Multispectral and Hyperspectral Imaging Systems

The main idea is how a remote-sensing sensor collects information about an area.

A sensor does not simply take an ordinary photograph. It measures the electromagnetic energy reflected or emitted by objects in different wavelength bands. Depending on how many bands are measured and how the sensor collects them, different imaging systems are used.

1. Multispectral vs. Hyperspectral

Multispectral imaging (MSI) records information in a limited number of relatively broad, separate spectral bands, such as blue, green, red, near-infrared and shortwave infrared.

Hyperspectral imaging (HSI) records information in many narrow and usually contiguous spectral bands. Therefore, it provides a much more detailed spectral signature of each pixel. The resulting dataset is commonly called a hyperspectral data cube (hypercube) because it contains:

• X-axis → spatial information

• Y-axis → spatial information

• Z-axis → wavelength/spectral information

Thus, hyperspectral imaging can be thought of as “a photograph plus a detailed spectrum for every pixel.” (PubMed Central (PMC))

Main Sensor Architectures

The major difference between imaging systems is how the sensor scans the Earth's surface.

A simple way to remember them is:

Whiskbroom → scans one point at a time
Pushbroom → scans one line at a time
Frame/Area array → captures an entire area at once

Discrete Detectors + Scanning Mirror — Whiskbroom Scanner

Basic idea

Imagine sweeping a floor with a broom from left to right.

A whiskbroom sensor works in a similar way. A mechanical mirror moves from side to side, directing energy from different ground pixels toward a detector.

The sensor therefore observes the ground pixel by pixel and builds the image line by line as the aircraft or satellite moves forward. This is called across-track scanning. (Natural Resources Canada)

How it works

Satellite moves forward → mirror scans left/right → detector records energy → next line is scanned

          Satellite →
              ↓
      ┌─────────────────┐
      │ Scanning mirror │ ↔
      └─────────────────┘
          ↓ ↓ ↓ ↓ ↓
      Ground pixels
      █ █ █ █ █ █ █

Important terms

• Whiskbroom = across-track scanner

• Scanning mirror = mechanically moves to view different ground locations

• Detector = measures incoming electromagnetic energy

• IFOV (Instantaneous Field of View) = small ground area seen by the detector at one instant

• Dwell time = time during which the detector observes a particular ground area

• Across-track = perpendicular to the direction of platform movement

Advantages

• Relatively established and robust technology

• Optical components and detectors can be accommodated effectively

• Traditionally useful for satellite and airborne remote sensing

Disadvantages

• Moving mechanical parts can wear out.

• Each ground pixel has a relatively short dwell time.

• Geometric calibration can be more complicated because the viewing geometry changes as the mirror scans.

• The short dwell time can limit the amount of energy collected. (Natural Resources Canada)

Examples

Landsat MSS/TM-type scanning systems are classic examples of the across-track scanning concept. (Canadian Forest Service)

Linear Array — Pushbroom Scanner

This is one of the most important concepts in modern remote sensing.

Basic idea

Instead of using a moving mirror, a row of detectors is placed across the sensor.

Think of a broom being pushed forward across the floor. The detector line moves forward with the aircraft or satellite.

Hence the name pushbroom scanner.

It records one line of the ground at a time, while the forward movement of the satellite/aircraft provides the second spatial dimension. (Natural Resources Canada)

How it works

                Satellite →
                    ↓
       Detector array
       ● ● ● ● ● ● ●
       ↓ ↓ ↓ ↓ ↓ ↓ ↓
       ───────────────  Line 1
       ───────────────  Line 2
       ───────────────  Line 3
       ───────────────  Line 4

The detectors continuously observe successive lines as the platform moves forward.

Key concept

Whiskbroom:

Mirror moves across the ground.

Pushbroom:

Platform moves forward over a stationary detector line.

Why is Pushbroom better in many situations?

A pushbroom sensor can observe a ground location for a longer integration/dwell time because the detector does not have to rapidly sweep across the scene.

More collected energy generally means a better signal-to-noise ratio (SNR) and improved radiometric sensitivity. It can also support finer spatial and spectral resolution. (Natural Resources Canada)

Advantages

• No moving scanning mirror

• Longer dwell/integration time

• Better signal-to-noise ratio

• Smaller and more reliable solid-state detectors

• Can provide high spatial and spectral resolution

• Lower mechanical complexity

Disadvantages

The major problem is detector calibration.

Suppose a detector array contains 2,000 individual detector elements. Ideally, all detectors should respond identically. If one detector is slightly more sensitive than another, visible stripes or striping noise may appear in the image.

Therefore, cross-calibration and uniformity of detector response are very important. (Natural Resources Canada)

Example

SPOT is a classic example of a pushbroom imaging system. (Canadian Forest Service)

Linear-Array Hyperspectral Imaging

Pushbroom technology is also widely used in hyperspectral imaging.

Here, the sensor does something more sophisticated than simply recording several broad bands.

A spectrometer separates incoming light into many wavelengths using an optical element such as a diffraction grating or prism.

The detector then records:

One spatial line + many wavelengths

As the platform moves forward, these lines are combined to create the complete hyperspectral image.

Result

A 3-D hyperspectral data cube is produced:

             Wavelength (λ)
                   ↑
                   │
             ┌───────────┐
            /│           /│
           / │          / │
          └─────────────┘ │
          │  │           │ │
          │  └───────────│─┘
          │ /            │
          └──────────────┘
        X, Y = Space
        Z   = Wavelength

Each pixel therefore has a spectral signature.

This is why hyperspectral remote sensing is particularly useful for distinguishing materials that may look similar in ordinary imagery—for example, different minerals, vegetation species or crop conditions. (PubMed Central (PMC))

Area Array / Frame Camera — Staring or Snapshot Imaging

This approach is closer to an ordinary digital camera.

Instead of scanning one point or one line, a 2-D detector array captures an entire scene or field of view.

Modern cameras commonly use:

• CCD — Charge-Coupled Device

• CMOS — Complementary Metal-Oxide-Semiconductor

Simple example

Your smartphone camera is essentially a frame-based area-array imaging system: it captures a 2-D image in a single exposure.

Remote-sensing frame cameras apply the same basic principle but can be designed to capture multiple spectral bands.

Multispectral frame cameras

They may use:

• Separate cameras for different spectral bands

• Different filters

• Filter wheels

• Multispectral Filter Arrays (MSFA)

Natural Resources Canada identifies full-frame systems as another major sensor architecture alongside mechanical scanners and pushbroom systems. (Canadian Forest Service)

Advantages

• Captures a complete 2-D frame

• Very fast acquisition

• Useful where the scene or platform is moving

• Familiar photogrammetric geometry

• Can reduce some distortions associated with sequential scanning

Disadvantages

There are trade-offs between:

• Spatial resolution

• Spectral resolution

• Number of spectral bands

• Frame rate

• Sensor complexity

• Data volume

A conventional frame camera generally does not provide the same detailed spectral information as a dedicated hyperspectral pushbroom system.

Area-Array Hyperspectral / Snapshot Imaging

A more advanced form of area-array imaging is snapshot hyperspectral imaging.

Instead of moving across the scene to collect every wavelength, the sensor attempts to acquire the 2-D spatial scene and spectral information simultaneously or nearly simultaneously.

Different technologies can be used, including:

• Tunable filters

• Filter mosaics

• Coded apertures

• Other computational/optical techniques

The important advantage is rapid acquisition, because the system does not necessarily need to scan the scene sequentially. Hyperspectral imaging literature commonly identifies point scanning, pushbroom, wavelength/plane scanning, and snapshot approaches as major acquisition strategies. (PubMed Central (PMC))

 Difference

System	What is scanned/captured?	Main mechanism	Simple analogy
Whiskbroom	One pixel/point at a time	Moving mirror	Sweeping with a broom
Pushbroom MSI	One line at a time	Linear detector array	Pushing a broom forward
Pushbroom HSI	One spatial line + many wavelengths	Spectrometer + detector array	Line-by-line spectral camera
Frame/Area array	Entire 2-D scene	2-D CCD/CMOS	Ordinary digital camera
Snapshot HSI	2-D scene + spectral information simultaneously	Special optical/filter system	Spectral camera taking an instant snapshot

Spectral resolution

Ability of a sensor to distinguish closely spaced wavelengths.

• Multispectral → fewer, broader bands

• Hyperspectral → many, narrower bands

Spatial resolution

Size of the smallest ground object/pixel that can be distinguished.

Radiometric resolution

Ability to distinguish small differences in the amount of energy recorded.

Temporal resolution

How frequently a sensor can acquire imagery of the same area.

Dwell time / Integration time

Time available for the detector to collect energy from a ground location.

Longer dwell time → more photons collected → potentially better SNR.

SNR — Signal-to-Noise Ratio

Ratio between useful measured signal and unwanted noise.

Higher SNR = cleaner and more reliable measurement.

Detector array

A group of individual detector elements arranged in a line or 2-D matrix.

Spectral signature

Characteristic pattern of reflectance/emission of an object across wavelengths.

Data cube / Hypercube

A 3-D representation of hyperspectral information:

X + Y + λ

where X and Y = spatial dimensions and λ = wavelength. (Agroengineering)

Think of three cameras/scanners:

🧹 Whiskbroom

Point → Point → Point

Moving mirror scans the ground.

🧹 Pushbroom

Line → Line → Line

A detector line records the ground while the platform moves.

📷 Frame camera

Whole area → Whole area → Whole area

A 2-D detector captures the complete scene.

And for hyperspectral imaging:

Pushbroom + Spectrometer = spatial line + many wavelengths → hyperspectral data cube

The terms whiskbroom and pushbroom can become confusing because different sources sometimes describe the detector arrangement differently. The safest distinction is based on scanning mechanism:

• Whiskbroom = across-track scanning using a moving/oscillating mirror

• Pushbroom = along-track scanning using a linear detector array

For hyperspectral pushbroom systems, a 2-D detector can simultaneously represent one spatial dimension and the spectral dimension, while platform motion supplies the second spatial dimension. (Natural Resources Canada)

Whiskbroom scans point-by-point, pushbroom scans line-by-line, and area-array/frame cameras capture an entire 2-D scene at once; hyperspectral systems add detailed wavelength information to produce a spectral data cube.

References

Ben-Dor, E., & Plaza, A. (2012). Hyperspectral remote sensing. In Hyperspectral imaging and spectral signatures.

Kim, J.-I., Chi, J., Lee, J., & Kim, H.-C. (2022). High-resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems. Geoscience Data Journal, 9(2), 221–234. https://doi.org/10.1002/gdj3.133 (Royal Meteorological Society)

Natural Resources Canada. (2025). Multispectral scanning. Government of Canada. (Natural Resources Canada)

Natural Resources Canada. (2019). ITC analysis of aerial images. Government of Canada. (Canadian Forest Service)

Qian, S. (2014). Optical hyperspectral imaging in microscopy and spectroscopy: A review of data acquisition. Journal of Biomedical Optics, 19(10). (PubMed Central (PMC))

Photonics Spectra. (2020). Hyperspectral and multispectral imaging. (Photonics)