Matlab Analyze Spectral Images

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From the author

Work with hyperspectral and multispectral images in MATLAB. Covers reading/writing (ENVI, NITF, TIFF, Sentinel-2, Landsat, ASTER), ECOSTRESS spectral libraries, processing (calibration, atmospheric correction, denoising, sharpening, dimensionality reduction, endmember extraction, unmixing, target/anomaly detection, spectral indices, segmentation), labeling (Spectral Image Labeler app, ground truth objects, automation algorithms), and deep learning (pixel classification CNNs, unmixing autoencoders, transfer learning). Use when reading, writing, processing, analyzing, classifying, labeling, or applying deep learning to hyperspectral or multispectral images.

— the description by MathWorks

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive interface for working with hyperspectral and multispectral data within the MATLAB environment. It covers the entire lifecycle of spectral imaging, including data ingestion from various formats (ENVI, NITF, TIFF, Sentinel-2, Landsat, ASTER), radiometric calibration, atmospheric correction, and advanced analytical techniques such as spectral unmixing, anomaly detection, and spectral index calculation. The skill also supports modern deep learning workflows, including pixel classification with 3D CNNs and semantic segmentation, as well as interactive labeling via the Spectral Image Labeler app.

What it does

Reads and writes industry-standard spectral image formats like ENVI, NITF, and GeoTIFF.
Performs radiometric calibration and atmospheric correction on raw spectral data.
Executes advanced spectral analysis including endmember extraction, unmixing, and target detection.
Computes standard and custom spectral indices like NDVI and EVI using optimized built-in functions.
Facilitates spectral-aware image segmentation and clustering.
Supports deep learning pipelines for pixel classification and semantic segmentation of spectral cubes.

When to use it

When processing hyperspectral or multispectral satellite or airborne imagery.
When performing quantitative analysis on spectral signatures using ECOSTRESS libraries.
When building deep learning models for spectral image classification or unmixing.
When needing to compute spectral indices or perform anomaly detection on hypercubes.
When managing large-scale spectral data with block processing requirements.

Advantages

Optimized built-in functions for spectral indices and processing, avoiding manual computation errors.
Seamless integration with MATLAB's deep learning and image processing toolboxes.
Native support for complex spectral data structures and metadata management.
Includes specialized apps for interactive labeling and visualization of high-dimensional data.

Usage examples

“Read a hyperspectral image from an ENVI file and calculate the NDVI.”: The agent provides code to load the file using imhypercube and compute the index using the built-in ndvi function.
“How do I perform anomaly detection on my hyperspectral cube?”: The agent provides the syntax for anomalyRX to identify anomalous pixels within the spectral data.
“Extract endmembers from my hyperspectral data.”: The agent provides a workflow using countEndmembersHFC to determine the number of endmembers followed by nfindr for extraction.
“How can I visualize my multispectral cube as a true-color image?”: The agent provides the colorize function syntax with the appropriate method parameter for multicube visualization.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/matlab/matlab-analyze-spectral-images/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip matlab-analyze-spectral-images.zip -d .agents/skills

Documentation by MathWorks

Spectral Imaging Skill

Generate MATLAB code for working with hyperspectral and multispectral data using the Hyperspectral Imaging Library for Image Processing Toolbox. Requires desktop MATLAB (not MATLAB Online or MATLAB Mobile) and the Hyperspectral Imaging Library add-on.

When to Use

Reading/writing hyperspectral or multispectral images (ENVI, NITF, GeoTIFF, TIFF, ASTER, Sentinel-2, Landsat)
Reading ECOSTRESS spectral library signatures (readEcostressSig)
Creating hypercube/multicube objects, band selection, cropping, block processing
Radiometric calibration, atmospheric correction, spectral correction
Denoising, sharpening, dimensionality reduction (PCA, MNF)
Endmember extraction, spectral unmixing, abundance estimation
Spectral matching, target detection, anomaly detection
Computing spectral indices (NDVI, EVI, custom)
Segmentation (spectral clustering, superpixels, ISODATA)
Labeling with the Spectral Image Labeler app
Creating ground truth objects for spectral labeling
Deep learning classification or unmixing of spectral images
Semantic segmentation of multispectral satellite imagery (DeepLabV3+ with multi-channel input)

When NOT to Use

General image reading/writing (use imread/imwrite)
Non-spectral 3-D volume data (use Medical Imaging or Image Processing Toolbox)
General image classification not involving spectral cubes (use Deep Learning Toolbox)
Non-spectral semantic segmentation of RGB/grayscale images (use Computer Vision Toolbox)
Point spectra or spectral libraries without spatial dimensions

Prerequisite Check

If you suspect the Hyperspectral Imaging Library might not be installed (e.g., user reports errors), verify with exist('imhypercube','file'). If not installed, suggest installing via Add-On Explorer and fall back to multibandread/imread. Do NOT run this check if you are confident the library is available.

Quick Start

% 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)

Essential Syntax (use without reading reference files)

colorize — Visualization

% 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

I/O and Band Management

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

Spectral Indices (NEVER compute manually — use built-in functions)

% 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

Detection, Matching, and Processing

% 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));

Workflow

For functions fully documented in the Essential Syntax and Common Mistakes sections above, generate code directly — those sections provide complete API signatures and constraints.

Read a reference file ONLY when:

Using a function NOT covered in Essential Syntax (e.g., sharc, empiricalLine, sharpencnmf, spectralImageLabeler)
Unsure about argument order or constraints for a complex workflow
The task involves the Spectral Image Labeler app or ground truth objects

Reference Files (consult only when needed):

references/spectral-io.md — I/O, band selection, cropping, block processing, colorize, enviwrite
references/spectral-calibration.md — Radiometric calibration, atmospheric correction, spectral correction, denoising, sharpening
references/spectral-analysis.md — PCA/MNF, unmixing, spectral matching, target/anomaly detection, indices, segmentation
references/spectral-deep-learning.md — Labeler app, groundTruthSpectralImage, deep learning pipelines
references/dl-classification.md — 3-D CNN, 1-D CNN, ground truth to training data workflows
references/dl-advanced.md — Unmixing autoencoder, transfer learning, DeepLabV3+ semantic segmentation

CRITICAL: Common Mistakes to Avoid

These are the most frequent errors. Violating any of these will cause runtime failures:

NEVER use hypercube() — deprecated. Use imhypercube() or geohypercube().
cropData(hcube, 30:110, 30:110) — row/col INDEX VECTORS, not [x y w h]
selectBands/removeBands require Name=Value: selectBands(hcube, BandNumber=[1 10 20]), removeBands(hcube, Wavelength=[1350 1450]). removeBands errors if range outside cube's actual wavelengths.
customSpectralIndex(spcube, [800 670], @(nir,red) (nir-red)./(nir+red)) — 3 args required
apply(hcube, @(block) mean(gather(block), 3)) — callback receives OBJECT, use gather()
inverseProjection(pcaCube, coeff) — data first, coefficients second
spectralMatch(libData, hcube) — struct from readEcostressSig FIRST. For numeric: use sam(hcube, target) directly.
L = hyperseganchor(hcube, 8) (1 output); [L, n] = hyperslic(hcube, 100) (2 outputs)
immulticube does NOT support ENVI. Sentinel-2: pass manifest.safe (NOT .SAFE folder). Landsat: *_MTL.txt. ASTER: .hdf.
spectralIndices(hcube, "NDVI") → struct with .IndexName/.IndexImage. Use ndvi(hcube) for numeric.
hyperslic with 3 bands: hyperslic(data, 100, IsInputDimReduced=true). Only params: NumIterations (max 30), IsInputDimReduced. NO Compactness.
detectTarget(hcube, target, "ACE") — method is POSITIONAL, not Name=Value
denoiseNGMeet: needs spatial dims > 16. Crop large images to ≤1024x1024 first.
flatField(hcube, [xmin ymin width height]) — rect vector, NOT index vectors like cropData
colorize(mcube) → true RGB (NO Method param!). colorize(mcube, [b1 b2 b3]) for false-color. colorize(hcube, Method="rgb") only works on hypercube.
resampleBands(mcube, 30) = resizes all bands to target res. selectBands(mcube, "DataResolution", 30) = keeps only bands already at that res.
NO NumRows/NumColumns/NumBands properties. Use hcube.Metadata.Height/Width/Bands. Multicube: mcube.BandSize(1,1)/mcube.BandSize(1,2).
NEVER manually compute indices. Use ndvi(spcube), spectralIndices(spcube, "NDVI"), or customSpectralIndex(spcube, [wl1 wl2], @(b1,b2) formula).
sam/sid refSpectra must be C-element VECTOR, not matrix.
assignData(hcube, rows, cols, bands, data) — 5 args with index ranges
enviwrite(hcube, "output") — hypercube ONLY, not multicube
groundTruthSpectralImage labelDefs: 6 columns required including AttributeList. Property: LabelDefinitions (plural).

Key Rules

Pipeline: DN → radiance → reflectance → atmospheric correction. Smile correction BEFORE denoising.
L2 products already have surface reflectance — DN pipeline is for L1 only.
Research datasets (Indian Pines, PaviaU) lack calibration metadata.
hypercube ONLY: denoiseNGMeet, hyperpca, hypermnf, inverseProjection, sharpencnmf, countEndmembersHFC, nfindr, ppi, estimateAbundanceLS, anomalyRX, sam, sid, spectralMatch, hyperslic, sharc, empiricalLine, reduceSmile
hypercube + multicube: dn2radiance, radiance2Reflectance, fastInScene, detectTarget, ndvi, spectralIndices, customSpectralIndex, hyperseganchor
+ numeric array: flatField, iarr, logResiduals, subtractDarkPixel
Deep learning: trainnet + dlnetwork (not trainNetwork). Use hyperpca before CNN.
Valid spectralIndices names: ndvi, osavi, sr, evi, gvi, ndni, pri, cai, mcari, mtvi, msi, cmr, nbr, ndbi, ndmi, mndwi (NOT "ndwi")
Labeler (R2026a+): groundTruthSpectralImage properties are read-only after creation

Files

ATTRIBUTION.mdAdded by AutoGineer1.1 KiB
LICENSELicence file added by AutoGineer1.4 KiB
SKILL.md14.5 KiB
manifest.yaml498 B
references/dl-advanced.md7.3 KiB
references/dl-classification.md7.7 KiB
references/spectral-analysis.md14.5 KiB
references/spectral-calibration.md10.0 KiB
references/spectral-deep-learning.md10.9 KiB
references/spectral-io.md17.0 KiB

references/spectral-io.md

Spectral Image I/O Reference

Reading, writing, and preparing hyperspectral/multispectral data using the Hyperspectral Imaging Library for Image Processing Toolbox.

Requirement: Desktop MATLAB only (not MATLAB Online or MATLAB Mobile). Requires the Hyperspectral Imaging Library add-on for Image Processing Toolbox.

CRITICAL: NEVER use the deprecated hypercube() constructor directly. It will be removed in a future release. Always use imhypercube, geohypercube, immulticube, or geomulticube instead.

Core Objects

Two object types: hypercube (hyperspectral, uniform bands) and multicube (multispectral, variable resolution bands).

Choosing the right creation function:

Data Type	No Geospatial	With Geospatial (needs Mapping Toolbox)
Hyperspectral (many narrow bands)	`imhypercube`	`geohypercube`
Multispectral (few broad bands)	`immulticube`	`geomulticube`

File format support:

Format	`imhypercube`/`geohypercube`	`immulticube`/`geomulticube`
ENVI (.dat, .hdr, .bsq, .bil, .bip)	Yes	No
NITF (.ntf)	Yes	No
EO-1 Hyperion (.L1R)	Yes	No
Landsat/GeoTIFF MTL (.txt)	Yes	Yes
Multipage TIFF (.tif)	Yes	Yes
TIFF folder (directory of .tif files)	No	Yes
ASTER HDF (.hdf)	No	Yes
Sentinel-2 (.safe)	No	Yes

Reading Hyperspectral Data

% WITHOUT geospatial info (returns hypercube object)
hcube = imhypercube(file)                          % from ENVI, NITF, Hyperion, GeoTIFF, multipage TIFF, Landsat MTL
hcube = imhypercube(file, wavelength)              % specify wavelengths
hcube = imhypercube(image, wavelength)             % from M-by-N-by-C numeric array
hcube = imhypercube(image, wavelength, metadata)   % with metadata struct
hcube = imhypercube(image)                         % array without wavelengths
hcube = imhypercube(___, BlockSize=blockSize)      % control memory via block size

% WITH geospatial info (requires Mapping Toolbox)
hcube = geohypercube(file)
hcube = geohypercube(file, wavelength)
hcube = geohypercube(image, wavelength)
hcube = geohypercube(image, wavelength, metadata)  % metadata must have RasterReference field
hcube = geohypercube(image)
hcube = geohypercube(___, BlockSize=blockSize)

Reading Multispectral Data

% WITHOUT geospatial info (returns multicube object)
mcube = immulticube(file)                          % from Landsat MTL .txt, multipage TIFF, ASTER .hdf, Sentinel-2 .safe, or TIFF folder path
mcube = immulticube(file, wavelength)
mcube = immulticube(image, wavelength)
mcube = immulticube(image, wavelength, metadata)
mcube = immulticube(image)
mcube = immulticube(___, BlockSize=blockSize)

% WITH geospatial info (requires Mapping Toolbox)
mcube = geomulticube(file)
mcube = geomulticube(file, wavelength)
mcube = geomulticube(image, wavelength)
mcube = geomulticube(image, wavelength, metadata)  % metadata must have RasterReference field
mcube = geomulticube(image)
mcube = geomulticube(___, BlockSize=blockSize)

Object Properties

hypercube properties (class: hyper.io.hypercube):

Property	Description
`ImageDims`	Dimensions and data type string, e.g. `"[610x340x103 double]"` (Since R2025a)
`Wavelength`	C-element vector of center wavelengths (nm)
`Metadata`	Struct with fields: `Height`, `Width`, `Bands`, `DataType`, `Interleave`, `HeaderOffset`, `ByteOrder`, `WavelengthUnits`, `AcquisitionTime`, `RasterReference`
`BlockSize`	2-element vector [rows cols] for block processing

IMPORTANT: There are NO NumRows, NumColumns, NumBands, or DataCube properties. Access dimensions via Metadata.Height, Metadata.Width, Metadata.Bands. The DataCube property was removed in R2025a — use gather(hcube) instead.

multicube properties:

Property	Description
`ImageFiles`	File references
`BandSize`	Dimensions of each band
`Wavelength`	Center wavelengths per band
`Metadata`	Associated metadata struct
`BandResolution`	Resolution per band
`BlockSize`	2-element vector [rows cols]

Writing Data

% Write to ENVI format (.dat + .hdr) — HYPERCUBE ONLY
% Use for hyperspectral data (many narrow bands). NOT for multispectral satellite data (Landsat, Sentinel-2).
% Multispectral satellite data should remain in its native format (GeoTIFF, etc.)
enviwrite(hcube, filename)
enviwrite(hcube, filename, Interleave="bsq")       % "bsq" (default), "bil", or "bip"
enviwrite(hcube, filename, DataType="single")      % "uint8","uint16","uint32","uint64","int16","int32","int64","single","double"
enviwrite(hcube, filename, ByteOrder="ieee-le")    % "ieee-le" or "ieee-be"
enviwrite(hcube, filename, HeaderOffset=0)         % bytes before data start

Reading ENVI Metadata

info = enviinfo(file)   % file is .hdr path
% Returns struct with fields:
%   Required: Height, Width, Bands, DataType, Interleave, HeaderOffset, ByteOrder
%   Optional: Wavelength, FWHM, Gain, Offset, BadBands, BandNames, MapInfo
%   File info: Filename, FileModDate, FileSize, Format, RasterFormat
% Supports .hdr, .dat, .bsq, .bil, .bip, .img extensions

Gathering Data Into Workspace

% Read full data cube into memory (Since R2025a)
dataCube = gather(hcube)    % returns M-by-N-by-C numeric array
dataCube = gather(mcube)    % for multicube

ROI Selection

% Assign new data to specific locations
newspcube = assignData(spcube, row, column, band, data)
% row, column, band: positive integer scalars or vectors
% data: must match dimensions of indexed region

% Crop region of interest
newspcube = cropData(spcube, row, column)           % crop spatial region (all bands)
newspcube = cropData(spcube, row, column, band)     % crop spatial + spectral (hypercube only)
% row, column, band: vectors of indices, e.g., 1:100

Band Selection, Removal, and Resampling

% Select bands by wavelength range (hypercube only)
newhcube = selectBands(hcube, Wavelength=[minWL maxWL])

% Select bands by index (hypercube and multicube)
newspcube = selectBands(spcube, BandNumber=bands)

% Select bands by resolution (multicube only)
newmcube = selectBands(mcube, DataResolution=resolution)

% Select most informative bands for endmember discrimination (hypercube only)
[newhcube, bandIdx] = selectBands(hcube, endmembers)                    % endmembers: C-by-K matrix
[newhcube, bandIdx] = selectBands(hcube, endmembers, NumberOfBands=n)   % specify count

% Remove bands by wavelength range (hypercube only)
newhcube = removeBands(hcube, Wavelength=[Wmin1 Wmax1; Wmin2 Wmax2])  % K-by-2 matrix

% Remove bands by index (hypercube only)
newhcube = removeBands(hcube, BandNumber=band)

% Resample multicube bands to uniform resolution (Since R2025a)
newmcube = resampleBands(mcube, resolution)
newmcube = resampleBands(mcube, resolution, Method="nearest")   % "nearest" (default), "bilinear", or "cubic"

Color Transformation

% For hypercube: supports Method parameter, 2 outputs, ContrastStretching defaults to FALSE
coloredImage = colorize(hcube)                                   % default: false-colored (3 most informative bands via selectBands)
coloredImage = colorize(hcube, band)                             % specify 3 band indices (false color composite)
[coloredImage, indices] = colorize(hcube)                        % also return selected band indices (2 outputs)
coloredImage = colorize(hcube, Method="rgb")                     % true color (R:600-700, G:500-600, B:400-500 nm)
coloredImage = colorize(hcube, Method="cir")                     % color infrared (NIR:760-960, R, G)
coloredImage = colorize(hcube, Method="falsecolored")            % default method
coloredImage = colorize(hcube, ContrastStretching=true)          % apply CLAHE (default: false)

% For multicube: NO Method parameter, 1 output only, ContrastStretching defaults to TRUE
% WARNING: colorize(mcube, Method="rgb") will ERROR — Method is NOT supported for multicube
coloredImage = colorize(mcube)                                   % TRUE RGB (auto-selects R,G,B bands by wavelength)
coloredImage = colorize(mcube, band)                             % false color composite from 3 band indices
coloredImage = colorize(mcube, ContrastStretching=false)         % disable contrast stretching (default: true)

% KEY DIFFERENCES:
% | Feature              | hypercube                     | multicube                          |
% |----------------------|-------------------------------|------------------------------------|
% | Default behavior     | false-colored (informative)   | true RGB (by wavelength)           |
% | Method parameter     | "rgb","cir","falsecolored"    | NOT supported                      |
% | ContrastStretching   | default FALSE (CLAHE)         | default TRUE (imadjust)            |
% | Number of outputs    | [img, indices]                | img only                           |
%
% For multicube RGB: colorize(mcube) already gives RGB! No need to specify band indices.
% For custom band composite: colorize(mcube, [bandR bandG bandB])
% Landsat 4-5 TM bands: Band 3=Red, Band 2=Green, Band 1=Blue
% Landsat 8 OLI bands:  Band 4=Red, Band 3=Green, Band 2=Blue

Block Processing (Since R2025a)

% Apply function to each block of a large spectral image
outputCube = apply(spcube, fcn)
outputCube = apply(spcube, fcn, BlockSize=[rows cols])
% fcn: function handle that receives a hypercube/multicube block object
% Use gather(block) inside fcn to get the numeric M-by-N-by-C array
% The function is applied to each spatial block independently
% Enables out-of-memory processing for large datasets

% Example: normalize spectra block-by-block
result = apply(hcube, @(block) gather(block) ./ max(vecnorm(gather(block), 2, 3), eps));

Common Workflows

Workflow 1: Load and inspect ENVI file

info = enviinfo("scene.hdr");
hcube = imhypercube("scene.hdr");
disp(hcube.ImageDims)
disp(hcube.Wavelength)
rgb = colorize(hcube, Method="rgb");
imshow(rgb)

Workflow 2: Load large file with blocked processing

hcube = imhypercube("large_scene.hdr", BlockSize=[512 512]);
% Process block-by-block without loading entire cube
% fcn receives a hypercube block — use gather(block) to get numeric array
result = apply(hcube, @(block) myProcessingFcn(gather(block)));

Workflow 3: Load Sentinel-2 multispectral data

mcube = immulticube("path/to/scene.SAFE/manifest.safe");
% Select specific resolution bands
mcube10m = selectBands(mcube, DataResolution=10);
rgb = colorize(mcube10m);  % multicube does not support Method param
imshow(rgb)

Workflow 4: Crop and export subset

hcube = imhypercube("scene.hdr");
% Crop spatial ROI and select VNIR bands
cropped = cropData(hcube, 100:500, 200:600, 1:100);
% Remove noisy bands
cropped = removeBands(cropped, Wavelength=[1350 1450; 1800 1950]);
enviwrite(cropped, "subset_scene")

Workflow 5: Create hypercube from numeric array

data = rand(256, 256, 224, 'single');
wavelength = linspace(400, 2500, 224);
metadata.WavelengthUnits = "Nanometers";
hcube = imhypercube(data, wavelength, metadata);

Workflow 6: Load Landsat data via MTL file and get RGB

% Landsat scenes use MTL text files that reference TIFF band files
mcube = immulticube("LC08_L1TP_MTL.txt");

% Get RGB — colorize(mcube) automatically selects R,G,B bands by wavelength
% NOTE: Do NOT use Method="rgb" — that is hypercube-only and will ERROR on multicube
rgb = colorize(mcube);         % true RGB with contrast stretching (default)
imshow(rgb)

% Without contrast stretching:
rgb = colorize(mcube, ContrastStretching=false);

% Custom false-color composite using specific band indices:
% Landsat 8 OLI: Band 4=Red, Band 3=Green, Band 2=Blue
falseColor = colorize(mcube, [5 4 3]);   % NIR, Red, Green false color

Workflow 7: Resample multicube to uniform resolution

% Load multispectral data with mixed resolutions (requires SpacecraftID in metadata)
mcube = immulticube("path/to/scene.SAFE/manifest.safe");
% Resample all bands to 10m resolution
mcube10m = resampleBands(mcube, 10);
% Now all bands have uniform spatial dimensions
dataCube = gather(mcube10m);

Supported Formats

Format	Function	File Extension
ENVI	`imhypercube` / `geohypercube`	`.hdr`, `.dat`
NITF	`imhypercube` / `geohypercube`	`.ntf`, `.nitf`
GeoTIFF	`imhypercube` / `immulticube`	`.tif`, `.tiff`
Multipage TIFF	`imhypercube` / `immulticube`	`.tif`, `.tiff`
ASTER HDF	`immulticube` / `geomulticube`	`.hdf`
Sentinel-2	`immulticube` / `geomulticube`	`.safe`
Landsat (MTL)	`immulticube` / `geomulticube`	`*_MTL.txt`
ECOSTRESS (spectral library)	`readEcostressSig`	`.txt` (signature files, NOT satellite images)
Hyperion	`imhypercube`	`.hdr`

Blocked I/O adapters (ENVI, NITF, ASTER, HDF, TIFF, Sentinel-2, Landsat MTL) are used internally when BlockSize is specified — no user action required beyond setting the parameter.

Key Rules

Use imhypercube/immulticube for data without geospatial info
Use geohypercube/geomulticube when geospatial referencing is needed (requires Mapping Toolbox)
BlockSize controls memory usage — default is [1024 1024]; read-only after creation (must recreate object to change)
gather loads the full cube into memory — use only when RAM allows
apply enables out-of-memory processing on blocked images; the function handle receives a hypercube/multicube block object — use gather(block) inside to get the numeric array (block is NOT numeric directly)
enviwrite supports interleave formats: "bsq" (band sequential), "bil" (band interleaved by line), "bip" (band interleaved by pixel)
enviinfo accepts extensionless files and multiple ENVI extensions (.hdr, .dat, .bsq, .bil, .bip, .img)
resampleBands requires SpacecraftID in metadata — fails on multicubes created from numeric arrays; use selectBands(mcube, DataResolution=res) as alternative
resampleBands supports interpolation methods: "nearest" (default), "bilinear", "cubic"
For geohypercube/geomulticube, the RasterReference in metadata is automatically updated when using cropData
colorize with ContrastStretching=true applies adaptive histogram equalization (CLAHE) for enhanced visualization

Multicube Limitations

selectBands: only DataResolution and BandNumber — does NOT support Wavelength or endmember selection
removeBands: NOT available for multicube — use selectBands(mcube, BandNumber=...) to keep desired bands
cropData: spatial only (row, column) — does NOT accept band parameter; mixed-resolution bands will error
colorize: does NOT support Method parameter ("rgb", "cir", "falsecolored" will ERROR). Default colorize(mcube) gives true RGB (auto-selects R/G/B by wavelength). Use band indices for custom composites: colorize(mcube, [b1 b2 b3]). ContrastStretching defaults to true (unlike hypercube which defaults to false)
enviwrite: NOT available for multicube — ENVI format is for hyperspectral data only; multispectral satellite data (Landsat, Sentinel-2) should remain in native GeoTIFF format
Must unify resolution before spatial operations: selectBands(mcube, DataResolution=res) or resampleBands

Labeling (Since R2026a)

% Create ground truth object for spectral images
gTruth = groundTruthSpectralImage(dataSource, labelDefs)
gTruth = groundTruthSpectralImage(dataSource, labelDefs, labelData)
gTruth = groundTruthSpectralImage(___, Resolution=resolution)

% dataSource: file path (string) or SpectralImageSource object
% labelDefs: table with REQUIRED columns:
%   LabelName (string), LabelColor (1x3 RGB), LabelID (integer),
%   LabelGroup (string), Description (string), AttributeList (cell)
% labelData: table with label mask data (optional)
% Properties: DataSource, LabelDefinitions (plural!), LabelData, Resolution

% Select labels by name or group
subGTruth = selectLabelsByName(gTruth, "Vegetation")
subGTruth = selectLabelsByGroup(gTruth, "Land")

% Merge multiple ground truth objects
mergedGTruth = merge(gTruth1, gTruth2)

% Change file paths (for relocating data — takes FOLDER paths, not file paths)
unresolvedPaths = changeFilePaths(gTruth, [oldFolder, newFolder])
% alternateFilePaths: n-by-2 string array of [oldFolder, newFolder] pairs

Labeling Example

% Create label definition table (all 6 columns required)
labelDefs = table( ...
    ["Vegetation";"Soil";"Water"], ...
    [0 1 0; 0.6 0.3 0; 0 0 1], ...
    [1;2;3], ...
    ["Land";"Land";"Water"], ...
    ["Veg areas";"Soil areas";"Water bodies"], ...
    {[];[];[]}, ...
    VariableNames=["LabelName","LabelColor","LabelID","LabelGroup","Description","AttributeList"]);
hdrFile = which("indian_pines.hdr");
gTruth = groundTruthSpectralImage(hdrFile, labelDefs);
landLabels = selectLabelsByGroup(gTruth, "Land");  % returns 2 labels

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

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Attribution and licence

Created by

MathWorks

Publisher

MathWorks (Official)

Source

matlab/matlab-agentic-toolkit · skills-catalog/image-processing-and-computer-vision/matlab-analyze-spectral-images · commit 2a7f215

Licence

BSD 2-Clause License (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE.md)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by MathWorks or MathWorks.

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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download matlab-analyze-spectral-images.zip

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by MathWorks

Spectral Imaging Skill

When to Use

When NOT to Use

Prerequisite Check

Quick Start

Essential Syntax (use without reading reference files)

colorize — Visualization

I/O and Band Management

Spectral Indices (NEVER compute manually — use built-in functions)

Detection, Matching, and Processing

Workflow

CRITICAL: Common Mistakes to Avoid

Key Rules

Files

references/spectral-io.md

Spectral Image I/O Reference

Core Objects

Reading Hyperspectral Data

Reading Multispectral Data

Object Properties

Writing Data

Reading ENVI Metadata

Gathering Data Into Workspace

ROI Selection

Band Selection, Removal, and Resampling

Color Transformation

Block Processing (Since R2025a)

Common Workflows

Workflow 1: Load and inspect ENVI file

Workflow 2: Load large file with blocked processing

Workflow 3: Load Sentinel-2 multispectral data

Workflow 4: Crop and export subset

Workflow 5: Create hypercube from numeric array

Workflow 6: Load Landsat data via MTL file and get RGB

Workflow 7: Resample multicube to uniform resolution

Supported Formats

Key Rules

Multicube Limitations

Labeling (Since R2026a)

Labeling Example

Requirements and compatibility

Security

Attribution and licence

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Relion

Qutip

Simpy

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