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Hyperspectral Remote Sensing : What Is It, How Is It Used And Its Classifications

Hyperspectral Remote Sensing is the acquisition of data from the Earth’s surface and atmosphere using airborne sensors or space borne sensors. The main difference of Hyperspectral Remote Sensing from MultiSpectral Remote Sensing is the ability of HyperSpectral Remote sensing to capture information across a wide range of ElectroMagnetic Spectrum. Unlike traditional Sensor that records only a few broad spectrum bands, hyperspectral sensors gather data across hundreds of narrow bands.This capability allows the creation of the continuous spectral reflectance signatures, making the hyperspectral data useful for in-depth analysis of Earth’s surface. 

The data that are collected via the hyperspectral remote sensing is organized into hypercubes and each cube contains two spatial dimensions and one spectral dimension. These hypercubes are the building blocks to extract valuable information from the image.

Hyperspectral data cube structure


The cube consists of four main components:

Push-Broom Scanning

The Push Broom Scanner or Along-Track Scanner is a spectroscopic sensor that is commonly used in Remote sensing from space or airborne. It operates by continuously scanning along a specific path, offering advantage in light capture but has potentially lower resolution compared to other methods.

Sequential Scan Lines

In the above image, multiple scan lines are stacked on top of another creating a 3 dimensional hyperspectral data cube. The X and Y axis in the image represent the spatial data, whereas the Z dimension represents the Spectral information. 

Stacked Spatial Images 

Each 2 Dimensional Spatial image corresponds to a specific narrow waveband and hyper spectral data cubes typically contain hundreds of stacked images forming a hyperspectral data cube. 

Spectral Samples

These samples allow the detection and classification of features in the spectra, serving as the primary mechanism for scene analysis.

In obtaining the HyperSpectral Data, there are 3 different methods that can be employed based on the type of imaging spectrometers. They are

  • dispersive elements-based approach,
  • spectral filters-based approach,
  • snapshot hyperspectral imaging

These methods are used to capture hyperspectral images with varying spatial and temporal resolutions with sensors mounted on different platforms such as unmanned aerial vehicle ( UAV ), airplanes and close-range platforms. 

Hyperspectral Imaging Platforms Comparison

CriteriaPlatform APlatform BPlatform C
Wavelength Range400 – 1000 nm450 – 900 nm380 – 1100 nm
Spatial Resolution0.5 meters1 meter0.3 meters
Spectral Resolution10 nm5 nm3 nm
Number of Bands200300400
Imaging Speed1000 lines/second500 lines/second1500 lines/second
Data Storage and Transfer1 TB SSD, USB 3.0500 GB HDD, USB 2.02 TB SSD, Thunderbolt 3
Operating Temperature Range-10°C to 40°C-20°C to 50°C-5°C to 45°C
Integration with UAV/DroneYesNoYes
Power Consumption100 W75 W120 W
Price (Approximate)$50,000$30,000$60,000
Spectral CalibrationFactory calibratedRequires calibrationFactory calibrated
Software SupportUser-friendly GUILimited softwareComprehensive software
Application ExamplesAgriculture, GeologyEnvironmental StudiesRemote Sensing
Data Processing CapabilitiesReal-time processingLimited processingAdvanced processing
Warranty and Support2 years1 year3 years
AvailabilityWidely availableLimited availabilityWidely available
Weight5 kg3 kg6 kg
Dimensions (L x W x H)30 cm x 20 cm x 15 cm25 cm x 15 cm x 10 cm35 cm x 25 cm x 18 cm
Additional FeaturesGPS, IMU, RadiometricNoneGPS, IMU, Pan-Sharpening
Hyperspectral Imaging Platforms Comparison

Types of Hyperspectral Sensors on Aircraft and Satellites

Sensor TypeKey CharacteristicsApplicationsPlatforms
PushbroomHigh spectral and spatial resolution, continuous dataAgriculture, mineral exploration, forestryAircraft, Satellites
WhiskbroomHigh spectral resolution, lower spatial resolutionOceanography, atmospheric studiesSatellites
SnapshotSimultaneous capture of entire sceneMedical imaging, surveillance, astronomySatellites, Airborne Systems
Dual-PushbroomSimultaneous collection of two spectral rangesGeology, coastal monitoringAircraft
Fourier TransformInterferometric technique, high spectral resolutionEnvironmental monitoring, geologyAircraft, Satellites
PrismModerate spatial and spectral resolutionUrban planning, vegetation analysisSatellites
AISA (Airborne Imaging Spec)High spectral resolution, variable configurationsEnvironmental monitoring, agricultureAircraft
CASI (Compact Airborne Spec)Good spectral and spatial resolution, compact designPrecision agriculture, land cover mappingAircraft
HSI-3Hyperspectral and LiDAR fusionVegetation health assessment, topographyAirborne Systems
HySpexHigh spectral resolution, versatile configurationsMineral exploration, remote sensingAircraft, Satellites
EnMAP (Environmental Mapping)Wide spectral range, medium resolutionLand use classification, forestrySatellites
WorldView-3High spatial resolution, limited spectral rangeUrban planning, disaster assessmentSatellites
HyperionHigh spectral resolution, limited spatial resolutionGeological mapping, environmental analysisSatellites
Sentinel-2Moderate spatial and spectral resolutionAgriculture, land cover, disaster monitoringSatellites
Landsat-8Moderate spatial and spectral resolutionLand use, environmental monitoringSatellites
Types of Hyperspectral Sensors on Aircraft and Satellites

Hyperspectral Remote Sensing Imagery (HRSI) Data Processing and Analyzing

Data PreProcessing

Hyperspectral data comes with its own sets of challenges, mainly due to its high dimensions and the complexity of the spectral data. Some of the key challenges are, 

High Dimensionality

Hyperspectral images are characterized by their high dimensionality. With hundreds of spectral bands, it can be hard to process the dimension of hyperspectral data.

Missing Labeled Samples

Acquiring hyperspectral images can be relatively easy but accurately labeling the collected information can be really channel. This shortage of the labeled information can be a real challenge in classifying the  hyperspectral images.

Spectral Variability Across Space: 

The spectral information in hyperspectral images can be different across the special dimensions due to many factors such as atmospheric conditions, sensor variations and  ground features. this difference can make the classification process complicated.

Image Quality

Image quality is an important factor in hyperspectral  collection. Noise and errors during the data acquisition can significantly impact data quality affecting the accuracy of the classification process

To overcome these challenges, Hyperspector data  undergoes a range of preprocessing steps. Unlike Multispectral and RGB sensors, the radiometric and atmospheric calibration workflow for hyperspectral data are more complicated. 

Hyperspectral image preprocessing workflow

The workflow includes several essential steps:

Spatial Calibration 

Spatial calibration is nothing but aligning each image pixel with its known units or features, providing information about spatial dimensions and correcting the optical distortions such as the smile and keystone effect. However, calibrating can be again misaligned by factors such as the changes in environment and the equipment. 

Dimensionality Reduction

Since there are a high number of spectral bands in the hyperspectral images, dimensional reduction is really important. The process of converting high-dimensional data into a low-dimensional space while keeping the spectral information intact. Dimensionality reduction not only improves the processing speed but also increases classification accuracy. 

Hyperspectral Image Classification

HyperSpectral Image Classification is important in extracting useful information from the hyperspectral data. It categorizes pixels in the image into predefined classes or clusters based on the spectral characteristics. The hyperspectral images are classified into 3 main types. 

  • Supervised
  • Unsupervised
  • Semi-Supervised

Supervised Classification

In supervised classification, labeled data is used for training. Common supervised classification methods include Support Vector Machines ( SVM ), Artificial Neural Networks ( ANN ), Decision Tree and Maximum Likelihood Classifications. These methods are used to determine discrete criteria based on known sample categories and prior knowledge. 

Unsupervised Classification

Unsupervised classification, on other hand, doesn’t rely on prior labeled images. It categorizes pixels based on spectral similarities, often involving clustering without prior information. Methods like K-means Classification and Iterative Self-Organizing Data Analysis Technique ( ISODATA ) fall under this category 

Semi-Supervised Classification

Semi Supervised Classification combines both labeled data and unlabeled data for training. This method compensates the limitations of both supervised and unsupervised learning methods. By using the labeled and unlabeled data in the feature space, semi-supervised classification enhances the classification accuracy of this method. 

Potential Advancements in Hyperspectral Technology

The evolution of HyperSpectral sensor tech is an ongoing process. Researchers and engineers are continuously working on improving the sensor capabilities, including increases in the spatial and spectral resolutions. These advancements will help in acquiring more detailed and accurate data collection and enhance the effectiveness of the HyperSpectral Remote Sensing. 


In conclusion, HyperSpectral Remote Sensing offers a unique capability to capture a wide range of narrow electromagnetic spectrum data, enabling in-depth analysis of Earth’s surface. The data is then organized into a hypercube, which can be used in information extraction. The evolution in the technology shows promising improvements in data collection and processing, thus enhancing the effectiveness of HyperSpectral Remote Sensing.

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