Spectral imaging was first introduced in the early 1970s and was mainly for military applications. It was originally referred to as multispectral. Due to the increase of spectral bands and advancements through successive generations of imaging, hyperspectral was introduced.
Multispectral and hyperspectral are types of spectral imaging with similar technologies. They are distinct imaging methods in that they each have their own application spaces. Such application spaces have evolved to include; remote sensings like map species, mineral exploration, food engineering, agriculture, atmospheric studies, ecology, health care, and agriculture.
Multispectral remote sensing involves the acquisition of visible, near-infrared, and short-wave infrared images. These images are acquired in several broad wavelength bands. Therefore, a multispectral image captures the image data within a specific wavelength range across the electromagnetic spectrum. The different materials captured reflect and absorb differently at these different wavelengths. In this imaging method, it is possible to differentiate among materials by their spectral reflection signatures as observed in these remotely sensed images. It makes direct identification not possible.
On the other hand, hyperspectral remote sensing is a technique that analyzes a wide spectrum of light instead of just assigning primary colors to each pixel. Its primary goal is to obtain a spectrum from each pixel in the image of a scene to find objects, detect processes, or identifying materials. Hyperspectral remote sensing samples a wide variety of bandwidths in the light spectrum. This aims to provide a rich dataset and detect objects of interest not visible to single-bandwidth imaging sensors.
In the article below, we give a more detailed overview of multispectral and hyperspectral remote sensing. Let’s get started.
Differences
1. Number of wavebands
Multispectral imaging follows a Low Earth Orbit and sun-synchronous. The multispectral satellites capture data along 5 to 10 bands of the spectrum. Most often, it also captures all three primary colors and a few blocks in the infrared portion.
On the other hand, hyperspectral imaging can detect thousands of different bands within the light spectrum. Sich imagery is extremely helpful to detect certain objects and minerals if an analyst is familiar with their spectral properties. Just like multispectral, their satellites also follow the sun-synchronization, Low Earth Orbit.
2. Spectral resolution detail
Spectral resolution refers to the number and width of the electromagnetic spectrum portions measured by a sensor. Multispectral remote sensing has a poor spectral resolution. Thus, it makes it harder to discern the Earth’s features as easily as hyperspectral sensors. The reason is that multispectral sensors are captured in small numbers due to the wide bands.
On the other hand, hyperspectral remote sensing has a high spectral resolution that makes it possible to detect the spectral properties of objects and minerals. It gives the better capability to see the unseen.
3. The narrowness of the band
Multispectral remote sensing systems use parallel sensor arrays that detect radiation in a small number of broader wavebands.
Meanwhile, in hyperspectral remote sensing, the bands are much narrower. These numerous narrow bands in hyperspectral sensors provide a continuous spectral measurement across the entire electromagnetic spectrum. Therefore, this makes them more sensitive to subtle variations in reflected energy.
4. Ability to use artificial intelligence and machine learning
Multispectral remote sensing images have lower informational content hence continued use of the same technology over time. Due to the lack of information richness in this imaging technology, it faces a disability of continual development.
On the other hand, hyperspectral imaging technology is facilitated by the high informational content hence continuous development. It is therefore positioned to become a primary remote sensing technology used on a global scale.
5. Complexity level
Due to the limited number of bands in multispectral remote sensing, data analysis and interpretation are straightforward. It is also easier to understand.
Meanwhile, one of the downfalls of hyperspectral remote sensing is its complexity. It has many bands one has to work with; hence, it may be difficult to reduce redundancies or hard work.
6. Image features
In multispectral data, reflected energy in the spectrum spans a wider range. It makes it difficult to get a lot of detail on an object or surface area captured. It is because the wavelength bands are much broader.
While when it comes to hyperspectral data, images present hundreds of points of each band hence much more detail to observe. The wavelength is split into numerous narrow bands that capture a unique spectral fingerprint or signature of an object. Therefore, the images captured contain much more data, thus preventing any analyst from the potential to detect differences among land and water features.
7. Camera differences
Multispectral images are captured with special cameras that separate wavelengths using filters or with instruments that are sensitive to particular wavelengths. These can include light from frequencies that are invisible to the human eye.
Hyperspectral cameras as on the other hand, can detect many different wavelengths separately. They can also see across a wider spectrum than a human can by covering parts of the infrared and ultraviolet regions. Therefore, in this imaging technique, analysts will acquire a 2-dimensional image whereby every pixel in the image contains a continuous spectrum.
8. Costs
Multispectral sensors commonly collect data from three to six spectral bands in a single observation. Such features make them cost-effective. They are cheap to buy and maintain as the image captures are not complex.
Meanwhile, hyperspectral imaging makes it possible to collect several hundred spectral bands in a single acquisition. Such a feature has made it expensive as it demands more technological advancement to produce more detailed spectral data. With this comes issues related to increased sensor and image costs, data volumes and data-processing costs, and high demand to maintain operations.
9. Pixel composition
In multispectral remote sensing, each pixel has a discrete sample spectrum. For example, some wavebands may have 4 to 20 data points per pixel.
While, in hyperspectral remote sensing, each pixel has a continuous or complete spectrum
10. Processing methods
The multispectral remote sensors process limited images.
On the other hand, the hyperspectral remote sensors processing methods include both spectral and images.
