Proposal for preservation criteria to rare event. Application on multispectral/hyperspectral images

Rare events can potentially occur in many applications, particularly in hyper spectral image analysis. When manifested as opportunities to be exploited, such events become of paramount significance. Due to their sporadic nature, the information-bearing spectral vectors associated with rare events often lie in a large set of irrelevant signals and are not easily accessible This paper provides a statistical frame of a local criterion to measure the rate of the conservation of these rare events during of the dimension reduction. The core component of this framework is a sampling procedure that adaptively and quickly focusses the information-gathering resources on the segments of the data set that bear the information pertinent to the rare events. A particular accent is put on the local vectors in every spectral band with the aim of measuring the rate of the conservation of the rare vectors.

[1]  H. Vincent Poor,et al.  Quickest Search Over Multiple Sequences , 2011, IEEE Transactions on Information Theory.

[2]  Mats Viberg,et al.  Subspace-based methods for the identification of linear time-invariant systems , 1995, Autom..

[3]  Peter Willett,et al.  Some methods to evaluate the performance of Page's test as used to detect transient signals , 1999, IEEE Trans. Signal Process..

[4]  Marina Thottan,et al.  Anomaly detection in IP networks , 2003, IEEE Trans. Signal Process..

[5]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[6]  Peter Swerling,et al.  Sequential detection in radar with multiple resolution elements , 1962, IRE Trans. Inf. Theory.

[7]  R. Younes,et al.  Dimensionality reduction on hyperspectral images: A comparative review based on artificial datas , 2011, 2011 4th International Congress on Image and Signal Processing.

[8]  José M. Bioucas-Dias,et al.  Signal Subspace Identification in Hyperspectral Linear Mixtures , 2005, IbPRIA.

[9]  V. Pisarenko,et al.  Statistical adaptive algorithms for estimation of onset moments of seismic phases , 1987 .

[10]  Bart De Moor,et al.  Subspace algorithms for the stochastic identification problem, , 1993, Autom..

[11]  Fethi Ben Ouezdou,et al.  Stability of Dimensionality Reduction Methods Applied on Artificial Hyperspectral Images , 2012, ICCVG.

[12]  Fethi Ben Ouezdou,et al.  Potential of hybridization methods to reducing the dimensionality for multispectral biological images , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[13]  J. Wolfowitz,et al.  Optimum Character of the Sequential Probability Ratio Test , 1948 .

[14]  David Malah,et al.  Anomaly Preserving $\ell _{\scriptscriptstyle 2,\infty }$-Optimal Dimensionality Reduction Over a Grassmann Manifold , 2010, IEEE Transactions on Signal Processing.

[15]  Enrico Magli,et al.  Hyperspectral Image Compression Employing a Model of Anomalous Pixels , 2007, IEEE Geoscience and Remote Sensing Letters.

[16]  David Malah,et al.  Global unsupervised Anomaly Extraction and Discrimination in hyperspectral images via Maximum Orthogonal-Complement analysis , 2008, 2008 16th European Signal Processing Conference.