Browsing by Subject "spectrum"

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  • Chrbolková, Katerina; Kohout, Tomas; Ďurech, Josef (2019)
    Higher magnetic field in lunar swirls is believed to deflect majority of incoming charged particles away from the lunar-swirl surfaces. As a result, space weathering inside and outside swirls should be different. We wanted to evaluate these differences, therefore we have examined seven swirl areas on the Moon (four mare and three highland swirls). We applied the Modified Gaussian Model to statistical sets of the Moon Mineralogy Mapper spectra. Using Principal Component Analysis (PCA), we were able to distinguish the old (weathered) material from both the fresh crater and swirl materials. The swirls did not follow the same behavior as the fresh material, nor were they fully separable. Additionally, we could distinguish between the mare and highland swirls (mare/highland dichotomy) based on the PCA and histogram plots of the albedo and strength of the 1000-nm absorption band. The mare/highland dichotomy can partially be caused by different FeO content in maria and highlands, which points to the existence of a threshold value that changes the spectral evolution due to space weathering. Slope behavior seemed to be dependent on whether the swirl was on the near- or far-side of the Moon, likely due to shielding of lunar nearside by Earth's magnetotail. Our results thus favor the solar wind stand-off hypothesis in combination with the fine dust transport hypothesis and point to the fact that micrometeoroid impacts generally do not reproduce the same weathering trends as all the space weathering effects together.
  • Toivonen, Mikko Evert; Talvitie, Topi; Rajani, Chang; Klami, Arto (2021)
    Accurate color determination in variable lighting conditions is difficult and requires special devices. We considered the task of extracting the visible light spectrum using ordinary camera sensors, to facilitate low-cost color measurements using consumer equipment. The approach uses a diffractive element attached to a standard camera and a computational algorithm for forming the light spectrum from the resulting diffraction images. We present two machine learning algorithms for this task, based on alternative processing pipelines using deconvolution and cepstrum operations, respectively. The proposed methods were trained and evaluated on diffraction images collected using three cameras and three illuminants to demonstrate the generality of the approach, measuring the quality by comparing the recovered spectra against ground truth measurements collected using a hyperspectral camera. We show that the proposed methods are able to reconstruct the spectrum, and, consequently, the color, with fairly good accuracy in all conditions, but the exact accuracy depends on the specific camera and lighting conditions. The testing procedure followed in our experiments suggests a high degree of confidence in the generalizability of our results; the method works well even for a new illuminant not seen in the development phase.