Multispectral vs Hyperspectral Imagery Explained

multispectral vs hyperspectral

Last Updated: Aug 23, 2017

What is the difference between multispectral and hyperspectral imagery?

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

When you read this post, your eyes see the reflected energy. But a computer sees it in three channels: red, green and blue.

  • If you were a goldfish, you would see light differently. A goldfish can see infrared radiation which is invisible to the human eye.
  • Bumble bees can see ultraviolet light. Again, humans can’t see ultraviolet radiation from our eyes. (UV-B harms us)

Now, imagine if we could view the world in the eyes of a human, goldfish and bumble bee? Actually, 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).

  • Humans see visible light (380 nm to 700 nm)
  • And 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) and bumble bees (ultraviolet). Actually, we can see even more than this as 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. To be clear, each band is obtained 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 or thousands of bands. In general, it comes from 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:

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

Each band has a spatial resolution of 30 meters with the exception of band 8, 10 and 11. While band 8 has a spatial resolution of 15 meters, band 10 and 11 have 100 meter pixel size.

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. For instance, 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. For example, AVIRIS delivers 224 contiguous channels with wavelengths from 0.4-2.5 um.

Multispectral vs hyperspectral
  • Multispectral: 3-10 wider bands.
  • Hyperspectral: Hundreds of narrow bands.

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. But the multispectral Landsat Thematic Mapper could not distinguish between the 3 minerals.

But one of the downfalls is that it adds a level of complexity. If you have 200 narrow bands to work with, how can you reduce redundancy 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 where multispectral and hyperspectral enable us to understand the world. For example, we use it in the fields of agriculture, ecology, oil and gas, oceanography and atmospheric studies.

5 Comments

  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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