Abstract
Displaying the abundant information contained in a remotely sensed hyperspectral image is a challenging problem. Currently no approach can satisfactorily render the desired information at arbitrary levels of detail. This chapter discusses user studies on several approaches for representing the information contained in hyperspectral information. In particular, we compared four visualization methods: grayscale side-by-side display (GRAY), hard visualization (HARD), soft visualization (SOFT), and double-layer visualization (DBLY). We designed four tasks to evaluate these techniques in their effectiveness at conveying global and local information in an effort to provide empirical guidance for better visual analysis methods. We found that HARD is less effective for global pattern display and conveying local detailed information. GRAY and SOFT are effective and comparable for showing global patterns, but are less effective for revealing local details. Finally, DBLY visualization is efficient in conveying local detailed information and is as effective as GRAY and SOFT for global pattern depiction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Marcal, A.: Automatic color indexing of hierarchically structured classified images. In: Proceedings of IEEE Geoscience and Remote Sensing Symposium, vol. 7, pp. 4976–4979 (2005)
Wessels, R., Buchheit, M., Espesset, A.: The development of a high performance, high volume distributed hyperspectral processor and display system. In: Proceedings of IEEE Geoscience and Remote Sensing Symposium, vol. 4, pp. 2519–2521 (2002)
Tyo, J.S., Konsolakis, A., Diersen, D.I., Olsen, R.C.: Principal components-based display strategy for spectral imagery. IEEE Trans. Geosci. Remote Sens. 41(3), 708–718 (2003)
Tsagaris, V., Anastassopoulos, V., Lampropoulo, G.A.: Fusion of hyperspectral data using segmented PCT for color representation and classification. IEEE Trans. Geosci. Remote Sens. 43(10), 2365–2375 (2005)
Jacobson, N.P., Gupta, M.R.: Design goals and solutions for display of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 43(11), 2684–2692 (2005)
Jacobson, N.P., Gupta, M.R., Cole, J.B.: Linear fusion of image sets for display. IEEE Trans. Geosci. Remote Sens. 45(10), 3277–3288 (2007)
Demir, B., Çelebi, A., Ertürk, S.: A low-complexity approach for the color display of hyperspectral remote-sensing images using one-bit-transform-based band selection. IEEE Trans. Geosci. Remote Sens. 47(1), 97–105 (2009)
Cui, M., Razdan, A., Hu, J., Wonka, P.: Interactive hyperspectral image visualization using convex optimization. IEEE Trans. Geosci. Remote Sens. 47(6), 1678–1684 (2009)
Du, Q., Raksuntorn, N., Cai, S., Moorhead, R.: Color display for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 46(6), 1858–1866 (2008)
Cai, S., Du, Q., Moorhead, R.J.: Hyperspectral imagery visualization using double layers. IEEE Trans. Geosci. Remote Sens. 45(10), 3028–3036 (2007)
Kosara, R., Healey, C.G., Interrante, W., Laidlaw, D.H., Ware, C.: User studies: why, how, and when? IEEE Comput. Graphics Appl. 23(4), 20–25 (2003)
Laidlaw, D.H., Kirby, R.M., Davidson, J.S., Miller, T.S., Silva, M., Warren, W.H., Tarr, M.: Quantitative comparative evaluation of 2D vector field visualization methods. In: IEEE Visualization Conference Proceedings, pp. 143–150 (2001)
Acevedo, D., Laidlaw, D.: Subjective quantification of perceptual interactions among some 2D scientific visualization methods. IEEE Trans. Visualization Comput. Graphics 12(5), 1133–1140 (2006)
Healey, C.G.: Choosing effective colours for data visualization. In: IEEE Visualization Conference Proceedings, pp. 263–270 (1996)
Bair, A.S., House, D.H., Ware, C.: Texturing of layered surfaces for optimal viewing. IEEE Trans. Visualization Comput. Graphics 12(5), 1125–1132 (2006)
Hagh-Shenas, H., Kim, S., Interrante, V., Healey, C.: Weaving versus blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. IEEE Trans. Visualization Comput. Graphics 13(6), 1270–1277 (2007)
Ward, M.O., Theroux, K.J.: Perceptual benchmarking for multivariate data visualization. In: IEEE Visualization Conference Proceedings, pp. 314–321 (1997)
Adams, J.B., Smith, M.O., Gillespie, A.R.: Imaging spectroscopy: Interpretation based on spectral mixture analysis. In: Pieters, C.M., Englert, P. (eds.) Remote Geochemical Analysis: Elemental and Mineralogical Composition 7. Cambridge University Press, New York (1993)
Heinz, D.C., Chang, C.-I.: Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 39(3), 529–545 (2001)
Harsanyi, J.C., Chang, C.-I.: Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Trans. Geosci. Remote Sens. 32(4), 779–785 (1994)
Howell, D.C.: Statistical Methods for Psychology, 6th edn. Wadsworth, Monterey, CA (2006)
Barnett, V., Lewis, T.: Outliers in Statistical Data, 3rd edn. Wiley, West Sussex, England (1994)
Cai, S.: Dissertation: Hyperspectral image visualization by using double and multiple layers, Mississippi State University, Dec (2008)
Ware, C.: Information visualization: perception for design, 2nd edn. Morgan Kaufmann, Maryland Heights, Missouri (2004)
Acknowledgments
This work was supported in part by the NASA Science Mission Directorate, Earth System Division, Applied Science Program under a contract to Mississippi State University through NASA’s Stennis Space Center and by the DoD High Performance Computing Visualization Initiative managed by the USACE ERDC. The authors wish to thank Dr. James Fowler for his insightful suggestions on improving some of the graphs.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Cai, S., Moorhead, R., Du, Q. (2011). An Evaluation of Visualization Techniques for Remotely Sensed Hyperspectral Imagery. In: Prasad, S., Bruce, L., Chanussot, J. (eds) Optical Remote Sensing. Augmented Vision and Reality, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14212-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-14212-3_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14211-6
Online ISBN: 978-3-642-14212-3
eBook Packages: EngineeringEngineering (R0)