spectralimaging

Spectral imaging is an imaging modality that captures both spatial information and spectral information for each pixel, producing a data cube with two spatial dimensions and one spectral dimension. Unlike conventional color cameras that record only a few broad bands (for example red, green, and blue), spectral imaging records many narrow spectral bands across a range of wavelengths, enabling material identification, composition analysis, and detection of subtle features not visible in standard images.

Technologies for spectral imaging include push-broom (line-scanning), whiskbroom (point-scanning), and snapshot (imaging spectrometer) approaches. Each uses a dispersive element such as a prism or diffraction grating to separate wavelengths, often in combination with filters or tunable elements, and a detector array suitable for the chosen spectral range (for example CCD/CMOS for visible/NIR or InGaAs/MCT detectors for infrared). Spectral coverage can span visible to near-infrared, short-wave infrared, and beyond, depending on the detector and optics used.

Data processing and analysis in spectral imaging involve calibrations (radiometric, geometric, and spectral) and operations on the data cube, such as spectral unmixing, end-member extraction, classification, and anomaly or target detection. These steps require specialized algorithms and sometimes spectral libraries or reference signatures.

Applications are widespread and include remote sensing and Earth observation, agriculture and forestry, mineralogy and surveillance of industrial materials, food quality control, medical imaging (e.g., hyperspectral endoscopy), cultural heritage conservation, and astronomy. In practice, spectral imaging encompasses both hyperspectral imaging (many narrow bands) and multispectral imaging (fewer bands), serving as an umbrella term for these techniques. Limitations include large data volumes, instrument cost and complexity, and the need for robust calibration and analysis.