Hyperspectral Academy

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1. Introduction to Hyperspectral Imaging

1.1. What is Hyperspectral Imaging?

Every pixel in a hyperspectral image contains a detailed spectral signature, akin to a chemical fingerprint of the material below. But to truly understand what makes hyperspectral imaging so powerful, we need to look at how it captures data differently from conventional sensors.

Hyperspectral imaging (HSI) is an advanced remote sensing technique allowing us to see the world in greater detail than most conventional satellite sensors can perceive. Fundamentally, HSI captures image data across hundreds of narrow, contiguous spectral bands, spanning the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions of the electromagnetic spectrum—typically ranging from 400 to 2500 nanometers.

While most commercial space-borne HSI systems, including Pixxel’s, focus on the VNIR and SWIR regions, hyperspectral sensors can extend into the thermal infrared (TIR) range in specialised airborne or lab-based setups, enabling applications in heat detection, gas analysis, and thermal emissivity studies.

Electromagnetic spectrum

Unlike traditional imaging systems that capture red, green, and blue (RGB) or multispectral systems that capture 4 to 10 broader bands, hyperspectral sensors collect data in narrow spectral intervals—between 1 and 10 nanometers. This enables HSI to detect subtle differences in how materials reflect light, offering extraordinary insight into their chemical and physical properties.

Multispectral vs hyperspectral imaging
While multispectral systems might tell you “this is vegetation,” hyperspectral systems can say “this is nutrient-deficient maize under heat stress.”

1.1.1. Hyperspectral vs. Multispectral vs. RGB

To understand what sets HSI apart, it helps to compare it with more familiar remote sensing systems like multispectral and RGB imaging. Each type captures Earth’s surface in a different way. The difference comes down to how many spectral bands are collected, how narrow those bands are, and what level of detail they reveal about the materials within each pixel.

Remote sensing systems vary in the number of spectral bands they capture and the level of detail they reveal about Earth’s surface. RGB imaging is the most basic form, capturing three broad bands (Red, Green, and Blue) that align with human vision. Multispectral imaging (MSI) improves upon this by capturing reflectance in several discrete bands—typically 4 to 10—extending into the near-infrared (NIR) spectrum. HSI, however, significantly advances spectral analysis by capturing dozens to hundreds of contiguous narrow bands across the visible, NIR, and shortwave infrared (SWIR) regions, unlocking highly detailed spectral fingerprints for every pixel.

RGB vs Multispectral vs Hyperspectral
This progression—from RGB to MSI to HSI—represents a shift from visual inspection to precision material analysis, enabling applications ranging from environmental monitoring to mineral exploration and precision agriculture.
Feature RGB Imaging Multispectral Imaging Hyperspectral Imaging
Number of bands 3 broad bands (Red, Green, and Blue) 4–12 discrete bands; up to 36 in advanced systems (USGS) 37–300+ narrow, contiguous spectral bands
Bandwidth (FWHM) ~50-70 nm ~15-35 nm <10 nm
Spectral coverage Visible light Visible and Near-Infrared (VNIR) Visible, NIR, and Shortwave Infrared (SWIR)
Wavelength range R: 635 nm-700 nm G: 520 nm-560 nm B: 450 nm-500 nm ~400–1000 nm ~400–2500 nm
Spectral resolution Low Moderate High
Pixel spectral count 3 discrete values (RGB) Non-continuous sample spectrum Continuous spectrum across wavelengths
Data output True colour image Basic spectral reflectance Detailed spectral fingerprint
Sensor architecture Broadband filters aligned with human vision Filters targeting specific bands Imaging spectrometers or diffraction-based systems
Processing complexity Low Moderate High - requires advanced processing
AI/ML compatibility Limited - mostly for visualisation Useful for classification models Highly compatible; enables spectral modelling and predictive analytics
Cost & accessibility Very low Relatively affordable and widely available Previously limited by high costs and access.
Strengths Easy to interpret; Minimal processing needed Balanced spectral coverage; Lightweight datasets for frequent imaging Spectral fingerprinting; Sub-pixel detection; Enables early-stage anomaly detection
Limitations Lacks analytical depth; Cannot distinguish between spectrally similar objects Broad bands average reflectance; Misses subtle spectral differences Large, complex datasets; Requires high storage, processing power, and expertise
Typical use cases Photography; Basic visual mapping Vegetation monitoring; Land-use/land-cover classification; Urban sprawl analysis Crop disease detection; Mineral identification; Water quality and pollution tracking; Environmental change detection

1.2. The Physics Behind Hyperspectral Imaging

Hyperspectral imaging is based on the physics of electromagnetic reflectance, i.e., the way materials absorb and reflect specific wavelengths of light. When sunlight interacts with the Earth’s surface, each object reflects a unique pattern of light across the electromagnetic spectrum.

Every pixel in a hyperspectral image contains rich spectral detail, but to truly understand what makes hyperspectral data so powerful, it's essential to grasp a few key concepts in spectrometry.

1.2.1. The Electromagnetic Spectrum, Bands, and Spectral Resolution

Hyperspectral sensors collect data across a continuous portion of the electromagnetic spectrum (EMS), typically spanning the Visible and Near Infrared (VNIR), Shortwave Infrared, and in some cases, the Thermal Infrared (TIR).

Each pixel in a hyperspectral image contains a reflectance spectrum: a detailed curve showing how that material interacts with light across the EMS, which can be analysed to determine material composition. To understand the richness of this information, it’s important to know how bands, spectral resolution, and bandwidth shape the data.

Feature Description
Spectral Band A specific range of wavelengths captured by an imaging spectrometer. For example, wavelengths between 695 nm and 700 nm may form a single band. Spectrometers collect reflected light within that range and record it as a single value per pixel. Band information is often reported using the centre wavelength, the midpoint of the wavelength range (e.g., a 695–700 nm band has a centre wavelength of 697.5 nm).
Spectral Resolution The narrowness of each band captured by a sensor. For example, if a sensor collects light from 695 nm to 700 nm in a single band, its spectral resolution is 5 nm. Higher spectral resolution, i.e., narrower bands, enables the detection of subtle material differences that broadband sensors might miss.
Full Width Half Maximum (FWHM) Measures the effective width of a band around its centre wavelength—specifically, the width of the band at half its maximum response. While spectral resolution gives the total range, FWHM shows the sharpness of the band response and may vary between bands on the same sensor.

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Together, spectral band positioning, resolution, and FWHM determine how well a hyperspectral sensor can detect fine variations in reflectance, essential for analysing the Earth’s surface.

Not all hyperspectral sensors are designed with equally spaced or uniformly wide bands. Some systems, such as Pixxel’s Firefly constellation, are optimised with variable bandwidths to enhance detection of specific features in different parts of the spectrum.

Default band combination (3A) of Pixxel’s Firefly constellation.
HSI is also called an imaging spectrometry technique, merging two capabilities: the spatial resolution of imaging and the compositional insight of spectroscopy. This dual nature means hyperspectral data answers both "Where is something?" and "What is it made of?"

1.2.2. VNIR, SWIR, and TIR regions

Hyperspectral sensors often span one or more of the following spectral regions, each revealing unique information:

Spectral Region Wavelength Range (nm) Key Applications Key Absorption Features
VNIR 400–1000 Vegetation health, water bodies Chlorophyll, carotenoids, water
SWIR 1000–2500 Mineral mapping, soil moisture, agriculture OH, CO₃²⁻, SO₄²⁻, clay minerals
TIR 8000–14000 Thermal mapping, industrial monitoring Emissivity, surface temperature

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Each of these regions reveals different physical and chemical properties of Earth's surface materials. For example, while VNIR is responsive to surface reflectance from vegetation and water, SWIR provides deeper compositional insight—such as whether a crop is water-stressed or if a rock contains clay minerals. TIR, though less common in hyperspectral imaging due to detector limitations, is valuable for studying temperature gradients and emissivity differences.

Advanced hyperspectral sensors can capture combinations of these regions in a single system, enabling multi-domain insights from vegetation health to thermal anomalies.

1.3. Components of a Hyperspectral System

1.3.1. Sensors and Platforms

Hyperspectral imaging systems can be deployed on different platforms, selected based on the target application, spatial scale, and desired resolution:

  • Spaceborne platforms including NASA’s EO-1 Hyperion and Pixxel’s Firefly satellites are ideal for large-scale area monitoring. Pixxel’s constellation, for instance, delivers a 5-metre ground sampling distance (GSD), with a swath of 40 km, and fine spectral resolution, supporting monitoring at commercially viable scales.
  • Airborne sensors such as AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) and HYDICE (Hyperspectral Digital Imagery Collection Experiment) are used for regional, high-resolution studies. These sensors are typically flown on aircraft or drones for more targeted missions.
  • UAV-mounted and handheld systems are popular for local-scale studies, offering flexibility and frequent deployment for agricultural monitoring, environmental assessments, and lab-based applications.
One of the defining differences between hyperspectral and other imaging technologies is its ability to go beyond appearance. Traditional cameras or even multispectral sensors might be able to tell you that a region is green and likely covered in vegetation, whereas hyperspectral sensors can reveal vegetation type, health and stress levels, moisture content, or the presence of disease or nutrient deficiency.

1.3.2. Data acquisition basics

Most hyperspectral systems operate as passive sensors, relying on sunlight reflected from Earth’s surface to capture information. This contrasts with active systems like LiDAR or Synthetic Aperture Radar (SAR), which emit their own energy and measure how it reflects back.

Active vs passive sensors

Because passive hyperspectral sensors depend on natural light, several factors influence data quality:

  • Cloud cover,
  • Time of day,
  • Atmospheric conditions

As a result, careful acquisition planning and atmospheric correction are essential to ensure usable data.

Finally, like all remote sensing data, hyperspectral imagery must be preprocessed to correct for artefacts, calibration errors, and atmospheric distortions. Once refined, hyperspectral imagery becomes a powerful layer of intelligence, revealing spectral properties of the Earth’s surface.

1.4 Applications of Hyperspectral Imaging

HSI fundamentally changes how we observe, classify, and understand the Earth’s surface. Unlike conventional imaging systems that capture limited spectral bands, HSI captures hundreds of narrow, contiguous spectral bands across the electromagnetic spectrum, forming a rich three-dimensional data structure known as a hypercube. The key strength of this technology lies in its ability to acquire a complete spectral signature for every pixel in an image, enabling precise material identification and sub-pixel analysis.

Each pixel in a hyperspectral image contains a continuous reflectance spectrum, which can be used to characterise the physical, biological, or chemical properties of surface materials. This allows analysts to move beyond detecting “what is visible” to inferring “what it’s made of”—a critical distinction in applications where fine compositional details matter.

1.4.1. The Advantages of Hyperspectral Imaging

Feature Description
Pixel-Level Spectral Profiling Each pixel contains a full spectral signature, enabling identification of individual materials or substances, even when multiple targets are mixed within a single pixel.
Subtle Change Detection Sensitive to minute spectral differences, HSI can detect early biochemical, geophysical, or structural changes over time—critical for monitoring degradation, contamination, or stress responses.
Material-Level Classification Capable of distinguishing materials with similar visual properties (e.g., malachite vs. chrysocolla, or healthy vs. pathogen-stressed crops) by analysing spectral absorption features.
Greater Spectral Dimensionality Compared to high spatial resolution satellites, HSI provides higher spectral resolution, enabling detailed analysis of rocks, soils, paints, vegetation, or synthetic materials.
Broad Application Versatility Supports use cases ranging from land-use classification and pollution monitoring to mineral exploration and forest disease tracking through its ability to extract both structural and biochemical information.
Large-Scale Monitoring Enables scalable, global coverage of environmental systems with reduced dependency on in-situ data collection.
Temporal Analysis Ideal for tracking changes in spectral behaviour across short intervals—essential for dynamic environments such as agriculture, water bodies, or urban-industrial zones.

1.4.2. Unique Insights Enabled by Hyperspectral Data

  • Material Decomposition at Pixel Scale: Using spectral, spatial, and character domain cues, HSI can distinguish and unmix materials that occupy a single pixel.
  • Biogeophysical Parameter Retrieval: Infers properties such as chlorophyll content, leaf water content, or mineralogical composition with high accuracy, even in heterogeneous landscapes.
  • Early Detection Capability: Detects early-stage anomalies like crop stress, pathogenic infection, or chemical contamination, allowing proactive interventions.
  • Advanced Classification: Enables land cover and land use classification with a finer granularity, extending down to functional traits of vegetation or chemical states of soils and pollutants.

1.4.3. Considerations in Hyperspectral Imaging

While HSI offers extraordinary spectral insight, it also generates large data volumes and requires advanced processing techniques. Due to the high dimensionality of hyperspectral data, traditional multispectral analysis workflows are often insufficient. Specialised approaches, such as dimensionality reduction (e.g., PCA, MNF), sub-pixel unmixing, and spectral feature extraction—are essential to extract meaningful thematic layers from HSI datasets.

1.5. Summary

  • Hyperspectral Imaging (HSI) captures hundreds of narrow, contiguous spectral bands across visible, near-infrared, shortwave infrared, and sometimes thermal regions, providing detailed spectral signatures for every pixel.
  • HSI is based on the physics of electromagnetic reflectance, allowing identification of materials by how they absorb and reflect light.
  • Key sensor characteristics include spectral bands, spectral resolution, and Full Width Half Maximum (FWHM), which determine the sensor’s ability to detect subtle material differences.
  • Hyperspectral sensors are deployed on various platforms:
    • Spaceborne satellites (e.g., Pixxel’s Firefly constellation) for large-scale monitoring
    • Airborne systems for regional, high-resolution data
    • UAV and handheld devices for local-scale, flexible studies
  • HSI enables precise material identification and classification, revealing details not visible to traditional or multispectral imaging.
  • Common applications include agriculture, mineral exploration, water quality monitoring, environmental assessments, urban planning, and defence.
  • Due to the high volume and dimensionality of hyperspectral data, specialised preprocessing and analysis methods are essential to extract meaningful insights.
  • Mastering these fundamentals provides a solid foundation for understanding the broad range of HSI applications explored in the following chapters.