System for eliminating or reducing exemplar effects in multispectral or hyperspectral sensors

A sensor system comprising:

• an exemplar set, said exemplar set comprising exemplar set members;
• a means for generating a data set, said data set comprising data set members wherein;
• said means for generating compares said data set members with respective ones of said exemplar set members effective to characterize said data set members as similar or dissimilar to said exemplar set members in accordance with a SERENE technique, and
• a means for including said data set members characterized as dissimilar in said exemplar set;
• a means for generating the endmember vectors of said exemplar set;
• a means for demixing said data set.

An optical sensor system comprising;

• a means for collecting spectral data,
• an adaptive learning module, said adaptive learning module computing endmember and source spectra by performing Gram-Schmidt/Salient analysis of said spectral data, said analysis defining a reduced dimensional spanning space by:
• a)designating the two vectors contained in said data farthest apart
• b)generating an orthogonal basis set in the plane defined by said orthogonal basis set;
• c)selecting the salient relating to said plane defined by said orthogonal basis set;
• d)generating a new orthogonal basis set based on the salient selected in step (c) said new orthogonal basis set defining a multi-dimensional volume,
• wherein the distance between said salient selected in step (c) and said multi-dimensional volume defines an error;
• continue steps (c)-(d) until said error falls within an acceptable range;
• said adaptive learning module further performing minimization of a convex manifold within said multi-dimensional volume subject to the condition that all vector information from said spectral data is contained within a minimized hyper triangle, wherein said endmembers are defined by the vertices of said minimized hyper triangle;
• said adaptive learning module computing filter vectors in accordance with the equation:
• Fi•Ei=δij
• subject to the condition that δij=1 if i=j and δij=0 otherwise, where Fi=said filter vector and Ei=said endmember,
• a demixer module, said demixer module comprising a means to compute an endmember coefficient in accordance with the equation,
• Fj·dk=ΣcjkFj·Ej+Fj·Nk≈cjk
• where Fj=said filter vector, dk=said data set, cjk=said endmember coefficient, Nk=noise and Ej=said endmember;
• a means to further process and display the spectral characteristics of said data set in terms of said endmembers and endmember coefficients said processing in accordance with the equation
• dk=ΣcjkEj+Nk
• where dk=said data set, cjk=said endmember coefficient and is ≧0, Nk=noise and Ej=said endmember.

An optical sensor system comprising;

• a means for collecting optical spectral data,
• a means for converting said optical spectral data into a minimized set of data vectors comprising:
• a prescreener module, said prescreener module computing a survivor set of data vectors S by screening data in accordance with the equation
• (di•Sj>1−e)
• where di represents a new data vector and Sj represents a data vector from a survivor set;
• an adaptive learning module,
• said adaptive learning module computing endmembers and source spectra by performing Gram-Schmidt/Salient analysis of said survivor set, said analysis defining a reduced dimensional spanning space by:
• a) designating the two survivor set vectors contained in said survivor set farthest apart;
• b) generating an orthogonal basis set in the plane defined by said two survivor set vectors;
• c) selecting the salient relating to said plane defined by said orthogonal basis set;
• d) generating a new orthogonal basis set based on the salient selected in step (c), said new orthogonal basis set defining a multi-dimensional volume;
• wherein the distance between said salient selected in step (c) and said multi-dimensional volume defines an error;
• continue steps (c)-(d) until said error falls within an acceptable range,
• said adaptive learning module further performing minimization of a convex manifold within said multi-dimensional volume subject to the condition that all vector information from said survivor set is contained within a minimized hyper triangle, wherein said endmembers are defined by the vertices of said minimized hyper triangle,
• said adaptive learning module computing said filter vectors in accordance with the equation
• Fi•Ei=δij
• subject to the condition that δij=1 if i=j and δij=0 otherwise, where Fi=said filter vector and E=said endmember;
• a demixer module;
• said demixer module comprising a means to compute an endmember coefficient in accordance with the equation
• Fj·dk=ΣcjkFj·Ej+Fj·Nk≈cjk
• where Fj=said filter vector, dk=said data set, cjk=said endmember coefficient, Nk=noise and Ej=said endmember;
• a means to further process and display the spectral characteristics of said data set in terms of said endmembers and endmember coefficients said processing in accordance with the equation
• dk=ΣcjkEj+Nk
• where dk=said data set, cjk=said endmember coefficient and is ≧0, Nk=noise and Ej=said endmember.

A method for processing hyperspectral data comprising:

• generating a plurality of data vectors,
• producing endmember vectors responsive to said data vectors,
• producing endmember vectors by employing a SERENE technique;
• wherein said producing means reduces the spanning space of said data vectors by application of a Graham-Schmidt/Salient analysis.

A method for processing hyperspectral data comprising:

• generating a survivor set, said survivor set comprising survivor set members;
• generating a data set, said data set comprising data set members;
• comparing said data set members with respective ones of said survivor set members;
• characterizing said data set members as similar or dissimilar to said survivor set members in accordance with the SERENE technique, and
• including said data set members characterized as dissimilar in said survivor set.