The CIE defines the Human Visual response to be in the range of 380nm to 780nm of light and provides weighting tables so that a user can determine a color from a spectral curve by multiplying the instrument measurement at a wavelength by the corresponding Human Visual response weight at that wavelength and then summing this data for all measured wavelengths. These tables are offered in 1nm, 5nm and 10nm intervals. For non-critical measurements larger intervals such as 20nm to 133nm have been employed and use abrided weighting tables. Resolution then refers to how many data points were used in the calculation. Imagine we are measuring two red paint chips and calculating their color. Only Research or National Metrology labs use 1nm weighting so let’s not use this in our discussion. Depending on the instrument used we would end up with 80 or 40 spectral results in the summation for critical color determination and for non-critical measurements one might end up with only 20 or even 3 results to sum.
Spectral Resolution can be further defines as how many measuring points are within the spectral results. If one uses a fixed array detector then how many array elements (pixels) are there in the measured spectrum. Assuming a 400nm range (380nm-780nm) one might have for example 256 or 128 pixels, resulting in either 1.56 nm/pixels or 3.125 nm/pixels.
What Is Resolution?
There are four types of data resolution: spatial, spectral, radiometric and temporal. Many instruments can capture one or two types of resolution simultaneously, yet it is rare to find equipment capable of delivering on all four types. This phenomenon is known as the resolution trade-off. Most instruments measure the most commonly used types of resolutions: spatial and spectral. Together, spatial and spectral resolutions allow scientists to quantitatively measure factors such as color, space and detail.
The Difference Between Spatial and Spectral Resolution
Spatial resolution is what most people commonly think of when they hear the term “resolution.” Resolution refers to the length of one side of a single pixel. The higher the resolution of an image, the more expensive it is to capture and process this image. In equipment such as telescopes and cameras, spatial resolution results from angular resolution. Other instruments such as radar devices, remote sensing equipment and satellite imagery feature sampling layouts that more closely relate to topology and the Earth’s surface.
Comparatively, spectral resolution measures color wavelengths by recording spectral bands. Spectral resolution is determined by the width of each band in a wavelength. The more bands in an image, the more complex the color will be. For example, black and white photographs contain only one wavelength for the color black, while color RGB images contain three bands for red, green and blue. Landsat 8 photographs use 11 total bands to capture images, and their bands have a further distance in between due to their broad wavelength.
Spectral resolution allows you to distinguish broad wavelength ranges that require fine wavelength range comparisons. Ultimately, spatial resolution helps scientists analyze images in great visual detail, while spectral resolution can imbue images with true-to-life color. Both spatial and spectral resolution are essential for thoroughly analyzing test samples in product quality assurance procedures, medical sample testing and forensic sample testing.
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