文献类型： 会议论文

第一作者： Saima Ben Hadj

作者： Saima Ben Hadj ¹ ; Jerome Bobin ¹ ; Arnaud Woiselle ² ;

作者机构： 1.CEA Saclay, CosmoStat lab

2.Sagem (Safran), Arcs de Seine

关键词： Anomaly detection;BSS;GMCA;PCA;Multi-spectral images

会议名称： IEEE International Conference on Image Processing

主办单位：

页码： 5147-5151

摘要： Anomaly detection from multi-spectral images is a standard image processing problem in civilian and military applications. The major difficulty of this problem lies in the complex background modeling: not only the background is usually heterogeneous (contains multiple sources, e.g. vegetation, soil, etc) but also spectrally varying even for an homogeneous background. Unlike most existing methods that rely on a Gaussian background model, we do not consider any parametric statistical model for the background; the latter is modeled by a linear mixture of multiple sources estimated using recent sparse blind source separation methods. The detection procedure is then based on contrast measures derived from the estimated mixture parameters. We numerically show that our method is more relevant than two other methods of the state of the art.

分类号： TP391.41-53