A spectrometer according to one aspect may include a plurality of light sources configured to emit light to a target object, a plurality of wavelength controllers installed on one surface of each of the plurality of light sources and configured to adjust a peak wavelength band of each of the light sources, and a detection unit configured to detect light returning from the target object.

A spectral sensing system includes an array of sampling optical elements (e.g. filters) and an array of optical sensors, and a deep learning model used to process output of the optical sensor array. The deep learning model is trained using a training dataset which includes, as training inputs, optical sensor output generated by shining each one of multiple different light waves with known spectra on the sampling optical element array for multiple different times, each time from a known angle of incidence, and as training labels, the known spectra of the multiple light waves. The trained deep learning model can be used to process output of the optical sensor array when light having an unknown spectrum is input on the sampling optical element array from unknown angles, to measure the spectrum of the light. The spectral sensing system can also be used to form a hyperspectral imaging system.

A spectral image capturing method using a spectral camera control device installed in aircraft, the method comprising:

a) setting an exposure time of the spectral camera so that a current exposure time is determined (S2),
b) determining whether or not either an amount of attitude change or an amount of position change of the spectral camera per exposure time exceeds a predetermined threshold based on a spatial resolution of the spectral camera (S4),
c1) when exceeding the predetermined threshold, resetting the current exposure time to be shorter (S5),
c2) when not exceeding the predetermined threshold, not resetting the current exposure time to be shorter, and
d) capturing a spectral image in a snapshot mode with the spectral camera using the reset exposure time,
wherein when the transmission wavelength of the liquid crystal tunable filter is switched while the aircraft is in a stationary flight, steps b) to d) are repeated.

A means to automate, using fuzzy logic, the classification of spectral power distributions of optical radiation for lighting systems, and more particularly horticultural lighting systems, is presented. After inputting the spectral power distribution of optical radiation from one or more light sources, radial basis function weights for the spectral power distribution are determined and fuzzified preparatory to fuzzy logic classification. Fuzzy if-then rules are then applied, and an aggregate of the rule votes from the fuzzy if-then rules applied is used to classify the spectral power distribution. The system utilizes a spectral sensor, a fuzzifier module, a fuzzy rule database, fuzzy rule engine, an output fuzzifier module, and a means of displaying the spectral power distribution classification.

A spectral sensing system includes an array of sampling optical elements (e.g. filters) and an array of optical sensors, and a deep learning model used to process output of the optical sensor array. The deep learning model is trained using a training dataset which includes, as training inputs, optical sensor output generated by shining each one of multiple different light waves with known spectra on the sampling optical element array for multiple different times, each time from a known angle of incidence, and as training labels, the known spectra of the multiple light waves. The trained deep learning model can be used to process output of the optical sensor array when light having an unknown spectrum is input on the sampling optical element array from unknown angles, to measure the spectrum of the light. The spectral sensing system can also be used to form a hyperspectral imaging system.

A method for reconstructing a hyperspectral image and a system therefor are provided. The method includes obtaining a dispersion model for dispersion created by a prism included in a camera, the prism including no coded aperture, and reconstructing a hyperspectral image corresponding to a captured image based on the dispersion model.

[Problem] To provide a spectral camera control device, a spectral camera control program, a spectral camera control system, an aircraft equipped with said system, and a spectral image capturing method, with which it is possible for each of a spatial resolution and an exposure time for spectral image capture to be set arbitrarily, and with which spatial distortion and displacement of the spectral image can be suppressed. [Solution] This spectral camera control device is installed together with a spectral camera 3 provided with a liquid crystal tunable filter 33 in an aircraft 1 capable of stationary flight, and causes the spectral camera 3 to capture an image in a snapshot mode each time the transmission wavelength of the liquid crystal tunable filter 33 is switched while the aircraft 1 is in stationary flight.

The disclosed embodiments include thin film multivariate optical element and detector combinations, thin film optical detectors, and downhole optical computing systems. In one embodiment, a thin film multivariate optical element and detector combination includes at least one layer of multivariate optical element having patterns that manipulate at least one spectrum of optical signals. The thin film multivariate optical element and detector combination also includes at least one layer of detector film that converts optical signals into electrical signals. The thin film optical detector further includes a substrate. The at least one layer of multivariate optical element and the at least one layer of detector film are deposited on the substrate.

According to the embodiment, a processing apparatus includes a processor. The processor is configured to: cause a plurality of different projection lights to be projected onto an object, the projection lights respectively including a plurality of wavelengths different from each other; cause intensity values of the plurality of wavelengths to be acquired from the object for each of the projection lights, as intensity values of detection signals having no information regarding position at a stage of processing signals; transform numerical values of the intensity values of the detection signals using independent parameters for each of wavelengths to first transformed signals corresponding to the plurality of wavelengths from the object; and cause an image of the object to be acquired by the first transformed signals corresponding to the plurality of wavelengths from the object and signals related to the projection lights.

In a standard process for determining an unknown sample, fluorescent substances are determined from respective fluorescence characteristics and model coefficients are calculated from spectrum ranges of the fluorescence characteristics of the determined fluorescent substances. An unknown sample is measured after reading of the model coefficients, whereby a target value of the unknown sample is obtained.