countEndmembersHFC

Find number of endmembers

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    Syntax

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

    Description

    numEndmembers = countEndmembersHFC(inputData) finds the number of endmembers present in a hyperspectral data by using the noise-whitened Harsanyi–Farrand–Chang (NWHFC) method.

    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

    This function requires the Hyperspectral Imaging Library for Image Processing Toolbox™. You can install the Hyperspectral Imaging Library for Image Processing Toolbox from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.

    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.

    Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

    Example: countEndmembersHFC(inputData,NoiseWhiten=false)

    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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