Multispectral image

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Video by SDO simultaneously showing sections of the Sun at various wavelengths

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. Spectral imaging 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 [1] to map details of the Earth related to coastal boundaries, vegetation, and landforms. [2] Multispectral imaging has also found use in document and painting analysis. [3] [4]

Contents

Multispectral imaging measures light in a small number (typically 3 to 15) of spectral bands. Hyperspectral imaging is a special case of spectral imaging where often hundreds of contiguous spectral bands are available. [5]

Applications

Military target tracking

Multispectral imaging measures light emission and is often used in detecting or tracking military targets. In 2003, researchers at the United States Army Research Laboratory and the Federal Laboratory Collaborative Technology Alliance reported a dual band multispectral imaging focal plane array (FPA). This FPA allowed researchers to look at two infrared (IR) planes at the same time. [6] Because mid-wave infrared (MWIR) and long wave infrared (LWIR) technologies measure radiation inherent to the object and require no external light source, they also are referred to as thermal imaging methods.

The brightness of the image produced by a thermal imager depends on the objects emissivity and temperature. [7]   Every material has an infrared signature that aids in the identification of the object. [8] These signatures are less pronounced in hyperspectral systems (which image in many more bands than multispectral systems) and when exposed to wind and, more dramatically, to rain. [8] Sometimes the surface of the target may reflect infrared energy. This reflection may misconstrue the true reading of the objects’ inherent radiation. [9] Imaging systems that use MWIR technology function better with solar reflections on the target's surface and produce more definitive images of hot objects, such as engines, compared to LWIR technology. [10] However, LWIR operates better in hazy environments like smoke or fog because less scattering occurs in the longer wavelengths. [7] Researchers claim that dual-band technologies combine these advantages to provide more information from an image, particularly in the realm of target tracking. [6]

For nighttime target detection, thermal imaging outperformed single-band multispectral imaging. Citation. Dual band MWIR and LWIR technology resulted in better visualization during the nighttime than MWIR alone. Citation Citation. The US Army reports that its dual band LWIR/MWIR FPA demonstrated better visualizing of tactical vehicles than MWIR alone after tracking them through both day and night.  

Land mine detection

By analyzing the emissivity of ground surfaces, multispectral imaging can detect the presence of underground missiles. Surface and sub-surface soil possess different physical and chemical properties that appear in spectral analysis. [8] Disturbed soil has increased emissivity in the wavelength range of 8.5 to 9.5 micrometers while demonstrating no change in wavelengths greater than 10 micrometers. [6] The US Army Research Laboratory's dual MWIR/LWIR FPA used "red" and "blue" detectors to search for areas with enhanced emissivity. The red detector acts as a backdrop, verifying realms of undisturbed soil areas, as it is sensitive to the 10.4 micrometer wavelength. The blue detector is sensitive to wavelengths of 9.3 micrometers. If the intensity of the blue image changes when scanning, that region is likely disturbed. The scientists reported that fusing these two images increased detection capabilities. [6]

Ballistic missile detection

Intercepting an intercontinental ballistic missile (ICBM) in its boost phase requires imaging of the hard body as well as the rocket plumes. MWIR presents a strong signal from highly heated objects including rocket plumes, while LWIR produces emissions from the missile's body material. The US Army Research Laboratory reported that with their dual-band MWIR/LWIR technology, tracking of the Atlas 5 Evolved Expendable Launch Vehicles, similar in design to ICBMs, picked up both the missile body and plumage. [6]

Space-based imaging

Most radiometers for remote sensing (RS) acquire multispectral images. Dividing the spectrum into many bands, multispectral is the opposite of panchromatic, which records only the total intensity of radiation falling on each pixel. [11] Usually, Earth observation satellites have three or more radiometers. Each acquires one digital image (in remote sensing, called a 'scene') in a small spectral band. The bands are grouped into wavelength regions based on the origin of the light and the interests of the researchers.

Weather forecasting

Modern weather satellites produce imagery in a variety of spectra. [12]

Multispectral imaging combines two to five spectral imaging bands of relatively large bandwidth into a single optical system. A multispectral system usually provides a combination of visible (0.4 to 0.7 µm), near infrared (NIR; 0.7 to 1 µm), short-wave infrared (SWIR; 1 to 1.7 µm), mid-wave infrared (MWIR; 3.5 to 5 µm) or long-wave infrared (LWIR; 8 to 12 µm) bands into a single system. — Valerie C. Coffey [13]

In the case of Landsat satellites, several different band designations have been used, with as many as 11 bands (Landsat 8) comprising a multispectral image. [14] [15] [16] Spectral imaging with a higher radiometric resolution (involving hundreds or thousands of bands), finer spectral resolution (involving smaller bands), or wider spectral coverage may be called hyperspectral or ultraspectral. [17] [16]

Documents and artworks

Multispectral imaging can be employed for investigation of paintings and other works of art. [3] The painting is irradiated by ultraviolet, visible and infrared rays and the reflected radiation is recorded in a camera sensitive in this regions of the spectrum. The image can also be registered using the transmitted instead of reflected radiation. In special cases the painting can be irradiated by UV, VIS or IR rays and the fluorescence of pigments or varnishes can be registered. [18]

Multispectral analysis has assisted in the interpretation of ancient papyri, such as those found at Herculaneum, by imaging the fragments in the infrared range (1000 nm). Often, the text on the documents appears to the naked eye as black ink on black paper. At 1000 nm, the difference in how paper and ink reflect infrared light makes the text clearly readable. It has also been used to image the Archimedes palimpsest by imaging the parchment leaves in bandwidths from 365–870 nm, and then using advanced digital image processing techniques to reveal the undertext with Archimedes' work. [19] Multispectral imaging has been used in a Mellon Foundation project at Yale University to compare inks in medieval English manuscripts. [4]

Multispectral imaging has also been used to examine discolorations and stains on old books and manuscripts. Comparing the "spectral fingerprint" of a stain to the characteristics of known chemical substances can make it possible to identify the stain. This technique has been used to examine medical and alchemical texts, seeking hints about the activities of early chemists and the possible chemical substances they may have used in their experiments. Like a cook spilling flour or vinegar on a cookbook, an early chemist might have left tangible evidence on the pages of the ingredients used to make medicines. [20]

Spectral bands

The wavelengths are approximate; exact values depend on the particular instruments (e.g. characteristics of satellite's sensors for Earth observation, characteristics of illumination and sensors for document analysis):

Spectral band usage

For different purposes, different combinations of spectral bands can be used. They are usually represented with red, green, and blue channels. Mapping of bands to colors depends on the purpose of the image and the personal preferences of the analysts. Thermal infrared is often omitted from consideration due to poor spatial resolution, except for special purposes.

Many other combinations are in use. NIR is often shown as red, causing vegetation-covered areas to appear red.

Classification

Unlike other aerial photographic and satellite image interpretation work, these multispectral images do not make it easy to identify directly the feature type by visual inspection. Hence the remote sensing data has to be classified first, followed by processing by various data enhancement techniques so as to help the user to understand the features that are present in the image.

Such classification is a complex task which involves rigorous validation of the training samples depending on the classification algorithm used. The techniques can be grouped mainly into two types.

Supervised classification makes use of training samples. Training samples are areas on the ground for which there is ground truth, that is, what is there is known. The spectral signatures of the training areas are used to search for similar signatures in the remaining pixels of the image, and we will classify accordingly. This use of training samples for classification is called supervised classification. Expert knowledge is very important in this method since the selection of the training samples and a biased selection can badly affect the accuracy of classification. Popular techniques include the maximum likelihood principle and convolutional neural network. The Maximum likelihood principle calculates the probability of a pixel belonging to a class (i.e. feature) and allots the pixel to its most probable class. Newer convolutional neural network based methods [21] account for both spatial proximity and entire spectra to determine the most likely class.

In case of unsupervised classification no prior knowledge is required for classifying the features of the image. The natural clustering or grouping of the pixel values, i.e. the gray levels of the pixels, are observed. Then a threshold is defined for adopting the number of classes in the image. The finer the threshold value, the more classes there will be. However, beyond a certain limit the same class will be represented in different classes in the sense that variation in the class is represented. After forming the clusters, ground truth validation is done to identify the class the image pixel belongs to. Thus in this unsupervised classification apriori information about the classes is not required. One of the popular methods in unsupervised classification is k-means clustering.

Data analysis software

See also

Related Research Articles

Infrared Form of electromagnetic radiation

Infrared (IR), sometimes called infrared light, is electromagnetic radiation (EMR) with wavelengths longer than those of visible light. It is therefore invisible to the human eye. IR is generally understood to encompass wavelengths from around 1 millimeter (300 GHz) to the nominal red edge of the visible spectrum, around 700 nanometers (430 THz). Longer IR wavelengths (30μm-100μm) are sometimes included as part of the terahertz radiation range. Almost all black-body radiation from objects near room temperature is at infrared wavelengths. As a form of electromagnetic radiation, IR propagates energy and momentum, with properties corresponding to both those of a wave and of a particle, the photon.

Forward-looking infrared Type of thermographic camera

Forward-looking infrared (FLIR) cameras, typically used on military and civilian aircraft, use a thermographic camera that senses infrared radiation.

Remote sensing Acquisition of information at a significant distance from the subject

Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object, in contrast to in situ or on-site observation. The term is applied especially to acquiring information about the Earth and other planets. Remote sensing is used in numerous fields, including geography, land surveying and most Earth science disciplines ; it also has military, intelligence, commercial, economic, planning, and humanitarian applications, among others.

Thermographic camera

A thermographic camera is a device that creates an image using infrared (IR) radiation, similar to a normal camera that forms an image using visible light. Instead of the 400–700 nanometre (nm) range of the visible light camera, infrared cameras are sensitive to wavelengths from about 1,000 nm to about 14,000 nm (14 μm). The practice of capturing and analyzing the data they provide is called thermography.

False color Methods of visualizing information by translating to colors

False color refers to a group of color rendering methods used to display images in color which were recorded in the visible or non-visible parts of the electromagnetic spectrum. A false-color image is an image that depicts an object in colors that differ from those a photograph would show. In this image, colors have been assigned to three different wavelengths that our eyes cannot normally see.

Satellite imagery Imagery of the Earth or another astronomical object taken from an artificial satellite

Satellite images are images of Earth collected by imaging satellites operated by governments and businesses around the world. Satellite imaging companies sell images by licensing them to governments and businesses such as Apple Maps and Google Maps.

Spectral signature Variation of reflectance or emittance of a material with respect to wavelengths

Spectral signature is the variation of reflectance or emittance of a material with respect to wavelengths. The spectral signature of stars indicates the composition of the stellar atmosphere. The spectral signature of an object is a function of the incidental EM wavelength and material interaction with that section of the electromagnetic spectrum.

Spectral imaging Branch of spectroscopy and of photography

Spectral imaging is imaging that uses multiple bands across the electromagnetic spectrum. While an ordinary camera captures light across three wavelength bands in the visible spectrum, red, green, and blue (RGB), spectral imaging encompasses a wide variety of techniques that go beyond RGB. Spectral imaging may use the infrared, the visible spectrum, the ultraviolet, x-rays, or some combination of the above. It may include the acquisition of image data in visible and non-visible bands simultaneously, illumination from outside the visible range, or the use of optical filters to capture a specific spectral range. It is also possible to capture hundreds of wavelength bands for each pixel in an image.

Imaging spectroscopy

In imaging spectroscopy each pixel of an image acquires many bands of light intensity data from the spectrum, instead of just the three bands of the RGB color model. More precisely, it is the simultaneous acquisition of spatially coregistered images in many spectrally contiguous bands.

Normalized difference vegetation index Graphical indicator of remotely sensed live green vegetation

The normalized difference vegetation index (NDVI) is a simple graphical indicator that can be used to analyze remote sensing measurements, often from a space platform, assessing whether or not the target being observed contains live green vegetation.

Hyperspectral imaging Method to create a complete picture of the environment or various objects, each pixel containing a full visible, visible near infrared, near infrared, or infrared spectrum.

Hyperspectral imaging, like other spectral imaging, collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes. There are three general branches of spectral imagers. There are push broom scanners and the related whisk broom scanners, which read images over time, band sequential scanners, which acquire images of an area at different wavelengths, and snapshot hyperspectral imaging, which uses a staring array to generate an image in an instant.

Chemical imaging is the analytical capability to create a visual image of components distribution from simultaneous measurement of spectra and spatial, time information. Hyperspectral imaging measures contiguous spectral bands, as opposed to multispectral imaging which measures spaced spectral bands.

The image fusion process is defined as gathering all the important information from multiple images, and their inclusion into fewer images, usually a single one. This single image is more informative and accurate than any single source image, and it consists of all the necessary information. The purpose of image fusion is not only to reduce the amount of data but also to construct images that are more appropriate and understandable for the human and machine perception. In computer vision, multisensor image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than any of the input images.

Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance Aerial imaging system

Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance, also known by the acronym ARCHER, is an aerial imaging system that produces ground images far more detailed than plain sight or ordinary aerial photography can. It is the most sophisticated unclassified hyperspectral imaging system available, according to U.S. Government officials. ARCHER can automatically scan detailed imaging for a given signature of the object being sought, for abnormalities in the surrounding area, or for changes from previous recorded spectral signatures.

Electro-optical MASINT is a subdiscipline of Measurement and Signature Intelligence, (MASINT) and refers to intelligence gathering activities which bring together disparate elements that do not fit within the definitions of Signals Intelligence (SIGINT), Imagery Intelligence (IMINT), or Human Intelligence (HUMINT).

Operational Land Imager

The Operational Land Imager (OLI) is a remote sensing instrument aboard Landsat 8, built by Ball Aerospace & Technologies. Landsat 8 is the successor to Landsat 7 and was launched on February 11, 2013.

Multispectral remote sensing is the collection and analysis of reflected, emitted, or back-scattered energy from an object or an area of interest in multiple bands of regions of the electromagnetic spectrum. Subcategories of multispectral remote sensing include hyperspectral, in which hundreds of bands are collected and analyzed, and ultraspectral remote sensing where many hundreds of bands are used. The main purpose of multispectral imaging is the potential to classify the image using multispectral classification. This is a much faster method of image analysis than is possible by human interpretation.

Vegetation Index

A Vegetation Index (VI) is a spectral transformation of two or more bands designed to enhance the contribution of vegetation properties and allow reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations.

Remote sensing (geology) Remote sensing used in the geological sciences as a data acquisition method

Remote sensing in geology is remote sensing used in the geological sciences as a data acquisition method complementary to field observation, because it allows mapping of geological characteristics of regions without physical contact with the areas being explored. About one-fourth of the Earth's total surface area is exposed land where information is ready to be extracted from detailed earth observation via remote sensing. Remote sensing is conducted via detection of electromagnetic radiation by sensors. The radiation can be naturally sourced, or produced by machines and reflected off of the Earth surface. The electromagnetic radiation acts as an information carrier for two main variables. First, the intensities of reflectance at different wavelengths are detected, and plotted on a spectral reflectance curve. This spectral fingerprint is governed by the physio-chemical properties of the surface of the target object and therefore helps mineral identification and hence geological mapping, for example by hyperspectral imaging. Second, the two-way travel time of radiation from and back to the sensor can calculate the distance in active remote sensing systems, for example, Interferometric synthetic-aperture radar. This helps geomorphological studies of ground motion, and thus can illuminate deformations associated with landslides, earthquakes, etc.

Land cover maps are tools that provide vital information on the Earth's land use and cover patterns. They aide policy development, urban planning, forest and agricultural monitoring etc.

References

  1. ↑ R.A. Schowengerdt. Remote sensing: Models and methods for image processing, Academic Press, 3rd ed., (2007)
  2. ↑ "13. Multispectral Image Processing | The Nature of Geographic Information". www.e-education.psu.edu. Retrieved 2019-11-14.
  3. 1 2 Baronti, A. Casini, F. Lotti, and S. Porcinai, Multispectral imaging system for the mapping of pigments in works of art by use of principal-component analysis, Applied Optics Vol. 37, Issue 8, pp. 1299–1309 (1998)
  4. 1 2 Weiskott, Eric. "Multispectral Imaging and Medieval Manuscripts." In The Routledge research companion to digital medieval literature. Boyle, Jennifer E., and Helen J. Burgess. London: Routledge. Pp. 186–96.
  5. ↑ Hagen, Nathan; Kudenov, Michael W. (2013). "Review of snapshot spectral imaging technologies". Optical Engineering. 52 (9): 090901. Bibcode:2013OptEn..52i0901H. doi: 10.1117/1.OE.52.9.090901 .
  6. 1 2 3 4 5 Goldberg, A.; Stann, B.; Gupta, N. (July 2003). "Multispectral, Hyperspectral, and Three-Dimensional Imaging Research at the U.S. Army Research Laboratory" (PDF). Proceedings of the International Conference on International Fusion [6th]. 1: 499–506.
  7. 1 2 "Primer on IR theory". Opto Engineering. Retrieved 2018-08-15.
  8. 1 2 3 Makki, Ihab; Younes, Rafic; Francis, Clovis; Bianchi, Tiziano; Zucchetti, Massimo (2017-02-01). "A survey of landmine detection using hyperspectral imaging". ISPRS Journal of Photogrammetry and Remote Sensing. 124: 40–53. Bibcode:2017JPRS..124...40M. doi:10.1016/j.isprsjprs.2016.12.009. ISSN   0924-2716.
  9. ↑ Li, Ning; Zhao, Yongqiang; Pan, Quan; Kong, Seong G. (2018-06-25). "Removal of reflections in LWIR image with polarization characteristics". Optics Express. 26 (13): 16488–16504. Bibcode:2018OExpr..2616488L. doi: 10.1364/OE.26.016488 . ISSN   1094-4087. PMID   30119479.
  10. ↑ Nguyen, Chuong; Havlicek, Joseph; Fan, Guoliang; Caulfield, John; Pattichis, Marios (November 2014). "Robust dual-band MWIR/LWIR infrared target tracking". 2014 48th Asilomar Conference on Signals, Systems and Computers: 78–83. doi:10.1109/ACSSC.2014.7094401. ISBN   978-1-4799-8297-4. S2CID   9071883.
  11. ↑ "3.1.1. Multispectral and panchromatic images". STARS project. Retrieved 14 May 2018.
  12. ↑ https://doi.org/10.1175/1520-0450(2001)040<2115:REFACO>2.0.CO;2
  13. ↑ Coffey, Valerie C. (1 April 2012). "Multispectral Imaging Moves into the Mainstream". Optics and Photonics News. 23 (4): 18. doi:10.1364/OPN.23.4.000018 . Retrieved 14 May 2018.
  14. ↑ "What are the band designations for the Landsat satellites?". U.S. Geological Survey. Archived from the original on January 22, 2017. Retrieved April 25, 2018.
  15. ↑ Grolier, Maurice J.; Tibbitts Jr., G. Chase; Ibrahim, Mohammed Mukred (1984). A qualitative appraisal of the hydrology of the Yemen Arab Republic from Landsat images Water Supply Paper 1757-P By. U.S. G.P.O. p. 19. Retrieved 14 May 2018.
  16. 1 2 Tatem, Andrew J.; Goetz, Scott J.; Hay, Simon I. (2008). "Fifty Years of Earth-observation Satellites". American Scientist. 96 (5): 390–398. doi:10.1511/2008.74.390. PMC   2690060 . PMID   19498953.
  17. ↑ "Multispectral vs Hyperspectral Imagery Explained". GIS Geography. 2014-07-23. Retrieved Feb 16, 2018.
  18. ↑ Multispectral imaging at ColourLex
  19. ↑ "Multi-spectral imaging of the Archimedes Palimpsest". The Archimedes Palimpsest Project. Retrieved 17 September 2015.
  20. ↑ Avril, Tom (May 14, 2018). "Scans reveal secrets of medieval 'Harry Potter' book and medical texts at Penn". The Philadelphia Inquirer. Retrieved 14 May 2018.
  21. ↑ Ran, Lingyan; Zhang, Yanning; Wei, Wei; Zhang, Qilin (2017-10-23). "A Hyperspectral Image Classification Framework with Spatial Pixel Pair Features". Sensors. 17 (10): 2421. doi:10.3390/s17102421. PMC   5677443 . PMID   29065535.
  22. ↑ Biehl, Larry; Landgrebe, David (2002-12-01). "MultiSpec: a tool for multispectral—hyperspectral image data analysis". Computers & Geosciences. 28 (10): 1153–1159. Bibcode:2002CG.....28.1153B. doi:10.1016/S0098-3004(02)00033-X . Retrieved 2017-04-28.
  23. ↑ Jordan, Johannes; Angelopoulou, Elli (2010). Gerbil - A Novel Software Framework for Visualization and Analysis in the Multispectral Domain. Vision. Vol. Modeling. doi:10.2312/PE/VMV/VMV10/259-266. ISBN   9783905673791.

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