What is Hyperspectral Imaging? Where it is used?

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.

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.

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

1. 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.
2. 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.
3. Geological Exploration: HSI is vital in mineral prospecting and mapping geological formations. It identifies minerals and their compositions, aiding in resource management.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. Pharmaceuticals: HSI is key in drug discovery, formulation analysis, and quality control in pharmaceutical manufacturing. It accelerates drug screening and aids in identifying impurities.
10. 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.

These are the general steps we need to follow:

1. Acquiring Raw Data Cubes: Start with the raw hyperspectral data cubes, which contain information about each pixel’s spectral properties across different wavelengths.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.

How does hyperspectral imaging differ from other imaging techniques?

Hyperspectral imaging is like having super-powered vision. Normal cameras see the world in just red, green, and blue. Hyperspectral cameras go way beyond, seeing hundreds of colors, even the ones our eyes can’t detect. This lets them pick out the tiny differences in how things reflect light, revealing hidden details about materials, plants, or even hidden objects.

What are the main challenges associated with hyperspectral imaging?

Hyperspectral imaging is hampered by its high cost, the need for specialized expertise in analysis, the challenges of handling massive datasets, and the influence of environmental factors on image quality.

In which industries or fields is hyperspectral imaging most commonly used?

Hyperspectral imaging finds applications across various industries and fields including agriculture, environmental monitoring, medical diagnostics, geological exploration, food quality control, forensic analysis, remote sensing, art conservation, pharmaceuticals, and security and defense