Multispectral Imaging: Unlocking the Spectrum: Advancements in Computer Vision

By Fouad Sabry

What is Multispectral Imaging

Multispectral imaging captures image data within specific wavelength ranges across the electromagnetic spectrum. The wavelengths may be separated by filters or detected with the use of instruments that are sensitive to particular wavelengths, including light from frequencies beyond the visible light range, i.e. infrared and ultra-violet. It can allow extraction of additional information the human eye fails to capture with its visible receptors for red, green and blue. It was originally developed for military target identification and reconnaissance. Early space-based imaging platforms incorporated multispectral imaging technology to map details of the Earth related to coastal boundaries, vegetation, and landforms. Multispectral imaging has also found use in document and painting analysis.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Multispectral imaging

Chapter 2: Infrared

Chapter 3: Remote sensing

Chapter 4: Thermographic camera

Chapter 5: Satellite imagery

Chapter 6: Spectral signature

Chapter 7: Spectral imaging

Chapter 8: Hyperspectral imaging

Chapter 9: Chemical imaging

Chapter 10: Normalized difference vegetation index

(II) Answering the public top questions about multispectral imaging.

(III) Real world examples for the usage of multispectral imaging in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Multispectral Imaging.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: May 12, 2024

Book preview

Chapter 1: Multispectral imaging

Multispectral imaging is a technique that takes pictures at various different wavelengths. Light with frequencies outside the visible light range, such as infrared and ultra-violet, can be separated by filters or detected using devices sensitive to particular wavelengths. It has the potential to enable the extraction of additional information that the human eye's red, green, and blue visual receptors miss. Its original purpose was for military reconnaissance and target identification. Multispectral imaging was first used in early space-based imaging devices.

Light is often measured in a range of 3 to 15 different spectral bands when using multispectral imaging. When hundreds of adjacent spectral bands are accessible, as they usually are in hyperspectral imaging, the resulting image can reveal subtle differences between objects.

The military frequently employs multispectral photography to detect or track targets by measuring their light emissions. The Federal Laboratory Collaborative Technology Alliance and the United States Army Research Laboratory reported a dual band multispectral imaging focal plane array in 2003. (FPA). With this FPA, scientists were able to simultaneously examine two infrared (IR) planes. Due to its ability to detect heat without an auxiliary light source, thermal imaging is another name for the mid- and long-wave infrared (MWIR) technologies.

The emissivity and temperature of an object determine how vivid the image produced by a thermal imager will be.

Thermal imaging surpassed single-band multispectral imaging for detecting targets at night. Better nighttime visibility was achieved with dual band MWIR and LWIR technologies than with MWIR alone. Citation Citation. The United States Army claims that its dual band LWIR/MWIR FPA, which tracks vehicles during the day and night, provides better visualization of tactical vehicles than MWIR alone.

Multispectral imaging can locate hidden missiles by measuring their emissivity on the ground. Spectral analysis can reveal the physical and chemical differences between surface and subsurface soil.

To successfully intercept an ICBM during its boost phase, it is necessary to image both the missile's hard body and the rocket plumes. LWIR produces emissions from the missile's body material, while MWIR presents a strong signal from highly heated items like rocket plumes. The United States Army Research Laboratory stated that its dual-band MWIR/LWIR system detected both the missile body and plumage when tracking Atlas 5 Evolved Expendable Launch Vehicles. These rockets are structurally comparable to intercontinental ballistic missiles.

The majority of remote sensing (RS) radiometers are capable of capturing multispectral images. Multispectral imaging is the polar opposite of panchromatic imaging, which simply captures the overall intensity of the radiation hitting each pixel, by dividing the spectrum into several bands. Typically, there are three or more radiometers on Earth-observing satellites. Each collects a single digital image (known as a scene in remote sensing terminology) in a narrow wavelength range. According on the researchers' goals and the source of the light, the spectrum has been divided into distinct wavelength regions, or bands..

Images captured by today's weather satellites can be found in a wide range of wavelengths.

Multispectral imaging uses a single optical setup to capture data from many, narrowband spectral imaging bands.

A multispectral system usually provides a combination of visible (0.4 to 0.7 µm), NIR, or near-infrared; 0.7 to 1 µm), SWIR, or short-wave infrared; 1 to 1.7 µm), MWIR, or mid-wave infrared; 3.5 to 5 µm) or long-wave infrared (LWIR; 8 to 12 µm) bands into a single system.

The Author, Valerie C.

Coffey

A multispectral image from a Landsat satellite can include as many as eleven separate bands with various names. The terms hyperspectral and ultraspectral are used to describe types of spectral imaging that have either a greater radiometric resolution (with hundreds or thousands of bands) or a finer spectral resolution (involving smaller bands) or a broader spectral coverage.

Paintings and other works of art can be studied with multispectral imaging.

The wavelengths are only approximations, with the true values depending on the specific equipment used (such as the properties of the satellite's sensors for Earth observation, or the parameters of the illumination and sensors for document analysis):

The blue spectrum (450-515..520 nm) is useful for imaging the atmosphere and deep water; in clean water, it may penetrate to a depth of 150 feet (50 m).

Green light (515..520-590..600 nm) can see up to 90 feet (30 m) deep in clear water, making it useful for imaging vegetation and deep water structures.

Images of man-made objects, dirt, and flora in depths of up to 30 feet (9 meters) can be captured in the red range of 600..630-680..690 nm.

The 750-900 nm NIR range is primarily employed for plant imaging.

Mid-infrared (MIR) cameras capture images of vegetation, soil moisture content, and some forest fires at wavelengths between 1550 and 1750 nanometers.

Soil, moisture, geological structures, silicates, clays, and fires can all be imaged using far-infrared (FIR) from 2080-2350 nm.

Geological features, thermal changes in water currents, fires, and nighttime studies can all be imaged using thermal infrared (10400-12500 nm), which relies on emitted rather than reflected radiation.

It is possible to detect and map terrain features with the use of radar and related technologies.

Various mixtures of spectral bands can be employed for various reasons. Red, green, and blue are the standard colors used to depict them. The image's intended use and the analyst's own preferences will determine how the bands are mapped to colors. Due to its low spatial resolution, thermal infrared is rarely taken into account.

Only the RGB color channels—red, green, and blue—are used in true color. Analyzing man-made objects is made simple with this straightforward color snapshot, making it accessible to novice analysts.

For vegetation, which is extremely reflective in near IR, the green-red-infrared color space is employed, with the blue channel substituted by near infrared. This is a common method for locating hidden objects and vegetation.

Blue-NIR-MIR displays visible blue, green NIR (so vegetation retains its green color), and red MIR. Images like this make it possible to see fires, water levels, plant covering, and soil moisture all at once.

Various other permutations are also in use. Since NIR is typically depicted in red, any regions with plants in it will also seem red.

These multispectral images, in contrast to other aerial photographic and satellite image interpretation efforts, do not make it simple to determine directly the feature type through visual inspection. Therefore, it is necessary to first categorize the remote sensing data, before subjecting it to processing using various data enhancement techniques to aid the user in making sense of the features visible in the image.

A comprehensive validation of the training samples may be required for such a classification task, depending on the classification algorithm chosen. The methods essentially fall into two categories:.

Supervised classification techniques

Unsupervised classification techniques

Training samples are used in supervised categorization. Ground truth, or training samples, are locations where the actual state of affairs is known. In order to identify the remaining picture pixels, we use the spectral signatures of the training areas