We present a constrained spectral unmixing method to remove highlight from a single spectral image. In the constrained spectral unmixing method, the constraints have been imposed so that all the fractions of diffuse and highlight reflection sum up to 1 and are positive. As a result, the spectra of the diffuse image are always positive. The spectral power distribution (SPD) of the light source has been used as the pure highlight spectrum. The pure diffuse spectrum of the measured spectrum has been chosen from the set of diffuse spectra. The pure diffuse spectrum has a minimum angle among the angles calculated between spectra from a set of diffuse spectra and the measured spectrum projected onto the subspace orthogonal to the SPD of the light source. The set of diffuse spectra has been collected by an automated target generation program from the diffuse part in the image. Constrained energy minimization in a finite impulse response linear filter has been used to detect the highlight and diffuse parts in the image. Results by constrained spectral unmixing have been compared with results by the orthogonal subspace projection (OSP) method [Proceedings of International Conference on Pattern Recognition (2006), pp. 812-815] and probabilistic principal component analysis (PPCA) [Proceedings of the 4th WSEAS International Conference on Signal Processing, Robotics and Automation (2005), paper 15]. Constrained spectral unmixing outperforms OSP and PPCA in the visual assessment of the diffuse results. The highlight removal method by constrained spectral unmixing is suitable for spectral images.
In this study, we propose a color mixing and color separation method for opaque surface made of the pigments dispersed in filling materials. The method is based on Kubelka-Munk model. Eleven different pigments with seven different concentrations have been used as training sets. The amount of concentration of each pigment in the mixture is estimated from the training sets by using the least-square pseudo-inverse calculation. The result depends on the number and type of pigments selected for calculation. At most we can select all pigments. The combinations resulted with negative concentrations or unusual high concentrations are discarded from the list of candidate combination. The optimal pigment's set and its concentrations are estimated by minimizing the reflectance difference of given reflectance and predicted reflectance.
HySpex ODIN-1024 is a next generation state-of-the-art airborne hyperspectral imaging system developed by Norsk Elektro Optikk AS. Near perfect coregistration between VNIR and SWIR is achieved by employing a novel common fore-optics design and a thermally stabilized housing. Its unique design and the use of state-of-the-art MCT and sCMOS sensors provide the combination of high sensitivity and low noise, low spatial and spectral misregistration (smile and keystone) and a very high resolution (1024 pixels in the merged data products). In addition to its supreme data quality, HySpex ODIN-1024 includes real-time data processing functionalities such as real-time georeferencing of acquired images. It also features a built-in onboard calibration system to monitor the stability of the instrument. The paper presents data and results from laboratory tests and characterizations, as well as results from airborne measurements.
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