Hyperspectral imagery is a type of remote sensing technology that captures and analyzes information from across the electromagnetic spectrum in numerous, narrowly defined wavelength bands. Airborne and satellite hyperspectral sensors collect imagery data across hundreds, or even thousands, of contiguous spectral bands, each representing a very narrow range of wavelengths. The high spectral resolution of hyperspectral imagery allows objects and materials to be identified based on their unique spectral signature and other analytical characteristics.
Characteristics of hyperspectral imagery
Hyperspectral imagery (HSI) sensors capture a wide range of wavelengths across the electromagnetic spectrum, providing highly detailed spectral information about objects and surfaces. Key characteristics include the following:
- High spectral resolution—HSI captures hundreds of contiguous spectral bands, typically covering the visible, near-infrared (NIR), shortwave infrared (SWIR), and sometimes thermal infrared (TIR) regions.
- Spectral signatures—Each material has a unique spectral signature, allowing precise identification of materials based on their reflectance properties.
- Contiguous spectral bands—Bands are captured in a continuous manner, rather than discrete, ensuring a detailed spectral profile for each pixel.
- High data volume—Due to the large number of bands, HSI generates significant amounts of data, requiring advanced processing techniques such as spectral band selection, dimensionality reduction, or machine learning.
- Material discrimination—The detailed spectral information enables better differentiation between similar-looking materials, useful in applications like mineral mapping, agriculture, and environmental monitoring.
- Spatial and spectral correlation—Each pixel contains a full spectral profile, allowing both spatial and spectral analysis of objects in a scene.
- Applications in various disciplines—HSI is widely used in agriculture (crop health monitoring), geology (mineral and hydrocarbon exploration), defense (target detection), medical and material imaging, and environmental science.
Supported sensors
The supported sensors, and their associated raster datasets, include the following:
- AVIRIS—The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is an airborne imaging sensor with 224 contiguous spectral bands, ranging from ultraviolet (360nm) to shortwave infrared (2,500nm). Ground resolution depends on flying height.
- EMIT—The Earth Surface Mineral Dust Source Investigation (EMIT) instrument is a space borne imaging spectrometer with 285 continuous bands ranging from 380nm to 2,500nm. The ground resolution is 60m per pixel.
- Hyperion—Hyperion is a satellite imaging spectrometer with 220 contiguous bands ranging from 360nm to 2,500nm. The ground resolution is 30m per pixel.
Refer to Use supported hyperspectral sensors for more details about the supported hyperspectral sensors.
Refer to Add hyperspectral data for how to ingest hyperspectral data.
Visualize HSI data in a map
ArcGIS supports hyperspectral images stored in standard raster formats, such as TIFF and ENVI formats, as well as EMIT imagery stored in NetCDF, and AVIRIS imagery stored in ENVI format. Since you cannot browse all these formats or use drag-and-drop functions as regular raster datasets in the Catalog pane, a common way to add hyperspectral data to a map is by using the Add Data 
button.
- This video was created with ArcGIS Pro 3.4.
HSI Analysis
The complex nature of HSI requires advanced capabilities to ingest, manage and analyze the data. HSI is managed in the mosaic dataset container, in which the radiometric, spectral and spatial information is stored and handled to support visualization and quantitative analysis.
Due to the high number of spectral bands, data volume, and nature and complexity of HSI data, special tools and unique methodologies have been developed for analysis. For example, the full spectrum of the sensor is associated with each pixel comprising the image. This allows for objects and materials to be identified by spectral signatures, often with subtle spectral differences. Consequently, extensive spectral libraries representing thousands of materials and objects have been developed by NASA, USGS, European Space Agency (ESA), and other international organizations.
Various tools and capabilities to analyze HSI data are outlined below.
Spectral profile chart
Spectral profile charts allow you to select areas of interest or ground features on the image and review the spectral information of all bands in a chart format. A spectral profile consists of geometry to define the pixel selection and an image with key metadata from which to sample.
The graphic above plots the spectral profile of various features in AVIRIS imagery. Each feature is represented by the color of choice. Notice the null data around wavelength ranges centered around 1300 and 1900. These represent noisy bands caused by water vapor in the atmosphere. These bands were specifically excluded for more accurate analysis. See Spectral profile for more information.
Linear Spectral Unmixing
The Linear Spectral Unmixing geoprocessing tool and raster function performs subpixel classification and calculates the fractional abundance of different material or land cover types for individual pixels. It calculates the fractional cover for individual pixels that contain multiple land cover types. A multiband layer is generated, in which each band corresponds to the fractional abundance of each land cover class. For example, you can use it to perform land cover classification on an HSI image to identify types of vegetation, bare soil, and dead or nonphotosynthetic vegetation. The result is a multidimensional raster in which each slice is a multiband raster containing the fractional abundance of each land cover class. See the Linear Spectral Unmixing tool for more information.
Detect Image Anomalies
The Detect Image Anomalies geoprocessing tool identifies irregularities in imagery. An anomaly in an image refers to pixels that are significantly different from the background values, such as ships in the ocean, vehicles on a road, or human development in natural areas. The tool supports the Reed-Xiaoli Detector (RXD), Uniform Target Detector (UTD), and KMEANS image anomaly detection methods. It processes a multiband or hyperspectral image and creates an anomaly score raster. An anomaly score raster is a single band raster, with values between 0 and 1. See Detect Image Anomalies for more information.
Classify Raster Using Spectra
The Classify Raster Using Spectra geoprocessing tool classifies an HSI dataset using spectral matching techniques based on input spectral data for different objects or classes. The tool uses two spectral matching methods:
- Spectral Angle Mapper, in which the vector angle between the input multiband raster and the reference spectra will be calculated in which the spectra of each pixel is treated as a vector. The angle values are in radians.
- Spectral Information Divergence, in which the spectral information divergence between the input multiband raster and the reference spectra will be calculated. A score will be calculated for each pixel based on the divergence between the probability distributions of the pixel and reference spectra. The values are in radians.
The tool can also produce a multiband output score raster that stores the matching results for each end member. The band order follows the order of the classes in the input class spectral data. See Classify Raster Using Spectra for more information.
Spectral anomaly detection wizard
Image anomalies refer to pixels and objects that are spectrally distinct from the surrounding image background. The purpose of anomaly detection is to analyze an image and detect unknown targets with distinct spectral characteristics, allowing image analysts to quickly focus on areas requiring further investigation. Anomaly detection does not rely on predefined inputs, unlike target detection or feature extraction which require reference spectra or training data. Instead, it computes an average spectral background and identifies pixels that deviate significantly from this background.
The Anomaly Detection Wizard guides you through the entire anomaly detection and extraction workflow from start to finish. The Anomaly Detection Wizard is composed of best practices and a simplified user experience, so you can perform image anomaly detection without missing a step.
The Anomaly Detection Wizard is found in the Analysis group on the Imagery tab. Select the raster dataset to analyze in the Contents pane to display the Imagery tab, click the Spectral Analysis drop-down expander, and select the Anomaly Detection Wizard. See Anomaly Detection Wizard for more information about the anomaly detection workflow.