What is the difference between multispectral and hyperspectral imagery?

The differences between multispectral and hyperspectral imagery

Agriculture is a rapidly growing industry. So the industry needs to keep innovating to support and optimize the agribusiness process. Sensors for aerial imagery have a massive role in several technologies used for agriculture. Agribusiness has long used sensors to assist agricultural operations such as spraying, mapping, and planting. To find out how the sensor works in spraying, you can read the article here. This article will describe what difference between multispectral and hyperspectral imagery. Then, why is aerial imagery vital for agriculture. 


Aerial imagery is a collection of captured and recorded images from above. It allows agribusiness to see otherwise impossible things, such as the layout of crops and land use. Aerial imagery has many benefits, such as planning for future needs and reducing the amount of land for farming, which helps our environment and economy reduce input needs. There are two types of aerial imagery: multispectral and hyperspectral. Multispectral and hyperspectral imagery both have their agricultural functions. They each have their own set of advantages and disadvantages. Equally important, it is essential to understand the difference between multispectral and hyperspectral imagery. It prevents you from getting inaccurate crop mapping results. 


Multispectral vs. Hyperspectral


Multispectral imaging 

Multispectral imagery is imagery that captures reflectance across multiple bands of the electromagnetic spectrum. Multispectral imagery collects data from numerous spectral bands across the electromagnetic spectrum. It typically has 5 to 10 bands such as red, green, and blue (RGB) to create a color image. In addition, the most common multispectral sensors collect data in the visible, near-infrared, and shortwave infrared bands. 



Applications of multispectral imagery

Most people have well-known multispectral imagery for mapping vegetation health and vigor, forested areas, estimating crop biomass and yield, and classifying surfaces. Moreover, it is also beneficial for:

  • Study vegetation and agricultural production trends and cycles.

  • Analyze water and environmental quality, soils, geology, and other earth resources.

  • Collect data from dangerous and inaccessible areas.

  • Map and monitor algal blooms in coastal waters.


Hyperspectral imaging

Hyperspectral imagery (HSI) captures reflectance across a continuous range of wavelengths, providing a more acceptable level of spectral resolution. Hyperspectral imaging collects data over a broader range of wavelengths, including invisible and short-wavelength infrared regions. It usually has hundreds or more bands. Sensors can tune to specific fields of wavelengths depending on the application. Hence, it gives HSI a much higher level of spectral resolution, allowing it to identify and measure objects' chemical composition in great detail. 



Applications of hyperspectral imagery

Hyperspectral imagery can be used to map tree species within the forest. Likewise, it has advantages in some aspects, such as:

  • Distinguishing the earth's surface features.

  • Trace seed viability by plotting the reflectance spectrum.

  • Tracking surface CO2 emissions and map hydrological formations and pollution levels.

  • Bruise detection in apples, the freshness of the fish, citrus fruit inspection, distribution of sugar in melons, and sorting of potatoes. 



Summary: Multispectral vs. Hyperspectral


Parameters

Multispectral

Hyperspectral

Wavebands numbers

5 to 10 bands of the spectrum

hundreds of bands of the spectrum

Spectral resolution detail

poor spectral resolution

high spectral resolution

Band narrowness 

broader wavebands

narrower wavebands

Processing methods

process limited images

use spectral and images



In a word, multispectral and hyperspectral play their unique roles in some industries, including agriculture. Agribusinesses can adapt to their needs when using multispectral or hyperspectral. Both are very helpful for agribusiness to analyze the process of determining agricultural land, the quality of vegetables and fruit, the level of water pollution, and reaching inaccessible human areas.




Posted by : Nurhayati

Published At : 28/05/2022 19:50:09

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