WHAT IS HYPERSPECTRAL IMAGING: A COMPREHENSIVE GUIDE

Hyperspectral imaging is a powerful technology combining spectroscopy with imaging capability. It enables gathering detailed information about the composition and characteristics of objects and surfaces in a way that is impossible with conventional imaging systems.

Thanks to its noninvasive, and nondestructive capability in identifying and quantifying material, hyperspectral imaging has become increasingly popular in various industries and research applications.

Hyperspectral imaging is a technique that collects and processes information across the electromagnetic spectrum to obtain the spectrum for each pixel in an image. This allows for the identification of objects and materials by analyzing their unique spectral signatures. Applications of hyperspectral imaging include food quality & safety, waste sorting and recycling, and control and monitoring in pharmaceutical production.

The electromagnetic spectrum describes all types of light, ranging from very long radio waves, through microwaves, infrared radiation, visible light, ultraviolet rays, and X-rays, to very short gamma rays — most of which the human eye can’t see (Figure 1).

Spectral imaging is imaging that uses multiple bands across the electromagnetic spectrum. While the RGB camera uses three visible light bands (red, green, and blue) to create images, hyperspectral imagery makes it possible to examine how objects interact with many more bands, ranging from 250 nm to 15,000 nm and thermal infrared. The study of light–matter interaction is called spectroscopy or spectral sensing. To learn more, read our article How does spectral sensing work? Understanding the basics of spectroscopy and spectral sensors.

Figure 1. Hyperspectral imaging captures wavelengths from 250 nm to 15,000 nm and thermal infrared.

Spectral imaging systems refer to a class of imaging technology that captures and processes information about the wavelength of light within an image. These systems are designed to capture multiple bands or channels of information across the electromagnetic spectrum beyond the visible light that our eyes can see. This data can then be processed to generate a color-coded representation of the spectral data, which can provide information about the chemical and physical properties of the objects within the image.

Hyperspectral imaging involves using an imaging spectrometer, also called a hyperspectral camera, to collect spectral information.

A hyperspectral camera captures a scene’s light, separated into its individual wavelengths or spectral bands. It provides a two-dimensional image of a scene while simultaneously recording the spectral information of each pixel in the image.

The result is a hyperspectral image, where each pixel represents a unique spectrum. This unique spectrum can be compared to fingerprints. Since every material and compound reacts with light differently, their spectral signatures are also different. Just like fingerprints can be used to identify a person, the spectra can identify and quantify the materials in the scene.

Hyperspectral imaging system analyzes a spectral response to detect and classify features or objects in images based on their unique spectra.

By combining the benefits of digital imaging and a spectrometer, hyperspectral imaging provides both spatial and spectral information about the object’s physical and chemical properties. The spectral information allows for the identification and classification of materials and the spatial provides data on the material’s distribution and areal separation. Hyperspectral imaging provides answers to questions concerning “what” (based on the spectrum), “where” (based on location), and “when”.

Figure 2: To match human vision, a digital photograph of a leaf (top) is created using three bands: red, green, and blue. The RGB data is comparable to a three-page pamphlet. In contrast, a hyperspectral image of a leaf (bottom) captures a spectral response from 220 wavelengths. The comparable 220-page book contains much more detailed information about the object.

One of the key benefits of hyperspectral imaging is its high spatial and spectral resolution which enables the detailed characterization of the materials.

A hyperspectral camera measures thousands or hundreds of thousands of spectra to create a massive hyperspectral data cube comprising position, wavelength, and time-related information.

Compared to multispectral imaging, hyperspectral imaging provides more information allowing for more accurate analysis, identification, and separation of materials and substances (To learn more, read our article Hyperspectral vs. Multispectral cameras).

Hyperspectral imaging lets us differentiate between materials with similar physical or visual characteristics or what the human eye cannot see, such as different minerals.

Hyperspectral imaging is an increasingly used technique in industry, research, and remote sensing.

The data provided by hyperspectral imaging systems can be used during inspection to locate, sort, or quantify the concentration of various materials that are invisible to common cameras or the human eye. For instance, a hyperspectral imaging system integrated into an in-line quality control system enables the identification of foreign objects, contaminants, and the amount of fat, sugar, or moisture in products.

Hyperspectral imagery acquired through remote sensing provides information about surfaces on the Earth, such as minerals or vegetation for example.

Hyperspectral imaging is used for various applications, including:

1. Environmental monitoring: Monitoring changes in land use, vegetation health, and water quality over time. This allows for detecting early signs of ecological degradation and tracking the effectiveness of conservation efforts.
2. Mineral exploration: mapping mineral deposits, detecting the mineral composition and grade.
3. Quality control: Non-destructive inspecting and grading of food products, detecting contaminants and defects in industrial products non-destructively.
4. Waste management: Separating a broad range of materials reliably and with high purity. This information can be used to automate recycling processes and to increase the value of recycled material.
5. Agriculture: Assessing the health and yield of crops, monitoring the soil moisture and nutrient content to optimize crop management practices and improve crop yields. Agricultural monitoring is usually done with a drone-mounted hyperspectral camera.
6. Military surveillance: detecting and identifying hazardous materials.

These are just a few examples of how hyperspectral imaging can provide valuable information for various applications.

In conclusion, hyperspectral imaging is both a prominent tool for research and highly useful machine vision technology for various industries to improve processes, increase quality, and reduce waste.