Principles of Spectral Imaging
Spectral imaging includes capturing images at various wavelengths, starting from ultraviolet (UV) to near-infrared (NIR) and occasionally reaching further. Every pixel in the picture holds a spectral signature that demonstrates its reflective or emitting properties throughout the spectrum. Through the examination of these spectral signatures, experts and professionals are able to detect distinct characteristics and trends that cannot be seen with the human eye.
Typically, spectral imaging systems include a light source, optical filters or sensors, and specialized software for analyzing data. The target object is lit up by the light source, and the filters or sensors detect the light that is reflected or emitted at various wavelengths. The captured data is processed by the software to produce spectral images and extract valuable insights.
What is Spectral imaging in Image processing?
Spectral imaging is method that captures and analyzes images across a range of wavelengths in the electromagnetic spectrum. In this article, we’ll uncover the underlying principles of spectral imaging and examine its broad-ranging applications across different sectors.
Table of Content
- Principles of Spectral Imaging
- Types of Spectral Imaging in Image Processing
- Multispectral Imaging
- Hyperspectral Imaging
- Applications of Spatial Imaging
While conventional imaging focuses solely on the light intensity at one wavelength, spectral imaging gathers spectral information for every pixel in the image. This capability allows for the retrieval of crucial details about the composition, structure, and characteristics of the subjects in the images.
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