Optics 101

Hyperspectral vs Multispectral Imaging: Advantages and Disadvantages

Imaging technologies have evolved significantly, offering powerful data collection and analysis tools. Among these technologies, hyperspectral and multispectral imaging stand out for their ability to capture detailed information across the electromagnetic spectrum. By surpassing traditional red, green, and blue wavelengths, these techniques offer an abundance of information for diverse use cases.

Hyperspectral vs Multispectral Imaging: Uses, Advantages, and Disadvantages

Image Credit: Francesco Scatena/Shutterstock.com

This article explores both techniques, discussing their principles, advantages and disadvantages, recent advancements in the field, and potential to revolutionize various scientific pursuits.    

Seeing the Unseen: A Technological Evolution

The genesis of present-day hyperspectral and multispectral imaging technologies can be traced back to the early 20th century when spectrophotometers were first invented. However, significant advancements in sensor technology and data processing have propelled the recent surge in their usage. Hyperspectral imaging, which captures hundreds of narrow spectral bands, emerged in the 1980s. In contrast, multispectral imaging captures a smaller and broader range of bands and has been in use for longer.1

Principles: Decoding the Spectral Language

Both hyperspectral and multispectral imaging rely on the fundamental principle that different materials interact with light in unique ways. Electromagnetic radiation encompasses a wide range of wavelengths, from gamma rays to radio waves, with only a small portion visible to the human eye. However, every material has a distinct spectral signature that reveals how it reflects and absorbs light across various wavelengths.

Hyperspectral and multispectral imaging techniques exploit this principle to identify and differentiate materials. These techniques use sensors to capture light reflected from a scene or object. The key distinction lies in how they capture this spectral information.

Hyperspectral imaging captures a detailed electromagnetic spectrum map by taking hundreds to thousands of narrow, contiguous bands. This provides a highly specific spectral signature for each pixel in the image, like a unique fingerprint for every tiny element in the scene.2

On the other hand, multispectral imaging captures a smaller number of broader spectral bands (usually 3 to 10) that may not be contiguous. Users can define these bands based on the intended application. This approach provides a more generalized overview of the spectral properties of a scene, like a simplified color palette representing the dominant spectral characteristics of different regions in the image.3

Applications: Seeing a Hidden World

The ability to decode the spectral language of the world unlocks a vast array of applications for both hyperspectral and multispectral imaging.

Hyperspectral Imaging: Unveiling the Details

Hyperspectral imaging, with its incredibly detailed spectral information, excels in tasks that require highly specific material identification. The vast number of narrow spectral bands allows for the detection of subtle spectral variations between materials. For instance, it can be used in mineral exploration to identify specific minerals based on their unique spectral signatures. This information can be used to map mineral deposits, identify alteration zones associated with mineralization, and even differentiate between different mineral grades.4

In precision agriculture, hyperspectral data can be used to assess crop health, detect nutrient deficiencies, and monitor water stress at a very granular level. By analyzing the spectral variations across a field, farmers can identify areas that require specific interventions, optimizing resource use and improving crop yields.5

Hyperspectral imaging also plays a vital role in environmental monitoring. It can be used to detect and map pollutants in air, water, and soil. Hyperspectral data can be used to identify oil spills, track the spread of invasive species, and monitor changes in vegetation cover, providing valuable insights for environmental management.6

Hyperspectral imaging also holds great promise for non-invasive medical diagnostics. Researchers are exploring its use in cancer detection, where subtle spectral variations between healthy and cancerous tissues can be identified. Additionally, hyperspectral imaging can be used to assess wound healing progress and characterize different types of tissue, potentially aiding in early disease detection and treatment planning.7

Multispectral Imaging: A Practical Approach

Multispectral imaging offers a more practical approach for applications where detailed spectral information is not crucial. Its simpler data sets provide a faster, more cost-effective solution for many tasks. In environment monitoring, multispectral imaging can detect changes in land cover, deforestation, and natural disasters. By analyzing changes in spectral reflectance patterns over time, it can identify deforested areas, track wildfires, and assess the extent of flood damage.8

Multispectral data is also valuable in creating detailed land-use maps, as different types of vegetation, urban areas, and agricultural land have distinct spectral signatures, allowing for accurate mapping for various management and planning purposes.9

Additionally, multispectral imaging can be used in machine vision for robotics, training machines to identify and differentiate objects based on their spectral characteristics. This has applications in agriculture, where robots can sort fruits and vegetables based on ripeness or identify weeds for targeted treatment.

In manufacturing, robots can leverage multispectral imaging for quality control or product sorting tasks. Autonomous vehicles can also use multispectral imaging for scene understanding and obstacle detection.10

Navigating Positives and Negatives

Both hyperspectral and multispectral imaging techniques have their unique advantages and disadvantages. Hyperspectral imaging boasts an unmatched spectral resolution, providing highly specific material identification, which is beneficial in a wide range of fields.

Additionally, hyperspectral imaging yields rich data for complex analysis, enabling the detection of subtle spectral variations. However, this technique generates a massive amount of data, requiring complex processing and specialized expertise for analysis. Hyperspectral cameras are also generally expensive and bulky, limiting their widespread use. Furthermore, interpreting hyperspectral data accurately requires advanced processing techniques and skilled personnel.11

On the other hand, multispectral imaging is a more cost-effective and compact option, making it readily available. It offers faster acquisition and processing times, thanks to its simpler data sets, and requires less sophisticated data processing techniques than hyperspectral imaging. However, multispectral imaging sacrifices some spectral detail for faster acquisition and processing, making it less suitable for tasks that require highly specific material identification. Additionally, with fewer spectral bands, there is a higher risk of misclassifying materials with similar spectral signatures.11

Choosing the Right Technique

When deciding between hyperspectral and multispectral imaging techniques, there are a few key factors to consider. The level of spectral detail required is one of the most important considerations. If the application requires highly specific material identification and the ability to detect subtle spectral variations, hyperspectral imaging is the better choice. However, if a more general overview of the spectral properties is sufficient, multispectral imaging may be adequate.

Another factor to consider is data processing capabilities. Hyperspectral data requires advanced processing techniques and skilled personnel for analysis. So, if these resources are not readily available, multispectral imaging might be a more practical option.

Cost and availability are also important. Hyperspectral cameras are expensive and bulky, while multispectral cameras are more affordable and readily available. So, budgetary constraints may influence the choice of technology.

Overall, it is important to carefully consider the specific needs of the application and the available resources before choosing between hyperspectral and multispectral imaging techniques.

The Future is Spectral: Recent Advancements

Recent studies have focused on addressing the challenges associated with hyperspectral and multispectral imaging technologies. Researchers are working hard to develop miniaturized hyperspectral cameras, which will make the technology more portable and accessible for field applications, opening up a wider range of possible uses.12

Developments in machine learning and artificial intelligence are also aiding the creation of more efficient algorithms for hyperspectral data processing, leading to significant reductions in processing times and improvements in the accuracy of spectral analysis.13

Advancements in sensor technology are leading to the development of hyperspectral cameras with higher spectral resolution and improved signal-to-noise ratio, enabling detailed material characterization.

These developments hold promise for the future of hyperspectral and multispectral imaging.12

The Future of Hyperspectral and Multispectral Imaging

Hyperspectral and multispectral imaging are rapidly evolving, providing exciting opportunities across various fields. With the improvement in sensor technology, more efficient data processing, and decreasing costs, wider adoption of these techniques can be expected. The future holds the potential to surpass the limitations of human vision, unlocking a deeper understanding of the planet and its inhabitants.

Hyperspectral and multispectral imaging offer a powerful window into the invisible world. Choosing the correct technique depends on the specific application and the desired level of spectral detail. Hyperspectral imaging provides unmatched precision for tasks requiring in-depth material characterization, whereas multispectral imaging offers a more practical and cost-effective approach for broader applications.

As these technologies continue to mature, with advancements in miniaturization, data processing, and standardization, they hold immense promise for revolutionizing various fields, from environmental monitoring and precision agriculture to medical diagnostics and industrial process control.

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References and Further Reading

  1. Calin, MA., Calin, AC., Nicolae, DN. (2020). Application of airborne and spaceborne hyperspectral imaging techniques for atmospheric research: past, present, and future. Applied Spectroscopy Reviews. doi.org/10.1080/05704928.2020.1774381
  2. Boldrini, B., Kessler, W., Rebner, K., Kessler, R. (2012). Hyperspectral imaging: a review of best practice, performance and pitfalls for inline and online applications. Journal of Near Infrared Spectroscopydoi.org/10.1255/jnirs.1003
  3. Ma, F., Yuan, M., Kozak, I. (2023). Multispectral Imaging (MSI): Review of Current Applications. Survey of Ophthalmology. doi.org/10.1016/j.survophthal.2023.06.004
  4. Peyghambari, S., Zhang, Y. (2021). Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review. Journal of Applied Remote Sensingdoi.org/10.1117/1.jrs.15.031501
  5. Sethy, PK., et al. Hyperspectral imagery applications for precision agriculture - a systemic survey. Multimed Tools Appl. doi.org/10.1007/s11042-021-11729-8
  6. Stuart, MB., et al. (2019). Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems. Sensors. doi.org/10.3390/s19143071
  7. Zhang, Y., Wu, X., He, L., Meng, C., Du, S., Bao, J., Zheng, Y. (2020). Applications of hyperspectral imaging in the detection and diagnosis of solid tumors. Translational Cancer Researchdoi.org/10.21037/tcr.2019.12.53
  8. Panuju, DR., Paull, DJ., Griffin, AL. (2020). Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics. Remote Sensingdoi.org/10.3390/rs12111781
  9. Chaves, MED., et al. (2020). Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sensingdoi.org/10.3390/rs12183062
  10. Xiao, Z., Wang, J., Han, L., Guo, S., Cui, Q. (2022). Application of Machine Vision System in Food Detection. Frontiers in Nutritiondoi.org/10.3389/fnut.2022.888245
  11. Ortega, S., Halicek, M., Fabelo, H., Callico, GM., Fei, B. (2020). Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review. Biomedical Optics Express. doi.org/10.1364/boe.386338
  12. Tran, MH., Fei, B. (2023). Compact and ultracompact spectral imagers: technology and applications in biomedical imaging. Journal of Biomedical Opticsdoi.org/10.1117/1.jbo.28.4.040901
  13. Wieme, J., Mollazade, K., Malounas, I., Zude-Sasse, M., Zhao, M., Gowen, A., Argyropoulos, D., Fountas, S., Van Beek, J. (2022). Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review. Biosystems Engineering. doi.org/10.1016/j.biosystemseng.2022.07.013

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Ankit Singh

Written by

Ankit Singh

Ankit is a research scholar based in Mumbai, India, specializing in neuronal membrane biophysics. He holds a Bachelor of Science degree in Chemistry and has a keen interest in building scientific instruments. He is also passionate about content writing and can adeptly convey complex concepts. Outside of academia, Ankit enjoys sports, reading books, and exploring documentaries, and has a particular interest in credit cards and finance. He also finds relaxation and inspiration in music, especially songs and ghazals.


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