Hyperspectral Imaging Is Transforming Science, Medicine, and Industry

Expanding the Spectrum Into A New Era of Imaging

Hyperspectral imaging (HSI) has emerged as one of the most versatile tools in modern science, offering a way to “see” beyond what conventional cameras can capture. In a comprehensive new review, Ming-Fang Cheng and colleagues detail the accelerating developments and practical applications of this technology across medicine, agriculture, environmental monitoring, food processing, and more (1). Published in Technologies the article consolidates the latest achievements and challenges, pointing toward a future where portable, artificial intelligence (AI)-driven HSI systems could become standard tools across industries (1).

The Science Behind the Spectrum

Unlike standard red, green, blue (RGB) cameras that capture only three color channels, HSI systems record hundreds or even thousands of spectral bands from the visible through near-infrared (NIR) regions (1,2). Each pixel contains a complete spectral profile, allowing the detection of subtle variations in material composition or physiological state. As the authors explain, “This high spectral resolution enables precise identification of objects, biological tissues, and materials that traditional imaging cannot distinguish” (1).

The authors describe several configurations of HSI systems: point-scanning (whiskbroom), line-scanning (pushbroom), tunable filter–based, and snapshot imaging, each optimized for different applications. Recent advances in sensor miniaturization and spectral calibration have enhanced spatial resolution, speed, and accuracy. Combining HSI with deep learning models now enables automated interpretation and feature extraction, dramatically expanding its analytical potential.

HSI Applications Across Industries

Counterfeit Detection

One of the most striking applications highlighted in the review is counterfeit detection. HSIin the 400–500 nm range can distinguish authentic Taiwanese currency from counterfeit notes using mean gray value analysis (1). In another study, Raman spectroscopy coupled with Partial Least Squares Regression accurately identified fake anti-malarial tablets, highlighting the technique’s value in pharmaceutical authentication (1).

Remote Sensing and Environmental Monitoring

HSI’s role in Earth and planetary observation is also expanding rapidly. Cheng and his co-authors cite findings showing that hyperspectral satellites improve forest classification accuracy by up to 50% and can quantify soil organic matter with R² values around 0.6. The technology also aids in assessing water quality and detecting marine plastic waste with accuracies between 70–80%. Such capabilities have implications for climate research and sustainable resource management (1,2).

Agriculture and Food Quality

In agriculture, hyperspectral analysis enables early disease detection and precision crop monitoring. A reported HSI-TransUNet model achieved 98.09% accuracy in detecting crop diseases and 86.05% in classification (1). In food processing, the method predicted egg freshness with an R² of 0.91 and achieved 100% accuracy in pine nut quality classification (1,2). These results emphasize HSI’s potential to ensure food safety and optimize supply chains.

Medical and Cancer Imaging

Perhaps the most impactful advances are in medical diagnostics. HSI can differentiate between healthy and cancerous tissues with high sensitivity and specificity, 87% and 88%, respectively, for skin cancer and 86% and 95% for colorectal cancer detection. The non-invasive, label-free nature of HSI makes it particularly suited for clinical use, enabling real-time decision support during surgery and pathology (1).

Overcoming Barriers with AI and Miniaturization

Despite its promise, widespread adoption of HSI has been constrained by high equipment costs, complex data analysis, and limited portability. Each image, comprising thousands of spectral channels, creates massive datasets requiring advanced computational methods. The review emphasizes that integrating AI and deep learning (DL) techniques, such as dimensionality reduction and neural network–based feature extraction, is key to overcoming these bottlenecks (1).

Researchers are now developing adaptive acquisition algorithms and compact HSI sensors, aiming to enable field-deployable systems for agriculture, healthcare, and environmental monitoring. According to Cheng and co-authors, the convergence of AI, miniaturization, and real-time processing will “enhance the accessibility, practicality, and efficacy of HSI in both industrial and clinical environments” (1).

Looking Forward

HSI is poised to become a cornerstone technology across scientific and industrial domains. As hardware becomes more affordable and algorithms more efficient, its ability to capture both spatial and spectral information in a single shot will transform how materials, living tissues, and even planetary surfaces are analyzed. From detecting counterfeit currency to diagnosing cancer non-invasively, hyperspectral imaging continues to expand the visible boundaries of science.

References

(1) Cheng, M.-F.; Mukundan, A.; Karmakar, R.; Valappil, M. A. E.; Jouhar, J.; Wang, H.-C. Modern Trends and Recent Applications of Hyperspectral Imaging: A Review. Technologies 2025, 13 (5), 170. DOI: 10.3390/technologies13050170

(2) Bouslihim, Y.; Bouasria, A. Potential of EnMAP Hyperspectral Imagery for Regional-Scale Soil Organic Matter Mapping. Remote Sens. 2025, 17 (9), 1600. DOI: 10.3390/rs17091600