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spectralIndices

Compute hyperspectral indices

Since R2020b

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Syntax

indices = spectralIndices(hcube)
indices = spectralIndices(hcube,indexNames)
indices = spectralIndices(hcube,'all')
indices = spectralIndices(___,'BlockSize',blocksize)

Description

indices = spectralIndices(hcube) computes the greenness indices: enhanced vegetation index (EVI), modified chlorophyll absorption ratio index (MCARI), and simple ratio (SR) index of a hyperspectral data. The function reads the data cube and the wavelength values stored in the hypercube object hcube to compute the greenness indices.

example

indices = spectralIndices(hcube,indexNames) computes one or more spectral indices specified by indexNames.

example

indices = spectralIndices(hcube,'all') computes all the supported spectral indices.

indices = spectralIndices(___,'BlockSize',blocksize) specifies the block size for block processing of the hyperspectral data cube by using the name-value pair argument 'BlockSize'. You can specify the 'BlockSize' name-value pair argument in addition to the input arguments in the previous syntaxes.

The function divides the input image into distinct blocks, processes each block, and then concatenates the processed output of each block to form the output matrix. Hyperspectral images are multi-dimensional data sets that can be too large to fit in system memory in their entirety. This can cause the system to run out of memory while running the spectralIndices function. If you encounter such an issue, perform block processing by using this syntax.

For example, spectralIndices(hcube,'BlockSize',[50 50]) divides the input image into non-overlapping blocks of size 50-by-50 and then computes the spectral indices for pixels in each block.

Note

To perform block processing by specifying the 'BlockSize' name-value pair argument, you must have MATLAB® R2021a or a later release.

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™ or MATLAB Mobile™ do not support the library.

Examples

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This example uses:

Open Live Script

Read hyperspectral data into the workspace.

hcube = hypercube('indian_pines.dat');

Compute the spectral indices value for each pixel in the data cube. By default, the spectralIndices function returns the simple ratio (SR) index, enhanced vegetation index (EVI), and modified chlorophyll absorption ratio index (MCARI). 

indices = spectralIndices(hcube);

Inspect the index names in the output struct indices. Read the corresponding index images returned at the output.

indices.IndexName
ans = 
"Simple Ratio (SR)"

ans = 
"Enhanced Vegetation Index (EVI)"

ans = 
"Modified Chlorophyll Absorption Ratio Index (MCARI)"

srImg = indices(1).IndexImage;
eviImg = indices(2).IndexImage;
mcariImg = indices(3).IndexImage;

Estimate a contrast-stretched RGB image from the original data cube by using the colorize function. 

rgbImg = colorize(hcube,'Method','RGB','ContrastStretching',true);

Display the original and the computed index images. The SR index value greater than 3 signifies vegetation. The EVI index identifies dense vegetation and the typical EVI index value for healthy vegetation lie between 0.2 and 0.8. MCARI index signifies abundance of chlorophyll in a region. 

fig = figure('Position',[0 0 800 700]);

axes1 = axes('Parent',fig,'Position',[0 0.54 0.42 0.42]);
imagesc(rgbImg,'Parent',axes1);
axis off
title('RGB Image of Data Cube')

axes2 = axes('Parent',fig,'Position',[0.5 0.54 0.45 0.42]);
imagesc(srImg,'Parent',axes2);
axis off
title('SR Image')
colorbar

axes3 = axes('Parent',fig,'Position',[0 0.035 0.45 0.42]);
imagesc(eviImg,'Parent',axes3);
axis off
title('EVI Image')
colorbar

axes4 = axes('Parent',fig,'Position',[0.5 0.035 0.45 0.42]);
imagesc(mcariImg,'Parent',axes4);
axis off
title('MCARI Image')
colorbar

Figure contains 4 axes objects. Hidden axes object 1 with title RGB Image of Data Cube contains an object of type image. Hidden axes object 2 with title SR Image contains an object of type image. Hidden axes object 3 with title EVI Image contains an object of type image. Hidden axes object 4 with title MCARI Image contains an object of type image.

This example uses:

Open Live Script

Read hyperspectral data into the workspace.

hcube = hypercube("jasperRidge2_R198.img");

Compute the MNDWI value for each pixel in the data cube and read the water index image.

indices = spectralIndices(hcube,"MNDWI");
mndwiImg = indices.IndexImage;

Estimate a contrast-stretched RGB image from the original data cube by using the colorize function.

rgbImg = colorize(hcube,Method="RGB",ContrastStretching=true);

Display the original and the MNDWI image. 

fig = figure(Position=[0 0 700 400]);
axes1 = axes(Parent=fig,Position=[0 0.1 0.4 0.8]);
imshow(rgbImg,Parent=axes1)
title("RGB Image of Data Cube")
axes2 = axes(Parent=fig,Position=[0.45 0.15 0.47 0.7]);
imagesc(mndwiImg,Parent=axes2)
colorbar
axis off
title("MNDWI Image")

Figure contains 2 axes objects. Hidden axes object 1 with title RGB Image of Data Cube contains an object of type image. Hidden axes object 2 with title MNDWI Image contains an object of type image.

Water regions typically have MNDWI values greater than 0.09. Perform thresholding of MNDWI image to segment the water regions. Specify the threshold value.

threshold = 0.09;

Create a binary image by applying the threshold. The regions in the binary image with a value of 1 correspond to the vegetation regions in the data cube with MNDWI values greater than the threshold. All other pixels have a value 0.

bw = mndwiImg > threshold;

Overlay the binary image on to the RGB image and display the overlaid image.

overlayImg = imoverlay(rgbImg,bw,[0 0 1]);
figure
imagesc(overlayImg)
axis off
title("Water Region Overlaid on RGB Image")

Figure contains an axes object. The hidden axes object with title Water Region Overlaid on RGB Image contains an object of type image.

Input Arguments

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Input hyperspectral data, specified as a hypercube object. The DataCube property of the hypercube object contains the hyperspectral data cube.

Name of spectral index to compute, specified as a character vector or string scalar. You can also specify the names of multiple spectral indices as a cell array of either character vectors or string scalars. The value of the indexNames must be one of the names listed in this table.

Supported spectral indices
indexNamesDescription
'CAI'Cellulose absorption index (CAI)
'CMR'Clay minerals ratio (CMR)
'EVI'Enhanced vegetation index (EVI)
'GVI'Green vegetation index (GVI)
'MCARI'Modified chlorophyll absorption ratio index (MCARI)
'MTVI'Modified triangular vegetation index (MTVI)
'MNDWI'Modified normalized difference water index (MNDWI)
'MSI'Moisture stress index (MSI)
'NBR'Normalized burn ratio (NBR)
'NDBI'Normalized difference built-up index (NDBI)
'NDMI'Normalized difference mud index (NDMI)
'NDNI' Normalized difference nitrogen index (NDNI)
'NDVI'Normalized difference vegetation index (NDVI)
'OSAVI'Optimized soil adjusted vegetation index (OSAVI)
'PRI'Photochemical reflectance index (PRI)
'SR'Simple ratio (SR)

You can compute the indices CMR, EVI, GVI, MNDWI, NBR, NDBI, NDVI, OSAVI, and SR, which use broadband band definitions, for both narrowband hyperspectral images and broadband multispectral images. You can compute the indices CAI, MCARI, MTVI, MSI, NDMI, NDNI, and PRI, which use narrowband band definitions, for only narrowband hyperspectral images. For more information about these spectral indices, see Spectral Indices.

Example: indexNames = 'PRI'indexNames = "PRI"indexNames = {'NDVI,'OSAVI'}indexNames = {"GVI","NDMI"}

Data Types: char | string

Size of the data blocks, specified as a 2-element vector of positive integers. The elements of the vector correspond to the number of rows and columns in each block, respectively. The size of the data blocks must be less than the size of the input image. Dividing the hyperspectral images into smaller blocks enables you process large data sets without running out of memory.

  • If the blocksize value is too small, the memory usage of the function reduces at the cost of increased execution time.

  • If the blocksize value is large or equal to the input image size, the execution time reduces at the cost of increased memory usage.

Example: 'BlockSize',[20 20] specifies the size of each data block as 20-by-20.

Output Arguments

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Spectral index values of the hyperspectral data, returned as a structure with two fields: IndexName and IndexImage.

FieldsDescription
IndexNameNames of the spectral indices computed for the hyperspectral data, returned as a string.
IndexImageIndex image returned as a matrix. Each pixel value is the spectral index value computed across all the spectral bands. If the size of the hyperspectral data cube specified at the input is M-by-N-by-C, the size of the index image is M-by-N.

The size of the output structure depends on the number of spectral indices computed for the hyperspectral data.

  • If the second input argument indexNames is not specified, the output is a structure array of size 1-by-3. The structure array contains the index images corresponding to EVI, MCARI, and SR index.

  • If the second input argument indexNames is specified and is of length 1-by-k, the output is a structure array of size 1-by-k. You can use dot notation to read the outputs obtained for each spectral index specified at the input.

  • If the second input argument is 'all', the output is a structure array of size 1-by-16. The structure array contains the index images corresponding to all the supported spectral indices.

Data Types: struct

Version History

Introduced in R2020b

See Also

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