This PDF file contains the front matter associated with SPIE Proceedings Volume 9860, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

Hyperspectral images allow obtaining large amounts of information about the surface of the scene that is captured by the sensor. Using this information and a set of complex classification algorithms is possible to determine which material or substance is located in each pixel. The HELICoiD (HypErspectraL Imaging Cancer Detection) project is a European FET project that has the goal to develop a demonstrator capable to discriminate, with high precision, between normal and tumour tissues, operating in real-time, during neurosurgical operations. This demonstrator could help the neurosurgeons in the process of brain tumour resection, avoiding the excessive extraction of normal tissue and unintentionally leaving small remnants of tumour. Such precise delimitation of the tumour boundaries will improve the results of the surgery. The HELICoiD demonstrator is composed of two hyperspectral cameras obtained from Headwall. The first one in the spectral range from 400 to 1000 nm (visible and near infrared) and the second one in the spectral range from 900 to 1700 nm (near infrared). The demonstrator also includes an illumination system that covers the spectral range from 400 nm to 2200 nm. A data processing unit is in charge of managing all the parts of the demonstrator, and a high performance platform aims to accelerate the hyperspectral image classification process. Each one of these elements is installed in a customized structure specially designed for surgical environments. Preliminary results of the classification algorithms offer high accuracy (over 95%) in the discrimination between normal and tumour tissues.

Hyperspectral imaging (HSI) is an exciting and rapidly expanding area of instruments and technology in passive remote sensing. Due to quickly changing applications, the instruments are evolving to suit new uses and there is a need for consistent definition, testing, characterization and calibration. This paper seeks to outline a broad prescription and recommendations for basic specification, testing and characterization that must be done on Visible Near Infra-Red grating-based sensors in order to provide calibrated absolute output and performance or at least relative performance that will suit the user’s task. The primary goal of this paper is to provide awareness of the issues with performance of this technology and make recommendations towards standards and protocols that could be used for further efforts in emerging procedures for national laboratory and standards groups.

Imaging spectroscopy is an important area with many applications in surveillance, agriculture and medicine. The disadvantage of conventional spectroscopy techniques is that they collect the whole datacube. In contrast, compressive spectral imaging systems capture snapshot compressive projections, which are the input of reconstruction algorithms to yield the underlying datacube. Common compressive spectral imagers use coded apertures to perform the coded projections. The coded apertures are the key elements in these imagers since they define the sensing matrix of the system. The proper design of the coded aperture entries leads to a good quality in the reconstruction. In addition, the compressive measurements are prone to saturation due to the limited dynamic range of the sensor, hence the design of coded apertures must consider saturation. The saturation errors in compressive measurements are unbounded and compressive sensing recovery algorithms only provide solutions for bounded noise or bounded with high probability. In this paper it is proposed the design of uniform adaptive grayscale coded apertures (UAGCA) to improve the dynamic range of the estimated spectral images by reducing the saturation levels. The saturation is attenuated between snapshots using an adaptive filter which updates the entries of the grayscale coded aperture based on the previous snapshots. The coded apertures are optimized in terms of transmittance and number of grayscale levels. The advantage of the proposed method is the efficient use of the dynamic range of the image sensor. Extensive simulations show improvements in the image reconstruction of the proposed method compared with grayscale coded apertures (UGCA) and adaptive block-unblock coded apertures (ABCA) in up to 10 dB.

MWIR imaging spectrometer is promising in detecting spectral signature of high temperature object such as jet steam, guided missile and explosive gas. This paper introduces an optical design of a MWIR imaging spectrometer with a cold slit sharply reducing the stray radiation from exterior environment and interior structure. The spectrometer is composed of a slit, a spherical prism as disperser, two concentric spheres and a correction lens. It has a real entrance pupil to match the objective and for setting the infrared cold shield near the slit and a real exit pupil to match the cold shield of the focal plane array (FPA). There are two cooled parts, one includes the aperture stop and slit, and the other is the exit pupil and the FPA with two specially positioned cooled shields. A detailed stray radiation analysis is represented which demonstrates the outstanding effect of this system in background radiation restraint.

Hyperspectral imaging (HSI) device users often require both high spectral resolution, on the order of 1 nm, and high light-gathering power. A wide entrance slit assures reasonable étendue but degrades spectral resolution. Spectrometers built using High Throughput Virtual Slit™ (HTVS) technology optimize both parameters simultaneously. Two remote sensing use cases that require high spectral resolution are discussed. First, detection of atmospheric gases with intrinsically narrow absorption lines, such as hydrocarbon vapors or combustion exhaust gases such as NOx and CO₂. Detecting exhaust gas species with high precision has become increasingly important in the light of recent events in the automobile industry. Second, distinguishing reflected daylight from emission spectra in the visible and NIR (VNIR) regions is most easily accomplished using the Fraunhofer absorption lines in solar spectra. While ground reflectance spectral features in the VNIR are generally quite broad, the Fraunhofer lines are narrow and provide a signature of intrinsic vs. extrinsic illumination.

The High Throughput Virtual Slit enables higher spectral resolution than is achievable with conventional spectrometers by manipulating the beam profile in pupil space. By reshaping the instrument pupil with reflective optics, HTVS-equipped instruments create a tall, narrow image profile at the exit focal plane, typically delivering 5X or better the spectral resolution achievable with a conventional design.

Spectral-temporal compressive imaging is a technique that allows to sense spatio-spectro-temporal information, known as spectral video from a single low-framerate monochrome measurement. Several optical architectures have been recently developed to capture the spectral features of a dynamic scene or spectral video based on the compressive sensing framework. These spectral-temporal compressive architectures are principally composed by four elements: a main lens, a coded aperture, a dispersive element and an FPA detector. Traditionally, these acquisition systems use block-unblock coded apertures that either block the light rays in the optical path or allow them to pass through. However, the modulation produced by the block-unblock coded apertures is wavelength independent, thus ignoring the highly correlated structure of the spectral information in a dynamic scene. In this work, the block-unblock coded apertures are replaced by colored coded apertures, whose pixels represent a set of specific optical filters such as low, high or band pass filters that modulate particular wavelengths of the scene. An analysis of the variations in the colored coded aperture pattern that allows to obtain improvements in PSNR compared with the block-unblock coded apertures is presented. Simulation results show an improvement up to 2 dB in the reconstruction quality with respect to the block-unblock coded apertures.

This research work was designed to evaluate the suitability and applicability of hyperspectral radiometry technology for robustly detecting adulterants in diary milk. The most common milk adulterants are (a) soda, (b) urea, (c) water and (d) detergents. The main contribution of this paper is to build a mathematical model to enable quantifying the degree of common adulterants present in milk. Data was collected using a portable spectroradiometer (Eko MS-720) which measures the spectral irradiance in the range from visible to near-infrared irradiance (350 nm 1050 nm) using samples of milk contaminated with four different adulterants (soda, urea, water and detergent) with known degree of contamination deliberately added in milk. In this study, we used the data in the range of 350 - 1050 nm to identify spectral signatures of different adulterants with different degree of concentration. Data cleansing, in the form of pre-processing was followed by machine learning techniques to create a model to capture the adulterants and also the degree of adulteration. Linear regression along with wrapper subset eval as attribute evaluator and best first search as search option was found to create the best model. Root Mean Square Error (RMSE) and Correlation Coefficient (CC) metrics were used to select the best model. The best model for detecting the degree of adulteration due to soda, urea, water and detergent in milk was found to have an RMSE of 0.027, 0.0069, 0.0382 and 0.0281 respectively while CC was 0.9919, 0.9997, 0.9887 and 0.9938 respectively. The preliminary experimental results demonstrate the effective use of spectroradiometer and machine learning technique in reliably detecting adulterants in milk.

Meat is an important food item in human diet. Its production and consumption has greatly increased in the last decades with the development of economies and improvement of peoples' living standards. However, most of the traditional methods for evaluation of meat quality are time-consuming, laborious, inconsistent and destructive to samples, which make them not appropriate for a fast-paced production and processing environment. Development of innovative and non-destructive optical sensing techniques to facilitate simple, fast, and accurate evaluation of quality are attracting increasing attention in the food industry. Hyperspectral imaging is one of the promising techniques. It integrates the combined merits of imaging and spectroscopic techniques. This paper provides a comprehensive review on recent advances in evaluation of the important quality attributes of meat including color, marbling, tenderness, pH, water holding capacity, and also chemical composition attributes such as moisture content, protein content and fat content in pork, beef and lamb. In addition, the future potential applications and trends of hyperspectral imaging are also discussed in this paper.

Push-broom hyperspectral imaging systems are increasingly used for various medical, agricultural and military purposes. The acquired images contain spectral information in every pixel of the imaged scene collecting additional information about the imaged scene compared to the classical RGB color imaging. Due to the misalignment and imperfections in the optical components comprising the push-broom hyperspectral imaging system, variable spectral and spatial misalignments and blur are present in the acquired images. To capture these distortions, a spatially and spectrally variant response function must be identified at each spatial and spectral position. In this study, we propose a procedure to characterize the variant response function of Short-Wavelength Infrared (SWIR) push-broom hyperspectral imaging systems in the across-track and along-track direction and remove its effect from the acquired images. A custom laser-machined spatial calibration targets are used for the characterization. The spatial and spectral variability of the response function in the across-track and along-track direction is modeled by a parametrized basis function. Finally, the characterization results are used to restore the distorted hyperspectral images in the across-track and along-track direction by a Richardson-Lucy deconvolution-based algorithm. The proposed calibration method in the across-track and along-track direction is thoroughly evaluated on images of targets with well-defined geometric properties. The results suggest that the proposed procedure is well suited for fast and accurate spatial calibration of push-broom hyperspectral imaging systems.