countEndmembersHFC

Find number of endmembers

Syntax

numEndmembers = countEndmembersHFC(inputData)

numEndmembers = countEndmembersHFC(inputData,Name,Value)

Description

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

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

Note

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

Examples

Find Number of Endmembers Using NWHFC Method

Read hyperspectral data into the workspace.

hcube = hypercube('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')   

Find Number of Endmembers Using HFC Method

Read hyperspectral data into the workspace.

hcube = hypercube('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')   

Input Arguments

inputData — Input hyperspectral data

3-D numeric array | hypercube object

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. If the input is a hypercube object, the function reads the data cube stored in the DataCube property of the 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

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: countEndmembersHFC(inputData,'NoiseWhiten',false)

PFA — Probability of false alarm

10-3 (default) | positive scalar

Probability of false alarm, specified as the comma-separated pair consisting of 'PFA' and a positive scalar in the range (0, 1].

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

NoiseWhiten — Perform noise whitening

true or 1 (default) | false or 0

Perform noise-whitening, specified as the comma-separated pair consisting of 'NoiseWhiten' and 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

numEndmembers — Number of endmembers in hyperspectral data

positive numeric scalar

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.