Matlab Analyze Spectral Images

% Read hyperspectral data
hcube = imhypercube("scene.hdr");

% Visualize — colorize syntax (NO name-value for basic usage):
falseColor = colorize(hcube);              % false-colored image (default, 3 most informative bands)
rgb = colorize(hcube, Method="rgb");       % true RGB (hypercube ONLY — needs R/G/B wavelengths)
cir = colorize(hcube, Method="cir");       % color infrared (hypercube ONLY)
custom = colorize(hcube, [30 20 10]);      % false color from specific band INDICES (not wavelengths)
imshow(falseColor)

% Radiometric pipeline
hcube = dn2radiance(hcube);
hcube = radiance2Reflectance(hcube);
hcube = fastInScene(hcube);

% Compute NDVI
ndviMap = ndvi(hcube);

% Endmember extraction and unmixing
numEM = countEndmembersHFC(hcube);
endmembers = nfindr(hcube, numEM);
abundance = estimateAbundanceLS(hcube, endmembers, Method="fcls");

% Anomaly detection
rxScore = anomalyRX(hcube);

% Segmentation
L = hyperseganchor(hcube, 8);

% Launch viewer app
hyperspectralViewer(hcube)

% Launch labeler app (Since R2026a)
spectralImageLabeler(hcube)

% HYPERCUBE (supports Method, 2 outputs, ContrastStretching default=false)
falseColor = colorize(hcube)                        % false-colored (default)
rgb = colorize(hcube, Method="rgb")                 % true color RGB
cir = colorize(hcube, Method="cir")                 % color infrared
custom = colorize(hcube, [b1 b2 b3])               % 3 band indices
[img, indices] = colorize(hcube)                    % also get band indices used
img = colorize(hcube, ContrastStretching=true)      % with CLAHE

% MULTICUBE (NO Method param, 1 output, ContrastStretching default=true)
rgb = colorize(mcube)                               % true RGB (auto-selects by wavelength)
custom = colorize(mcube, [b1 b2 b3])               % false color from band indices
img = colorize(mcube, ContrastStretching=false)     % disable contrast stretching

hcube = imhypercube("file.hdr");                    % read ENVI/NITF/TIFF
mcube = immulticube("MTL.txt");                     % read Landsat (pass *_MTL.txt file)
mcube = immulticube("manifest.safe");              % read Sentinel-2 (pass manifest.safe file, NOT .SAFE folder)
mcube = immulticube("AST_L1T.hdf");                % read ASTER HDF
cropped = cropData(hcube, 30:110, 30:110);          % row indices, col indices (NOT [x y w h])
vnir = selectBands(hcube, Wavelength=[400 1000]);   % by wavelength range
subset = selectBands(hcube, BandNumber=[1 10 20]);  % by band index
cleaned = removeBands(hcube, Wavelength=[1350 1450]); % remove wavelength range
data = gather(hcube);                               % get M-by-N-by-C numeric array

% ACCESSING DIMENSIONS — NO NumRows/NumColumns/NumBands properties exist:
height = hcube.Metadata.Height;                     % number of rows (M)
width  = hcube.Metadata.Width;                      % number of columns (N)
bands  = hcube.Metadata.Bands;                      % number of spectral bands (C)
% For multicube: use mcube.BandSize(1,1) for height, mcube.BandSize(1,2) for width

% RESAMPLING vs SELECTING (multicube only):
mcubeUniform = resampleBands(mcube, 30);                       % RESAMPLE all bands TO 30m (keeps ALL bands, changes spatial size)
mcubeUniform = resampleBands(mcube, 30, Method="bilinear");    % interpolation: "nearest"(default),"bilinear","cubic"
mcube30m = selectBands(mcube, "DataResolution", 30);           % KEEP only bands already at 30m (DISCARDS others)
% When asked to "resample" or "match resolution" → use resampleBands
% When asked to "select" or "filter" bands by resolution → use selectBands

% NDVI — use built-in ndvi() function (supports block processing natively)
ndviMap = ndvi(spcube);                             % direct numeric M-by-N output, range [-1,1]
ndviMap = ndvi(spcube, BlockSize=[512 512]);        % with block processing for large data

% Standard indices — use spectralIndices() (returns struct with .IndexName and .IndexImage)
indices = spectralIndices(spcube, "NDVI");          % single index → struct
indices = spectralIndices(spcube, ["NDVI","EVI"]);  % multiple → struct array
indices = spectralIndices(spcube, "all");           % all 16 supported indices
indices = spectralIndices(spcube, "NDVI", BlockSize=[512 512]);  % with block processing
% Supported: "NDVI","EVI","NDBI","NBR","MNDWI","MSI","GVI","OSAVI","SR","CMR","MCARI","MTVI","PRI","NDNI","NDMI","CAI"

% Custom formula — use customSpectralIndex() (3 required args: cube, wavelengths, @func)
indexImage = customSpectralIndex(spcube, [800 670], @(nir,red) (nir-red)./(nir+red));
indexImage = customSpectralIndex(spcube, [800 670 475], @(nir,red,blue) 2.5*(nir-red)./(nir+6*red-7.5*blue+1));
indexImage = customSpectralIndex(spcube, [560 1650], @(g,swir) (g-swir)./(g+swir), BlockSize=[512 512]);
% DO NOT manually extract bands and compute — always use these built-in functions

% Target detection — method is POSITIONAL (3rd arg, not Name=Value)
target = resampleSignature(libData(1), hcube.Wavelength); % resample library spectrum to cube wavelengths
score = detectTarget(hcube, target, "ACE");         % "CEM","ACE","SignedACE","MF","GLRT","AMSD","OSP"
rxScore = anomalyRX(hcube);                         % higher = more anomalous

% Similarity scores — cube first, reference second (lower = better match)
score = sam(hcube, refSpectrum);                    % spectral angle mapper [0, pi]
score = sid(hcube, refSpectrum);                    % spectral information divergence

% spectralMatch — UNUSUAL ARG ORDER: library struct FIRST, then cube/spectrum
% DO NOT pass numeric vectors — first arg MUST be a struct with .Reflectance and .Wavelength fields
libData = readEcostressSig("vegetation.txt");       % returns 1-by-K struct array
score = spectralMatch(libData, hcube);              % returns M-by-N-by-K (one score map per signature)
score = spectralMatch(libData, hcube, Method="sam");% "sam"(default),"sid","sidsam","jmsam","ns3"
score = spectralMatch(libData, reflectance, wavelength); % compare against single spectrum (returns K-vector)
% Lower score = stronger match. Returns NaN when bandwidth overlap < MinBandWidth (default 300nm)
% For direct numeric comparison without library: use sam(hcube, target) or sid(hcube, target)

% Spectral clustering / segmentation (use INSTEAD of kmeans for spectral data)
L = hyperseganchor(hcube, numClusters);             % spectral-aware clustering (1 output)
[L, n] = hyperslic(hcube, K);                       % spectral superpixels (2 outputs)

% Block processing — callback receives OBJECT, must use gather()
result = apply(hcube, @(block) mean(gather(block), 3));