What is Hyperspectral Imaging? Where it is used?

Last Updated : 14 May, 2024

Spectral imaging integrates two distinct fields: spectroscopy and photography, to capture image data across numerous wavelength bands. Typically, spectral imaging is categorized into multispectral, which involves sampling fewer than 20 wavelength bands, and hyperspectral, which encompasses sampling more than 20 wavelength bands.

In this article, we are going to discuss What Hyperspectral Imaging means and where it is used in detail.

What is Hyperspectral Imaging (HSI images)?

Hyperspectral imaging (HSI) integrates optical spectroscopy with imaging, creating a multidimensional dataset where each pixel contains both spatial and spectral information. This results in a three-dimensional data cube (Hypercube), with spectral bands representing different wavelengths. These bands typically sample more than 20 equally distributed wavelengths, extending beyond the visible range into ultraviolet and infrared regions.

👁 What-is-Hyperspectral-Imaging-Where-it-is-used

Below are the differences between RGB images multispectral images and hyperspectral images:

Property	RGB Images	Multispectral images	Hyperspectral images
Color Representation	These visuals depict primary light colors, red, green, and blue, with various shades created by blending these colors at varying strengths.	These pictures offer a more comprehensive color depiction than RGB photographs by capturing data in specific electromagnetic spectrum wavelength bands.	Photos provide comprehensive spectral information about objects or settings, covering a wide range of wavelengths and channels, enabling accurate analysis.
Spectral Information	RGB images are limited in spectral data, capturing information from three narrow bands (red, green, and blue) in the visible spectrum.	These images provide enhanced spectral data by capturing information from multiple specific bands in the spectrum, including non-visible wavelengths.	These images offer detailed spectral data across various bands, covering a broad range of wavelengths, enabling comprehensive analysis and identification of materials and features.
Number of Bands	These images contain only three bands, corresponding to red, green, and blue.	Multispectral images typically have below 20 bands, focusing on specific sections of the electromagnetic spectrum.	Hyperspectral images have more than 20 bands, often covering the entire electromagnetic spectrum with high resolution.
Common Usage	RGB images are commonly used for standard color imaging, such as photography and visual displays.	Multispectral images are utilized for specific band analysis, especially in applications requiring specific wavelength ranges.	Hyperspectral images enable comprehensive analysis across various wavelengths, aiding in the identification of materials and features.
Applications	RGB images are primarily used in photography, video, and displays.	Multispectral images are used in agriculture, environmental monitoring, and other fields where specific bands analysis can yield valuable insights.	Hyperspectral images are utilized in scientific research, such as agriculture, environmental monitoring, and geological exploration, to provide detailed information for material identification and analysis.

Applications Of Hyperspectral Imaging

Agriculture: HSI aids in crop monitoring, spotting diseases, and gauging plant health. It assists farmers in refining irrigation, fertilization, and pest management practices, leading to higher yields and eco-friendlier farming.
Environmental Monitoring: HSI detects pollutants, surveys vegetation types, and tracks changes in land cover. It's crucial in forestry, wetland mapping, and assessing ecosystem well-being.
Geological Exploration: HSI is vital in mineral prospecting and mapping geological formations. It identifies minerals and their compositions, aiding in resource management.
Food Quality and Safety: HSI is integral in food processing to spot contaminants, assess freshness, and determine nutritional content. It ensures food safety and quality along the supply chain.
Medical Diagnosis: HSI is pivotal in non-invasive medical imaging, notably in spotting skin cancer, evaluating tissue oxygen levels, and tracking wound healing. It provides key insights for diagnosing diseases and guiding treatments.
Forensic Analysis: HSI aids in forensic investigations by detecting and analyzing trace evidence like fingerprints and chemical residues. It's invaluable in criminal investigations and legal proceedings.
Remote Sensing: HSI is crucial in satellite and aerial imaging for environmental monitoring, land use planning, and disaster management. It offers detailed data for assessing vegetation health and land cover changes.
Art Conservation: HSI is indispensable in art authentication, preservation, and restoration by uncovering hidden layers and identifying pigments. It aids conservators in understanding artists' techniques and materials.
Pharmaceuticals: HSI is key in drug discovery, formulation analysis, and quality control in pharmaceutical manufacturing. It accelerates drug screening and aids in identifying impurities.
Security and Defense: HSI is deployed in military and security contexts for target and camouflage detection, as well as surveillance. It provides vital intelligence for reconnaissance and threat detection.

Working of HSI Images

These are the general steps we need to follow:

Acquiring Raw Data Cubes: Start with the raw hyperspectral data cubes, which contain information about each pixel's spectral properties across different wavelengths.
Data Preparation and Quality Assessment: Clean up the data cubes by removing any unwanted noise or distortions that may affect the accuracy of our analysis. This ensures that the data is reliable and ready for further examination. Assess the quality of the images to ensure they meet the necessary standards for analysis. This involves checking factors like resolution, clarity, and overall image fidelity.
Smoothing Spectral Data: Smooth out the spectral data using Savitzky-Golay filtering techniques. This helps to reduce noise and enhance the clarity of spectral features, making it easier to identify patterns and anomalies in the data.
Assigning Labels and Categories: Assign labels or categories to specific areas of interest within the data cubes. This step provides context to the analysis by identifying regions with distinct spectral signatures, such as different land cover types or material compositions.
Selecting Relevant Spectral Bands: Choose the most relevant spectral bands from the data cubes based on the analysis objectives. By selecting the appropriate bands, we can focus on specific features or properties of interest while reducing the complexity of the data.
Performing Spectral Classification: Use classification techniques to categorize pixels within the hyperspectral data cubes based on their spectral signatures. This can involve machine learning algorithms or spectral libraries to classify pixels into different classes or categories, such as land cover types or material compositions.

This is how we analyze Hyperspectral image data.

Advantages and Disadvantages of Hyperspectral Images

Advantages	Disadvantages
HSI captures hundreds to thousands of narrow, continuous spectral bands.	HSI systems are often more expensive than traditional imaging systems, both in terms of equipment and operational costs.
HSI provides a detailed spectral signature of materials, enabling highly precise discrimination between substances that look similar in traditional imaging.	The high dimensionality of hyperspectral data requires specialized software, substantial computing power, and expertise in analysis techniques.
HSI can be used in wide range of applications such as agriculture, mineralogy, food industry etc...	HSI produces vast amounts of data, posing challenges in storage, management, and processing.
The in-depth spectral analysis allows for the identification and mapping of specific chemical components within a sample or environment.	HSI data can be susceptible to noise and interference, requiring careful calibration and potential noise-reduction techniques.
HSI is a non-invasive analysis method making it suitable for sensitive samples across diverse fields.	HSI systems have a smaller field of view compared to traditional cameras, making them less suitable for some large-scale imaging applications.