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MAP-based denoising of hyperspectral imagery using 3-D edge-preserving priors
Chen Shaolin; Hu Xiyuan; Peng Silong; Shaolin Chen
2012
Conference Name2012 2nd International Conference on Remote Sensing Environment and Transportation Engineering RSETE 2012
Conference Date2012/6/1-2012/6/3
Conference Place中国NanjingChina
AbstractIn the hyperspectral imaging acquired images are inherently affected by noise whose levels may vary from band to band. It is not a trivial task to remove this kind of noise while preserving the edges and details of hyperspectral images (HSIs). This paper provides a maximum a posterior (MAP)-based denoising approach for HSIs corrupted by band-varying noise. Compared with the classical MAP-based methods for 2-D degraded image restoration the proposed approach uses 3-D edge preserving priors to keep sharp edges while smoothing the 3-D HSIs. In order to adapt to the characteristics of bandvarying noise statistics and high dynamic ranges of HSIs we adaptively estimate the noise variance and scaling parameter of each point. For minimizing the cost function the half-quadratic optimization algorithm is used. Both denoising and classification experimental results confirm the superiority and validity of the proposed method
KeywordHyperspectral Images Denoising Map Edgepreserving Prior
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12877
Collection智能制造技术与系统研究中心_多维数据分析
Corresponding AuthorShaolin Chen
Recommended Citation
GB/T 7714
Chen Shaolin,Hu Xiyuan,Peng Silong,et al. MAP-based denoising of hyperspectral imagery using 3-D edge-preserving priors[C],2012.
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