Inside Hyperspectral Imaging:
The Science of Seeing Everything
More than 10 years of hyperspectral vision experience.
What is hyperspectral imaging?
Seeing What Matters:
Turning Invisible Light into Actionable Data
While traditional cameras – and even the human eye – are limited to a narrow sliver of the electromagnetic spectrum (roughly 380 to 750 nanometers), the world doesn’t end at what we can see.
The full spectrum stretches far beyond, into ultraviolet, infrared, and beyond. Each region offes unique insights into the physical and chemical makeup of materials.
Most imaging systems stop at visible light (RGB), but hyperspectral cameras unlock access to these “invisible” wavelengths, capturing hundreds of spectral bands across the infrared and shortwave infrared ranges.
This deeper view enables detection of subtle spectral signatures often tied to material composition, contamination, moisture levels, or biological states,providing critical data where standard imaging fails. Hyperspectral imaging, isn’t just about seeing more – it’s about knowing more, faster, and with greater accuracy, opening doors to process optimization, product differentiation, and entirely new applications.
Hyperspectral vs Multispectral
Not All Vision is Equal
Hyperspectral vs. Multispectral and RGB Explained
When it comes to imaging, not all technologies see the world the same way.
The difference lies in how much of the electromagnetic spectrum they can capture – and how much valuable information they can reveal.
We can group imaging technologies by the number of spectral bands (or “colors”) they use:
- Monochrome Imaging: Uses just one spectral band—essentially grayscale. While it can detect light in the visible or near-infrared ranges, it captures limited information.
- RGB Imaging (Red, Green, Blue): The standard in most cameras, it captures three spectral bands in the visible spectrum – what the human eye sees. Great for visual detail, but blind to what’s happening outside visible light.
- Multispectral Imaging: Goes a step further with more than three spectral bands. These can be in both the visible and infrared regions, giving more insight—such as differentiating healthy from stressed vegetation.
- Hyperspectral Imaging: Offers the most detailed view, capturing dozens to hundreds of narrow, continuous spectral bands. This allows it to see subtle differences in material properties across both visible and infrared light (especially NIR and SWIR).
But what makes hyperspectral truly powerful? Unlike multispectral, which captures select bands, hyperspectral imaging collects a full, uninterrupted spectrum for each pixel. This results in a “data cube” rich with spectral information – essentially turning every image into a detailed chemical map.Why It Matters for You? These “invisible colors” can reveal critical features that normal cameras miss. In short, hyperspectral imaging provides a deeper, data-rich view that translates into smarter decisions, better quality control, and competitive advantage.
Hyperspectral imaging techniques
From Light to Insight:
Exploring Hyperspectral Imaging Techniques
Whisk broom
What it is: Captures one pixel at a time by moving a spectrometer across the scene.
How it works: Either the camera or the object must move in a very controlled way to “paint” the image one pixel at a time.
Use case: Suitable for very high – precision lab setups but slow and not ideal for dynamic environments.
Strategic note: Excellent for research, but not practical for production or industrial use due to speed constraints.
Push broom
What it is: Captures one line of pixels at a time, using a high-speed hyperspectral line sensor.
How it works: The object or sensor moves steadily, line by line, building a full image as it scans.
Use case: Ideal for conveyor systems, aerial surveys, or any controlled-motion environment.
Strategic note: A proven method for industrial, agricultural, and remote sensing applications – a powerful balance of speed, resolution, and scalability.
Learn more about our Push broom hyperspectral solution here
Spectral scan
What it is: Captures the entire object or scene – but only one wavelength at a time.
How it works: A filter switches between wavelengths, building the spectral image over time.
Use case: Great for static objects in lab conditions where movement isn’t an issue.
Strategic note: Not suitable for moving targets or time – sensitive tasks; high-resolution but slow.
Snapshot
What it is: Captures all wavelengths and the full image in a single instant.
How it works: Uses specialized sensor arrays to take a complete hyperspectral picture in one shot.
Use case: Effective for fast-moving objects or real-time analysis, like biomedical imaging.
Strategic note: Very fast but often limited in spatial or spectral resolution – currently more niche and expensive.
The hyperspectral datacube
Inside the Cube:
Understandstanding the Power of Hyperspectral Insight
What is a hyperspectral data cube?
A hyperspectral data cube is the core output of a hyperspectral imaging system—and it’s far more than just a picture.
Think of it as a 3D block of data:
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The X and Y axes represent the spatial dimensions of the scene – just like a regular image (height and width).
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The Z axis contains hundreds of layers, each representing a different wavelength (or spectral band) of light, captured from the same scene.
In other words, every pixel in the image doesn’t just hold color – it holds a full light spectrum. That spectrum is like a fingerprint, revealing detailed chemical or material properties.
Qtec data cube file formats
PAM (Portable Arbitrary Map)
Simple, ASCII+RAW format with BIP/BSQ interleave
8- or 16-bit integer only
No standard open-source viewer
HV tools support all interleave types (BIP, BIL, BSQ)
ENVI
Widely used in hyperspectral imaging
ASCII
.hdr+ RAW data (.raw,.dat, etc.)Supports all interleave types
Highly configurable header fields
Common in research and satellite imaging
File extension may need adjustment for some viewers
TIFF
Multipage format, BSQ interleave
Supports many data types (int/float, up to 64-bit)
Can be compressed
Compatible with tools like ImageJ
