Canterbury, Dept Elect & Comp Engn, Chrischurch, New Zealand
关键词
Oil Spill; Synthetic Aperture Radar (SAR); Registration; Image Fusion; Segmentation; Gaussian Processes; SATELLITE; CLASSIFICATION;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Synthetic Aperture Radar (SAR) satellite systems are very efficient in oil spill monitoring due to their capability to operate under all weather conditions. This paper presents a framework using Gaussian process (GP) to fuse SAR images of different modalities and to segment dark areas (assumed oil spill) for oil spill detection. A new covariance function; a product of an intrinsically sparse kernel and a Rational Quadratic Kernel (RQK) is used to model the prior of the estimated image allowing information to be transferred. The accuracy performance evaluation demonstrates that the proposed framework has 37% less RMSE per pixel and a compelling enhancement visually when compared with existing methods.
引用
收藏
页码:62 / 69
页数:8
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