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Hyperspectral Image Processing

Import, export, process, and visualize hyperspectral data

Image Processing Toolbox™ Hyperspectral Imaging Library provides MATLAB^® functions and tools for hyperspectral image processing and visualization.

Use the functions in this library to read, write, and process hyperspectral data captured by using the hyperspectral imaging sensors in a variety of file formats. The library supports national imagery transmission format (NITF), environment for visualizing images (ENVI), tagged image file format (TIFF), and metadata text extension (MTL) file formats.

The library presents a set of algorithms for endmember extraction, abundance map estimation, radiometric and atmospheric correction, dimensionality reduction, band selection, spectral matching, and anomaly detection.

The Hyperspectral Viewer app enables you to read hyperspectral data, visualize individual band images and their histograms, create a spectrum plot for a pixel or region in a hyperspectral data cube, generate different color or false-color representations of hyperspectral images, and display metadata.

To perform hyperspectral image analysis, download the Image Processing Toolbox Hyperspectral Imaging Library from the Add-On Explorer. For more information on downloading add-ons, see Get and Manage Add-Ons.

Apps

Hyperspectral Viewer

Visualize hyperspectral data (Since R2020a)

Functions

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Explore, Analyze, and Visualize

Read and Write

`hypercube`	Read hyperspectral data (Since R2020a)
`enviwrite`	Write hyperspectral data to ENVI file format (Since R2020a)
`enviinfo`	Read metadata from ENVI header file (Since R2020a)

Band Selection and Band Removal

`selectBands`	Select most informative bands (Since R2020a)
`removeBands`	Remove spectral bands from data cube (Since R2020a)

ROI Selection

`assignData`	Assign new data to hyperspectral data cube (Since R2020a)
`cropData`	Crop regions-of-interest (Since R2020a)

Color Transformation

colorize Estimate color image of hyperspectral data (Since R2020a)

Filtering and Enhancement

`denoiseNGMeet`	Denoise hyperspectral images using non-local meets global approach (Since R2020b)
`sharpencnmf`	Sharpen hyperspectral data using coupled nonnegative matrix factorization (CNMF) method (Since R2020b)

Data Correction

Radiometric Calibration

`dn2radiance`	Convert digital number to radiance (Since R2020b)
`dn2reflectance`	Convert digital number to reflectance (Since R2020b)
`radiance2Reflectance`	Convert radiance to reflectance (Since R2020b)

Atmospheric Correction

`correctOOB`	Correct out-of-band effect using sensor spectral response (Since R2020b)
`empiricalLine`	Empirical line calibration of hyperspectral data (Since R2020b)
`fastInScene`	Perform fast in-scene atmospheric correction (Since R2020b)
`flatField`	Apply flat field correction to hyperspectral data cube (Since R2020b)
`iarr`	Apply internal average relative reflectance (IARR) correction to hyperspectral data cube (Since R2020b)
`logResiduals`	Apply log residual correction to hyperspectral data cube (Since R2020b)
`rrs`	Compute remote sensing reflectance (Since R2020b)
`subtractDarkPixel`	Subtract dark pixel value from hyperspectral data cube (Since R2020b)
`sharc`	Perform atmospheric correction using satellite hypercube atmospheric rapid correction (SHARC) (Since R2020b)

Spectral Correction

`smileMetric`	Compute spectral smile metrics of hyperspectral data (Since R2021a)
`reduceSmile`	Reduce spectral smile effect in hyperspectral data cube (Since R2020b)

Dimensionality Reduction

`hyperpca`	Principal component analysis of hyperspectral data (Since R2020a)
`hypermnf`	Maximum noise fraction transform of hyperspectral data (Since R2020a)
`inverseProjection`	Reconstruct data cube from principal component bands (Since R2020a)

Spectral Unmixing

`ppi`	Extract endmember signatures using pixel purity index (Since R2020a)
`fippi`	Extract endmember signatures using fast iterative pixel purity index (Since R2020a)
`nfindr`	Extract endmember signatures using N-FINDR (Since R2020a)
`countEndmembersHFC`	Find number of endmembers (Since R2020a)
`estimateAbundanceLS`	Estimate abundance maps (Since R2020a)

Spectral Matching and Target Detection

`readEcostressSig`	Read data from ECOSTRESS spectral library (Since R2020a)
`sam`	Measure spectral similarity using spectral angle mapper (Since R2020a)
`sid`	Measure spectral similarity using spectral information divergence (Since R2020a)
`jmsam`	Measure spectral similarity using Jeffries Matusita-Spectral Angle Mapper method (Since R2020b)
`sidsam`	Measure spectral similarity using spectral information divergence-spectral angle mapper hybrid method (Since R2020b)
`ns3`	Measure normalized spectral similarity score (Since R2020b)
`spectralMatch`	Identify unknown regions or materials using spectral library (Since R2020a)
`spectralIndices`	Compute hyperspectral indices (Since R2020b)
`customSpectralIndex`	Compute hyperspectral index using custom formula (Since R2023a)
`ndvi`	Normalized difference vegetation index (Since R2020a)
`anomalyRX`	Detect anomalies using Reed-Xiaoli detector (Since R2020a)

Segmentation

hyperSlic 2-D superpixel oversegmentation of hyperspectral images (Since R2023b)

Topics

Get Started

Getting Started with Hyperspectral Image Processing
Basics of hyperspectral image processing.
Explore Hyperspectral Data in the Hyperspectral Viewer
This example shows how to explore hyperspectral data using the Hyperspectral Viewer app.
Hyperspectral Data Correction
Describes radiometric calibration and atmospheric correction.
Spectral Indices
Describes spectral indices.
Support for Singleton Dimensions
Analysis of 1-D and 2-D spectral data using singleton hypercube.

Classification

Classify Hyperspectral Image Using Library Signatures and SAM
This example shows how to classify pixels in a hyperspectral image by using the spectral angle mapper (SAM) classification algorithm.
Classify Hyperspectral Images Using Deep Learning
This example shows how to classify hyperspectral images using a custom spectral convolution neural network (CSCNN) for classification.
Classify Hyperspectral Image Using Support Vector Machine Classifier
This example shows how to preprocess a hyperspectral image and classify it using a support vector machine (SVM) classifier.

Region Identification

Target Detection Using Spectral Signature Matching
This example shows how to detect a known target in the hyperspectral image by using spectral matching method.
Identify Vegetation Regions Using Interactive NDVI Thresholding
This example shows how to identify the types of vegetations regions in a hyperspectral image through interactive thresholding of a normalized difference vegetation index (NDVI) map.
Find Regions in Spatially Referenced Multispectral Image
This example shows how to identify water and vegetation regions in a Landsat 8 multispectral image and spatially reference the image.

Featured Examples

Ship Detection from Sentinel-1 C Band SAR Data Using YOLO v2 Object Detection

Detect ships from Sentinel-1 C Band SAR Data using YOLO v2 object detection.

Open Live Script

Change Detection in Hyperspectral Images

Detect changes in land cover from hyperspectral images taken at different times.

Open Live Script

Manually Label ROIs in Multispectral Image

Manually select regions of interest (ROIs) from a multispectral image and save them in a shapefile.

Open Live Script

Hyperspectral Image Analysis Using Maximum Abundance Classification

Identify different regions in a hyperspectral image by performing maximum abundance classification (MAC). An abundance map characterizes the distribution of an endmember across a hyperspectral image. Each pixel in the image is either a pure pixel or a mixed pixel. The set of abundance values obtained for each pixel represents the percentage of each endmembers present in that pixel. In this example, you will classify the pixels in a hyperspectral image by finding the maximum abundance value for each pixel and assigning it to the associated endmember class.

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Endmember Material Identification Using Spectral Library

Identify the classes of endmember materials present in a hyperspectral image. The endmembers are pure spectral signature that signifies the reflectance characteristics of pixels belonging to a single surface material. The existing endmember extraction or identification algorithms extracts or identifies the pure pixels in a hyperspectral image. However, these techniques do not identify the material name or class to which the endmember spectrum belong to. In this example, you will extract the endmember signatures and then, classify or identify the class of an endmember material in the hyperspectral image by using spectral matching.

Open Live Script

Automate Pixel Labeling of Hyperspectral Images Using ECOSTRESS Spectral Signatures in Image Labeler

Load hyperspectral images into the Image Labeler and automatically label pixels.

Open Live Script