Abstract
We present an overview of a structure from motion (SFM) pipeline for processing hyperspectral imagery (HSI), and demonstrate the data exploitation advantages associated with post-processing HSI data in a 3D environment. Using only raw HSI datacubes as input, we leverage HSI anomaly detection and spectral matching to create a 3D spatial model of the scene being imaged. The resulting 3D space provides an intuitive basis for all forms of HSI analysis. We demonstrate the usefulness of the proposed HSI SFM pipeline through an experimental data set collected using an aerial hyperspectral sensor.
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References
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, New York (2003)
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. In: ACM Transactions on Graphics (TOG), vol. 25, pp. 835–846. ACM (2006)
Geiger, A., Ziegler, J., Stiller, C.: Stereoscan: dense 3d reconstruction in real-time. In: Intelligent Vehicles Symposium (IV), 2011 IEEE, pp. 963–968. IEEE (2011)
Yuen, P.W., Richardson, M.: An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition. Imaging Sci. J. 58(5), 241–253 (2010)
Van der Meer, F.D., van der Werff, H., van Ruitenbeek, F.J., Hecker, C.A., Bakker, W.H., Noomen, M.F., van der Meijde, M., Carranza, E.J.M., Smeth, J., Woldai, T.: Multi-and hyperspectral geologic remote sensing: a review. Int. J. Appl. Earth Obs. Geoinf. 14(1), 112–128 (2012)
Tochon, G., Féret, J., Valero, S., Martin, R., Knapp, D., Salembier, P., Chanussot, J., Asner, G.: On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images. Remote Sens. Environ. 159, 318–331 (2015)
Resende, M.R., Bernucci, L.L.B., Quintanilha, J.A.: Monitoring the condition of roads pavement surfaces: proposal of methodology using hyperspectral images. J. Transp. Lit. 8(2), 201–220 (2014)
Nieto, J.I., Monteiro, S.T., Viejo, D.: 3D geological modelling using laser and hyperspectral data. In: 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4568–4571. IEEE (2010)
Kim, M.H., Harvey, T.A., Kittle, D.S., Rushmeier, H., Dorsey, J., Prum, R.O., Brady, D.J.: 3D imaging spectroscopy for measuring hyperspectral patterns on solid objects. ACM Trans. Graph. (TOG) 31(4), 38 (2012)
Miller, C.A., Walls, T.J.: Passive 3D scene reconstruction via hyperspectral imagery. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., McMahan, R., Jerald, J., Zhang, H., Drucker, S.M., Kambhamettu, C., Choubassi, M., Deng, Z., Carlson, M. (eds.) ISVC 2014, Part I. LNCS, vol. 8887, pp. 413–422. Springer, Heidelberg (2014)
Neumann, J., Allman, E.C., Downes, T., Howard, J., Kruer, M., Lee, J., Linne von Berg, D., Leathers, R., Murray-Krezan, J., Nezis, N.: Demonstration of the MX-20SW standoff SWIR hyperspectral imaging ball gimbal system. MSS, Passive Sensors (2008)
Reed, I.S., Yu, X.: Adaptive multiple-band cfar detection of an optical pattern with unknown spectral distribution. IEEE Trans. Acoust. Speech Signal Process. 38(10), 1760–1770 (1990)
Hartley, R.I., Sturm, P.: Triangulation. Comput. Vis. Image Underst. 68(2), 146–157 (1997)
Zach, C.: Simple Sparse Bundle Adjustment (SSBA) (2011) http://www.inf.ethz.ch/personal/chzach/opensource.html. Accessed on October 2013
Manolakis, D.G., Shaw, G.A., Keshava, N.: Comparative analysis of hyperspectral adaptive matched filter detectors. In: AeroSense 2000, International Society for Optics and Photonics, pp. 2–17 (2000)
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Miller, C.A., Walls, T.J. (2015). Hyperspectral Scene Analysis via Structure from Motion. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_65
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DOI: https://doi.org/10.1007/978-3-319-27857-5_65
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