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 ultraviolet). 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. 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.
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.
True-color uses only red, green, and blue channels, mapped to their respective colors. As a plain color photograph, it is good for analyzing man-made objects, and is easy to understand for beginner analysts. Green–red–infrared, where the blue channel is replaced with near infrared, is used for vegetation, which is highly reflective in near IR; it then shows as blue. This combination is often used to detect vegetation and camouflage. Blue–NIR–MIR, where the blue channel uses visible blue, green uses NIR (so vegetation stays green), and MIR is shown as red. Such images allow the water depth, vegetation coverage, soil moisture content, and the presence of fires to be seen, all in a single image. Many other combinations are in use. NIR is often shown as red, causing vegetation-covered areas to appear red.
Typical 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):
Blue, 450–515/520 nm, is used for atmosphere and deep water imaging, and can reach depths up to 150 feet (50 m) in clear water. Green, 515/520–590/600 nm, is used for imaging vegetation and deep water structures, up to 90 feet (30 m) in clear water. Red, 600/630–680/690 nm, is used for imaging man-made objects, in water up to 30 feet (9 m) deep, soil, and vegetation. Near infrared (NIR), 750–900 nm, is used primarily for imaging vegetation. Mid-infrared (MIR), 1550–1750 nm, is used for imaging vegetation, soil moisture content, and some forest fires. Far-infrared (FIR), 2080–2350 nm, is used for imaging soil, moisture, geological features, silicates, clays, and fires. Thermal infrared, 10,400–12,500 nm, uses emitted instead of reflected radiation to image geological structures, thermal differences in water currents, fires, and for night studies. Radar and related technologies are useful for mapping terrain and for detecting various objects.
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 techniques Unsupervised classification techniques 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 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 a priori information about the classes is not required. One of the popular methods in unsupervised classification is k-means clustering.
Data analysis software MicroMSI is endorsed by the NGA. Opticks is an open-source remote sensing application. Multispec is freeware multispectral analysis software. Gerbil is open source multispectral visualization and analysis software.
Applications
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