Multispectral imaging

Multispectral imaging (MSI) is a scientific and technological technique that captures image data within specific wavelength ranges across the electromagnetic spectrum, beyond what the human eye can perceive. Unlike conventional colour photography, which records three primary colour bands (red, green, and blue), multispectral imaging records data from multiple distinct spectral bands, including visible, ultraviolet (UV), and infrared (IR) regions. This allows detailed analysis of materials, surfaces, and structures based on their spectral properties and reflectance characteristics.

Principle of Multispectral Imaging

Every material reflects, absorbs, and emits electromagnetic radiation differently at various wavelengths. These spectral characteristics act as unique signatures for identifying materials or detecting changes that are not visible in natural light.
In multispectral imaging, sensors capture images of the same scene or object in several discrete spectral bands. By analysing the intensity of reflected or emitted radiation in each band, scientists can extract detailed information about the composition, condition, and features of the object.
For instance, vegetation reflects strongly in the near-infrared (NIR) region, while healthy and stressed plants differ in their spectral responses. Similarly, minerals, pigments, and tissues have characteristic spectral patterns that can be identified through multispectral analysis.

Components of a Multispectral Imaging System

A multispectral imaging setup generally consists of the following key components:

• Illumination Source: Provides controlled light, which may be natural sunlight or artificial sources such as halogen or LED lamps, depending on the application.
• Optical Filters: Select specific wavelength ranges for imaging; these can be mechanical filter wheels, tunable filters, or interference filters.
• Imaging Sensor: Specialised cameras (often CCD or CMOS detectors) sensitive to multiple spectral bands, including non-visible regions.
• Calibration System: Ensures accuracy by comparing the captured data to known reference standards.
• Image Processing Software: Integrates and analyses data from multiple bands to generate composite images or extract spectral signatures.

Spectral Range and Bands

Multispectral imaging typically captures data in 3 to 15 discrete bands, depending on the purpose. The main spectral ranges include:

• Visible light (400–700 nm) — Provides colour information similar to human vision.
• Near-Infrared (700–1000 nm) — Useful for studying vegetation, moisture content, and biological tissues.
• Short-Wave Infrared (1000–2500 nm) — Detects water absorption features, minerals, and surface contamination.
• Ultraviolet (below 400 nm) — Helps reveal fluorescence and surface details not visible under normal light.

Each band provides different information, and the combination of all bands creates a multidimensional dataset for interpretation.

Techniques of Multispectral Imaging

Multispectral imaging can be implemented through several techniques based on the method of capturing spectral data:

• Filter-Based Imaging: Uses a rotating filter wheel or multiple filters in sequence to capture images in different wavelength bands.
• Beam-Splitter Systems: Split the incoming light into several beams, each directed to a separate detector for simultaneous multi-band capture.
• Tunable Filter Imaging: Employs electronically controlled filters (such as liquid crystal or acousto-optic tunable filters) to rapidly switch wavelengths.
• Snapshot Multispectral Cameras: Capture all bands simultaneously using advanced sensor arrays, allowing real-time imaging.

Applications of Multispectral Imaging

1. Remote Sensing and Earth Observation: Multispectral imaging is fundamental to satellite and aerial remote sensing. Instruments like Landsat, Sentinel, and MODIS capture multispectral data to study:

• Land use and vegetation cover.
• Soil and mineral mapping.
• Water quality assessment.
• Environmental monitoring and disaster management.

2. Agriculture and Forestry: Multispectral imaging is widely used in precision agriculture for crop health assessment and resource management. It helps in:

• Detecting plant stress and disease.
• Estimating chlorophyll content and biomass.
• Monitoring irrigation and soil moisture levels.
• Distinguishing crop species and mapping yield zones.

3. Archaeology and Cultural Heritage: In conservation science, MSI helps recover hidden or faded texts and paintings by imaging in non-visible wavelengths.

• Detection of under-drawings, erased manuscripts, and pigment compositions.
• Non-destructive examination of ancient artefacts and wall paintings.

4. Medical and Biological Applications: Multispectral imaging is used in biomedical diagnostics and life sciences research to detect variations in tissue composition and oxygenation.

• Early detection of skin cancers or wounds.
• Mapping blood oxygenation in tissues.
• Studying cell and tissue morphology under different spectral conditions.

5. Defence and Surveillance: Military and defence applications utilise MSI for target detection, camouflage identification, and reconnaissance. The technology allows differentiation between natural and artificial materials based on their spectral properties.
6. Industrial and Food Quality Inspection: Used in quality control and manufacturing for identifying contaminants, surface defects, or adulteration.

• Detection of bruises or rot in fruits.
• Sorting materials based on chemical composition.
• Monitoring pharmaceutical coatings and packaging integrity.

Advantages of Multispectral Imaging

• Non-destructive Analysis: Provides information without damaging the object or sample.
• Enhanced Detection: Reveals details invisible in standard imaging, especially in environmental and biological studies.
• Versatility: Applicable to a wide range of disciplines from remote sensing to art restoration.
• Quantitative Data: Enables precise measurement of material composition and condition through spectral analysis.

Limitations

• High Cost: Advanced multispectral systems and sensors are expensive.
• Complex Data Processing: Requires specialised software and expertise to interpret multidimensional datasets.
• Limited Spectral Resolution: Compared to hyperspectral imaging, which records hundreds of narrow bands, multispectral imaging captures fewer, broader bands.
• Environmental Dependence: Illumination and atmospheric conditions can affect data quality in outdoor imaging.

Multispectral vs Hyperspectral Imaging

While both techniques capture data across multiple spectral bands, they differ in spectral resolution:

• Multispectral Imaging: Captures a few discrete, broad wavelength bands.
• Hyperspectral Imaging: Captures hundreds of contiguous narrow bands, offering finer spectral detail for precise material discrimination.