Rancang Bangun Perangkat Kalibrasi Sistem Pencitraan Spektral Berbasis Kamera DSLR
Keywords:
calibration, spectral, data cube, imaging, DSLR cameraAbstract
Spectral imaging systems are an arrangement of optical components to measure and map the electromagnetic spectrum into a three-dimensional image known as a data cube. This research aims to develop a calibration device to capture data cube measurements from the visible light spectrum to near-infrared in a single image capture. The calibration process is intended to determine the transformation matrix for the optical component setup, which is arranged based on the Computed Tomography Imaging Spectrometry (CTIS) method. This study successfully implemented a calibration system using an acrylic device consisting of four parts. First, the holding camera, an acrylic structure that follows the camera body’s contours, keeps the camera stationary. Second, the spatial dot, which consists of spatial points that remain stationary relative to each other, provides known spatial information. Third, the guider, a board that connects the holding camera to the spatial dot, ensures that both components are aligned on the same axis. Fourth, the LED Board is a collection of LEDs with known wavelengths. Based on the calibration results, this system produces a data cube consisting of 16 bands with a wavelength range of 675–735 nm, a spectral resolution of 15 nm, and a spatial size of 20 × 20 pixels.
References
Schaepman, E. M. (2009): Imaging Spectrometers, 166 – 178 dalam Warner, A. T., Nellis, D. M., dan Foody, M. G., The SAGA Handbook of Remote Sensing, 568 hal, SAGE Publication Ltd, London.
Goetz, H. F. A. (2009): Three decades of hyperspectral remote sensing of the Earth: a personal view, Remote Sens. of Environment, 113, S5 – S16.
Goetz, H. F. A., Vane, G., Solomon, E. J., dan Rock, N. B. (1985): Imaging spectrometry for Earth remote sensing, Sci., 228, 1147 – 1153.
Liao, L., Jarecke, J. P., Gleichauf, D., dan Hedman, T. (2000): Performance characterization of the Hyperion imaging spectrometer instrument, Int. Symp. On Opt. Sci. and Technol., San Diego, 264 – 275.
Datt, B., McVicar, R. T., Niel, G. T., Jupp, B. L. D., dan Pearlman, S. J. (2003): Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes, IEEE Trans. Geosci. Remote Sens., 41, 1246 – 1259.
Lu, G., dan Fei, B. (2014): Medical hyperspectral imaging: a review, J. of Biomed. Optics, 19.
Renkoski, E. T., Hatch, D. K., dan Utzinger, U. (2012): Wide-field spectral imaging of human ovary autofluorescence and oncologic diagnosis via previously collected probe data, J. of Biomed. Optics, 17.
Gat, N. (2000): Imaging spectroscopy using tunable filter: a review, Proc. SPIE Wavelet Applicat. VII, Florida, 50 – 64.
Panasyuk, V. S., Yang, S., Faller, V. D., Ngo, D., Lew, A. R., Freeman, E. J., dan Rogers, E. A. (2007): Medical hyperspectral imaging to facilitate residual tumor identification during surgery, Cancer Biology Therapy, 6, 439 – 446.
Habel, R., Kudenov, M., dan Wimmer, M. (2012): Practical spectral photography, Comput. Graph. Forum, 31, 449 – 458.
Green, J. (2003): Optical spectroscopy, 279 – 294 dalam Gauglitz, G., dan Vo-Dinh, T., Handbook of Spectroscopy, 538 hal., WILEY-VCH Gmbh & Co. KGaA, Weinheim.
Barbe, F. D. (1975): Imaging devices using the charge coupled concept, Proc. of the IEEE, 63, 38 – 66.
Garini, Yuval., Young, T. I., dan McNamara, G. (2006): Spectral imaging: principles and application, The J. of the Int. Soc. for Analytical Cytology, 69, 735 – 747.
Shaw, A. G., dan Burke, K. H. (2003): Spectral imaging for remote sensing, Lincoln Laboratory J., 14, 3 – 28.
Vane, G., Goetz, H. F. A., dan Wellman, B. J. (1984): Airborne imaging spectrometer: a new tool for remote sensing, IEEE Trans. Geosci. Remote Sens., 22, 546 – 549.
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