Multispectral vs Hyperspectral Imagery Explained

multispectral vs hyperspectral

What is the difference between multispectral and hyperspectral imagery?

Multispectral vs hyperspectral. What are the differences between the two?

You read this post. Your eyes see the reflected energy from your screen in three channels: red, green and blue.

Now, imagine if we could view the world in the eyes of a human, goldfish and bumble bee? We can. We do this with multispectral and hyperspectral sensors.

The Electromagnetic Spectrum

Visible (red, green and blue), infrared and ultraviolet are descriptive regions in the electromagnetic spectrum. We, humans made up these regions for our own purpose – to conveniently classify them. Each region is categorized based on its frequency (v) /wavelength (\lambda).

  • Humans see visible light (380 nm to 700 nm)
  • Goldfish see infrared (700 nm to 1mm)
  • Bumble bees see ultraviolet (10 nm to 380 nm)

Multispectral and hyperspectral imagery gives the power to see as humans (red, green and blue), goldfish (infrared), bumble bees (ultraviolet) and more. This comes in the form of reflected EM radiation to the sensor.

The main difference between multispectral and hyperspectral is the number of bands and how narrow the bands are.

Multispectral imagery generally refers to 3 to 10 bands that are represented in pixels. Each band is acquired using a remote sensing radiometer.

Multispectral Example
Multispectral Example: 5 wide bands (Image not drawn to scale)

Hyperspectral imagery consists of much narrower bands (10-20 nm). A hyperspectral image could have hundreds of thousands of bands. This uses an imaging spectrometer.

Hyperspectral Example
Hyperspectral Example: Imagine hundreds of narrow bands (Image not drawn to scale)

Multispectral example




An example of a multispectral sensor is Landsat-8. Landsat-8 produces 11 images with the following bands:

Band 1: Coastal aerosol (0.43-0.45 um)
Band 2: Blue (0.45-0.51 um)
Band 3: Green (0.53-0.59 um)
Band 4: Red (0.64-0.67 um)
Band 5: Near infrared NIR (0.85-0.88 um)
Band 6: Short-wave Infrared SWIR 1 (1.57-1.65 um)
Band 7: Short-wave Infrared SWIR 2 (2.11-2.29 um)
Band 8: Panchromatic (0.50-0.68 um)
Band 9: Cirrus (1.36-1.38 um)
Band 10: Thermal Infrared TIRS 1 (10.60-11.19 um)
Band 11: Thermal Infrared TIRS 2 (11.50-12.51 um)

Each band has a spatial resolution of 30 meters with the exception of band 8, 10 and 11. Band 8 has a spatial resolution of 15 meters. Band 10 and 11 have spatial resolutions of 100 meters.

If you’re wondering why there is no 0.88-1.36 band, atmospheric absorption is the main motive why there are no sensors detecting these wavelengths.

Hyperspectral Example

The TRW Lewis satellite was meant to be the first hyperspectral satellite system in 1997. Unfortunately, NASA lost contact with it.




But later NASA did have a successful launch mission. The Hyperion imaging spectrometer (part of the EO-1 satellite) is an example of a hyperspectral sensor. The Hyperion produces 30-meter resolution images in 220 spectral bands (0.4-2.5 um).

NASA’s Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) is an example of a hyperspectral airborne sensor. AVIRIS delivers 224 contiguous channels with wavelengths from 0.4-2.5 um.

Multispectral vs hyperspectral
  • Multispectral: 3-10 wider bands. Example: Landsat-8
  • Hyperspectral: Hundreds of narrow bands. Example: Hyperion

Multispectral vs Hyperspectral




Having a higher level of spectral detail in hyperspectral images gives better capability to see the unseen. For example, hyperspectral remote sensing distiguished between 3 minerals because of its high spectral resolution. The multispectral Landsat Thematics Mapper could not distinguish between the 3 minerals.

It also adds a level of complexity. 200 narrow bands can be difficult to work with at times.. How much redundancy is there between channels?

Hyperspectral and multispectral images have many real world applications. For example, hyperspectral imagery has been used to map invasive species and help in mineral exploration.

There are hundreds more applications in the fields of agriculture, ecology, oil and gas, oceanography and atmospheric studies where multispectral and hyperspectral remote sensing are being used to better understand the world we live in.

5 Comments on Multispectral vs Hyperspectral Imagery Explained

  1. Short answer is that you’ll be able to discern better between Earth’s features with higher spectral resolution. For example, in the mining industry there are over 4000 different types of minerals. Each mineral has its own composition. This is the equivalent to saying each mineral has its own spectral composition and spectral signature – http://gisgeography.com/spectral-signature/

    With multispectral data, reflected energy in the EM spectrum spans a wider range. So you don’t get the level of detail as hyperspectral. The graph in the spectral signature page (link above) is just an example, but there would only be a couple of points on it using multispectral data (for each band). But with hyperspectral data, there could potentially be hundreds of points for each band. This means you have much more detail

  2. Depends on what you are trying to do. This article should help you with some of the spectral bands commonly found in multispectral sensors like Landsat-7/8 (free), Sentinel 2A/2B (free) and Worldview-2/3 – http://gisgeography.com/spectral-signature/

    The more specific detail you need, the more spectral resolution (hyperspectral) would be beneficial

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