Geometric calibration and radiometric correction of the maia multispectral camera

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 42, Issue 3W3, 2017, Pages 149-156

  Nocerino, E ^a   Dubbini, M ^b   Menna, F ^a   Remondino, F ^a   Gattelli, M ^c   Covi, D ^d  

^a FONDAZIONE BRUNO KESSLER   (Italy)

^b UNIVERSITY OF BOLOGNA   (Italy)

^c SAL Engineering srl   (Italy)

^d Eoptis srl   (Italy)

Author keywords

Geometrical Calibration; MAIA; Multispectral; Radiometric Calibration

Indexed keywords

BANDPASS FILTERS; CAMERAS; GEOMETRY; INFRARED DEVICES; RADIOMETRY; REMOTE SENSING; SPECTROSCOPY;

CULTURAL HERITAGE DIAGNOSTICS; GEOMETRICAL CALIBRATIONS; MAIA; MULTI-SPECTRAL; MULTISPECTRAL IMAGING SENSORS; RADIOMETRIC CALIBRATIONS; RADIOMETRIC CHARACTERIZATION; REMOTE SENSING TECHNIQUES;

CALIBRATION;

EID: 85033697002     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5194/isprs-archives-XLII-3-W3-149-2017     Document Type: Conference Paper

References (26)

1. Adam, E., Mutanga, O. and Rugege, D., 2010. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecology and Management, 18(3), pp.281-296.
2. Ahamed, T., Tian, L., Zhang, Y. and Ting, K.C., 2011. A review of remote sensing methods for biomass feedstock production. Biomass and Bioenergy, 35(7), pp.2455-2469.
3. Berni, J.A., Zarco-Tejada, P.J., Suárez, L. and Fereres, E., 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3), pp.722-738.
4. Candiago, S., Remondino, F., De Giglio, M., Dubbini, M. and Gattelli, M., 2015. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 7(4), pp.4026-4047.
5. Del Pozo, S., Rodríguez-Gonzálvez, P., Hernández-López, D. and Felipe-García, B., 2014. Vicarious radiometric calibration of a multispectral camera on board an unmanned aerial system. Remote Sensing, 6(3), pp. 1918-1937.
6. DigitalGlobe, 2009. WorldView-2 Data sheet. https://dg-cms-uploads-production.s3.amazonaws.com/uploads/document/file/98/World View2-DS-WV2-rev2.pdf
7. Dinguirard, M. and Slater, P.N., 1999. Calibration of space-multispectral imaging sensors: A review. Remote Sensing of Environment, 68(3), pp.194-205.
8. Dubbini, M., Pezzuolo, A., De Giglio, M., Gattelli, M., Curzio, L., Covi, D., Yezekyan, T. and Marinello, F., 2017. Last generation instrument for agriculture multispectral data collection. CIGR Journal, 19(1): 87-93. ISSN: 1682-1130.
9. ESA, European Space Agency, 2012. Sentinel-2. ESA's Optical High-Resolution Mission for GMES Operational Services. http://esamultimedia.esa.int/multimedia/publications/SP1322_2/
10. Hruska, R., Mitchell, J., Anderson, M. and Glenn, N.F., 2012. Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle. Remote Sensing, 4(9), pp.2736-2752.
11. Kelcey, J. and Lucieer, A., 2012. Sensor correction of a 6-band multispectral imaging sensor for UAV remote sensing. Remote Sensing, 4(5), pp.1462-1493.
12. Markelin, L., 2013. Radiometric calibration, validation and correction of multispectral photogrammetric imagery. Finnish Geodetic Institute.
13. Leifer, I., Lehr, W.J., Simecek-Beatty, D., Bradley, E., Clark, R., Dennison, P., Hu, Y., Matheson, S., Jones, C.E., Holt, B. and Reif, M., 2012. State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill. Remote Sensing of Environment, 124, pp.185-209.
14. Liang, H., 2012. Advances in multispectral and hyperspectral imaging for archaeology and art conservation. Applied Physics A, 106(2), pp.309-323.
15. Luhmann, T., 2014. Eccentricity in images of circular and spherical targets and its impact on spatial intersection. The Photogrammetric Record, 29(148), pp.417-433.
16. McGlone, J.C. ed., 2013. Manual of photogrammetry. American Soc. for Photogrammetry and Remote Sensing.
17. Mulla, D.J., 2013. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems engineering, 114(4), pp.358-371.
18. Panagou, E.Z., Papadopoulou, O., Carstensen, J.M. and Nychas, G.J.E., 2014. Potential of multispectral imaging technology for rapid and non-destructive determination of the microbiological quality of beef filets during aerobic storage. International journal of food microbiology, 174, pp.1-11.
19. Ray, S.F., 2002. Applied photographic optics: Lenses and optical systems for photography, film, video, electronic and digital imaging. Focal Press.
20. Remondino, F., Fraser, C., 2006. Digital camera calibration methods: considerations and comparisons. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(5), pp. 266-272.
21. Remondino, F., Rizzi, A., Barazzetti, L., Scaioni, M., Fassi, F., Brumana, R., Pelagotti, A., 2011. Review of geometric and radiometric analyses of paintings. The Photogrammetric Record, 26(136), pp. 439-461
22. Robson, S., MacDonald, L., Kyle, S.A. and Shortis, M.R., 2014. Multispectral calibration to enhance the metrology performance of C-mount camera systems. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(5), p.517.
23. Schutt, J.B., Arens, J.F., Shai, C.M. and Stromberg, E., 1974. Highly reflecting stable white paint for the detection of ultraviolet and visible radiations. Applied optics, 13(10), pp. 2218-2221.
24. Shaw, G.A. and Burke, H.K., 2003. Spectral imaging for remote sensing. Lincoln Laboratory Journal, 14(1), pp.3-28.
25. VDI/VDE 2634, 2002. Guideline 2634 part 1, Optical 3D measuring systems - Imaging systems with point-by-point probing.
26. Zang, W., Lin, J., Wang, Y. and Tao, H., 2012, June. Investigating small-scale water pollution with UAV remote sensing technology. In: World Automation Congress (WAC), 2012 (pp. 1-4). IEEE.