Hyperspectral Imaging in Space

Imagine seeing the world in more spectral detail so you can better understand and discern anything on our planet. Both multispectral and hyperspectral imaging captures reflected light.Multispectral imaging breaks light into 4 to 36 bands. Then, it assigns those bands names such as red, green, blue and near infrared.
Hyperspectral imaging does the same. It takes a spectrum of light. But it divides the light into hundreds of narrow spectral bands.

Now that you've got the basics, let's explore hyperspectral imaging from space. What's the past, present and future of hyperspectral sensors?A Breakdown of Imaging Types
The major types of imaging are panchromatic, visible with near infrared (VNIR), multispectral, super-spectral and hyperspectral.

Is Hyperspectral Imaging the Future of Remote Sensing?

Hyperspectral imaging from space is a bit of a rare commodity. The main reasons are its level of complexity and large data size. For example, a Hyperion image contains 242 individual TIFF files. Each one shows a tiny spectrum of light.

PRO TIP: Go to USGS Earth Explorer to download Hyperion imagery. Check out our tutorial how to download satellite imagery from it.

If you combine all 242 images, the data size is 188 MB (zipped). But the swath is absolutely tiny at 680 km2 (262 mi2). Then if you compare this to a typical Sentinel-2 scene which is 12,000 km2 (4,660 mi2).

But over the years, technology and storage have advanced rapidly. Could hyperspectral imaging be a trend for the future?

The answer is yes.

Hyperspectral Imaging Applications

The main idea behind the emergence of hyperspectral imaging is that it gives a greater level of spectral detail. If you have hundreds of narrow band, you have finer amounts of detail that you can comb through. In turn, you can reveal new information about featured that you didn't know were possible.

The main areas of usage for hyperspectral imagery can be categorized into 8 groups. This includes vegetation, agriculture, geology, soil, water resources, disaster and land use.
The underlying principle is that it improves any type of classification. For example, you can get more detail for geologic surface composition. The same is true for vegetation types, soil classes and land cover too.

Hyperspectral imaging helps identify pests for crop management. For water resources, it's about understanding bathymetry, water quality and chemistry. And it's been used for disaster management such as prevention and post-monitoring.
25 November / 2020