What Is a Multispectral Image?

A multispectral image is an image that contains many bands, sometimes only three bands (color images are an example), but sometimes many more, even hundreds. Each band is a grayscale image that represents the brightness of the scene based on the sensitivity of the sensor used to generate the band. In such an image, each pixel is associated with a string of values in a different band of pixels, that is, a vector. This string is called the spectral signature of the pixel. ^[1]

1. Replace the current band with other bands that are irrelevant or independent; this problem is particularly relevant for remote sensing applications, but in general image processing,

Multispectral image processing includes optical processing and digital processing. Optical processing includes ordinary photographic processing, optical geometric correction, layered overlay exposure, false color synthesis, electronic grayscale segmentation, related mask processing, and physical optical processing. Digital processing is the use of a computer system to correct image radiation and geometric errors, feature enhancement, image registration, feature classification, and target feature extraction on the original information. Compared with the two, digital processing is more important. Digital processing methods are flexible, fast, reproducible, and can produce high geometric accuracy and high-quality images. In most cases, the image signal should be digitized and then processed in a computer.

The multi-spectral image information processing system includes the following parts: The main computer system and peripheral equipment, including minicomputers, medium-sized computers or high-end microcomputers; peripheral equipments are mainly tape drives, printers, disk drives, etc. Image input and output equipment. Among them, input devices mainly include high-density digital tape drives, cameras, drum scanning devices, etc .; output devices mainly include high-resolution color monitors, various image reproduction devices such as cameras, color printers, plotters, and flying spot scanners. , Electron beam imager, etc. General and special image processing software. ^[2]

Multi-spectral image construction grayscale image

In the simplest method, select one of the image bands and use it as a grayscale image. However, this method loses significant image information. For example, some edges may be more prominent in one band than others. Select only one edge with some images that may be missing. One way to avoid this problem is to average the gray values of all bands. In fact, this is a fairly widely used method by many image processing packages. However, the grayscale image generated in this way is not optimal in the sense that it contains the largest amount of image information. ^[1]

Constructing a Single Band from Multispectral Images

A grayscale image carries information by the relative grayscale value of its pixels. The more contrast an image has, the more information it carries. If the band of the image is replaced by its uncorrelated component, the maximum uncorrelated band of the corresponding pixel value is diffused and contains more information than any other band. Therefore, generating a band with maximum contrast is a by-product of generating uncorrelated image bands. This can be achieved using principal component analysis (PCA). ^[1]

Multispectral image restoration method

In general, blur, especially motion blur, is expected to affect all channels equally. Therefore, a recovery filter derived from one channel will be used for all channels, and it should be used to recover each channel separately, unless there is reason to think that different channels have different levels of noise. If this is indeed the case, the components of the filter used to remove noise (Wiener filter), or the constraint matrix inverse filter will have to be designed differently for different channels. ^[1]

Multispectral image segmentation method

The most commonly used multispectral image segmentation method is to treat the value of each pixel in different bands as the characteristics of the pixel. In other words, for an L-band image, consider an LD space in which the value of the pixel in a band is measured along each axis. This is the spectral histogram of the image. For a 3-band image, this is called the color histogram of the image. ^[1]

Multi-spectral image edge extraction

Edge detection is often performed using the average band of a multispectral image. This is a quick and easy method, but it is not the best method.

Edge detection is best performed in the first principal component of a multispectral image because it has the largest contrast ratio over all original image bands.

However, if you want to avoid the calculation of the first principal component, you can also use the gradient magnitude for each band to estimate, and then select the largest gradient value of all bands for each pixel. In this way, a combined gradient map is obtained, which includes the maximum contrast of a pixel with its neighbors in all bands. Subsequent processing of this gradient map may be performed with reference to a conventional gradient map of an amplitude image. ^[1]