countEndmembersHFC

R2026b

Find number of endmembers

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    Syntax

    numEndmembers = countEndmembersHFC(inputData)
    numEndmembers = countEndmembersHFC(inputData,Name=Value)

    Description

    Add-On Required: This feature requires the Hyperspectral Imaging Library for Image Processing Toolbox add-on.

    numEndmembers = countEndmembersHFC(inputData) finds the number of endmembers present in a hyperspectral data by using the noise-whitened Harsanyi–Farrand–Chang (NWHFC) method. For more information about endmembers, see Spectral Unmixing.

    example

    numEndmembers = countEndmembersHFC(inputData,Name=Value) specifies additional options using one or more name-value arguments. For example, NoiseWhiten=false does not perform noise-whitening of the data before extracting the endmembers.

    Note

    The Hyperspectral Imaging Library for Image Processing Toolbox™ requires desktop MATLAB®, as MATLAB Online™ and MATLAB Mobile™ do not support the library.

    example

    Examples

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    This example uses:

    Open Live Script

    Read hyperspectral data into the workspace.

    hcube = imhypercube("jasperRidge2_R198.hdr");

    Find the number of endmembers in the hyperspectral data by using the NWHFC method.

    numEndmembers = countEndmembersHFC(hcube);

    Estimate the endmember spectra using the N-FINDR method. 

    endmembers = nfindr(hcube,numEndmembers);

    Plot the endmember spectra. 

    figure
plot(endmembers)
title("Number of Endmembers: "+num2str(numEndmembers))
xlabel("Band Number")
ylabel("Data Values")

    Figure contains an axes object. The axes object with title Number of Endmembers: 13, xlabel Band Number, ylabel Data Values contains 13 objects of type line.

    This example uses:

    Open Live Script

    Read hyperspectral data into the workspace.

    hcube = imhypercube("jasperRidge2_R198.hdr");

    Find the number of endmembers in the hyperspectral data by using the HFC method. To use the HFC method, set the 'NoiseWhiten' parameter value to false.

    numEndmembers = countEndmembersHFC(hcube,NoiseWhiten=false);

    Estimate the endmember spectra using the N-FINDR method. 

    endmembers = nfindr(hcube,numEndmembers);

    Plot the endmember spectra.

    figure
plot(endmembers)
title("Number of Endmembers: "+num2str(numEndmembers))
xlabel("Band Number")
ylabel("Data Values")

    Figure contains an axes object. The axes object with title Number of Endmembers: 15, xlabel Band Number, ylabel Data Values contains 15 objects of type line.

    Input Arguments

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    Input hyperspectral data, specified as a 3-D numeric array that represent the hyperspectral data cube of size M-by-N-by-C or hypercube object. The hyperspectral data cube must be real and non-sparse.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Name-Value Arguments

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    Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

    Example: countEndmembersHFC(inputData,NoiseWhiten=false) sets NoiseWhiten to false and skips the noise-whitening step and runs the original HFC method.

    Probability of false alarm, specified as a positive scalar in the range (0, 1].

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Perform noise-whitening, specified as a numeric or logical 1 (true) or 0 (false).

    • true or 1 — Perform noise-whitening of input data before computing the number of endmembers. This approach is the NWHFC method.

    • false or 0 — Do not perform noise-whitening of input data before computing the number of endmembers. This approach is the Harsanyi–Farrand–Chang (HFC) method.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

    Output Arguments

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    Number of endmembers in the hyperspectral data, returned as a positive numeric scalar.

    Data Types: double

    References

    [1] Chang, C.-I., and Q. Du. “Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 42, no. 3 (March 2004): 608–19. https://doi.org/10.1109/TGRS.2003.819189.

    Version History

    Introduced in R2020a

    See Also

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