Abstract
We discuss a roughness problems in this paper. Roughness measurements play a fundamental role in terrestrial planet research. The main question discussed in this paper is how to build a more efficient roughness model on the basis of images. The model needs to beat a series of traditional roughness problems on the image. Lunar reconnaissance orbiter camera (LROc) and charged coupled device (CCD) images are our primary data target. Roughness is divided into trend surface roughness and topography roughness with analysis and inversion. The two parts are combined and analyzed. The analysis is based on two structural features: trend reflection and detail details surface. Our team use inversion to remap the surface roughness by CCD image, and then classify the roughness into the trend influence class and the detail influence class. We use frequency analysis tools are to find the discontinuous energy in the frequency domain. Finally, these roughness information are remapped into a visual image results. Our model provide a more accurate method on terrestrial planetary surface roughness problem. We can analyze the difference between the trend surface and the detail roughness by single image. We find that, lunar surface roughness presents more detail with the help of the frequency analysis. Highlands and the mare shows very different roughness decomposition. Our analysis shows better results in the areas in which details surface is not associated with roughness. It is hoped that this study will provide new insights in the surface roughness of terrestrial planets. Especially for CCD images (LROc) rather than details surface data (DEM). In the future, we will focus on calculations and analysis for Mars based on the mode in this paper. For the roughness data, we will start depth learning, classification, pattern recognition and other research in future work.
Similar content being viewed by others
References
Grohmann, C.H., Smith, M.J., Riccomini, C.: Multiscale analysis of topographic surface roughness in the Midland Valley, Scotland. IEEE Trans. Geosci. Remote Sens. 49(4), 1200–1213 (2011)
Hobson, R.D.: Surface roughness in topography: quantitative approach. In: Chorley, R.J. (ed.) Spatial Analysis in Geomorphology, pp. 221–245. Methuen & Co., London (1972)
Mckean, J., Roering, J.: Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology 57(3–4), 331–351 (2004)
David, C.M., David, M.C., Matthew, S.H., et al.: Floodplain friction parameterization in two-dimensional river flood models using vegetation heights derived from airborne scanning laser altimetry. Hydrol. Process. 17(9), 1711–1732 (2003)
Streutker, D.R., Mundt, J.T., Glenn, N.F.: Mapping sagebrush distribution using fusion of hyperspectral and lidar classifications. Photogram. Eng. Remote Sens. 72(1), 47–54 (2006)
Davenport, I.J., Holden, N., Gurney, R.J.: Characterizing errors in airborne laser altimetry data to extract soil roughness. IEEE Trans. Geosci. Remote Sens. 42(10), 2130–2141 (2004)
Pelletier, J.D., Mitasova, H., Harmon, R.S., et al.: The effects of interdune vegetation changes on eolian dune field evolution: a numerical-modeling case study at Jockey’s Ridge, North Carolina, USA. Earth Surf. Process. Landforms 34(9), 1245–1254 (2010)
Sankey, J.B., Glenn, N.F., Germino, M.J., et al.: Relationships of aeolian erosion and deposition with LiDAR-derived landscape surface roughness following wildfire. Geomorphology 119(1–2), 135–145 (2010)
Glenn, N.F., Streutker, D.R., Chadwick, D.J., et al.: Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity. Geomorphology 73(1–2), 131–148 (2006)
Glaze, L.S., Baloga, S.M.: Topographic variability on Mars: implications for lava flow modeling. J. Geophys. Res. Planets (2007). https://doi.org/10.1029/2006JE002879
Rosenburg, M.A., Aharonson, O., Head, J.W., et al.: Global surface slopes and roughness of the lunar from the lunar orbiter laser altimeter. J. Geophys. Res Planets (2011). https://doi.org/10.1029/2010JE003716
Evans, I.S.: Correlation structures and factor analysis in the investigation of data dimensionality: statistical properties of the Wessex land surface, England. In: Proceedings of the International Symposium on Spatial Data Handling, Zurich, Switzerland, vol. 1, pp. 98–116 (1984)
Rosenburg, M.A., Aharonson, O., Head, J.W., et al.: Global surface slopes and roughness of the lunar from the lunar orbiter laser altimeter. J Geophys Res Planets 116(2), 1161–1172 (2011)
Neumann, G.A., Forsyth, D.W.: High resolution statistical estimation of seafloor morphology: oblique and orthogonal fabric on the flanks of the Mid-Atlantic Ridge, 34°–35.5°S. Mar. Geophys. Res. 17(3), 221–250 (1995)
Orosei, R., Bianchi, R., Coradini, A., et al.: Self-affine behavior of Martian topography at kilometer scale from mars orbiter laser altimeter data. J Geophys Res Atmos (2003). https://doi.org/10.1029/2002JE001883
Sharpton, V.L., Head, J.W.: Global topographic and structural fabric on venus. In: Lunar and Planetary Science Conference, pp. 765–766 (1985)
Wang, J., Li, J., Liu, B., et al.: 3D simulation model of lunar crater based on single CCD image. In: Proceedings of the International Conference on Information Science and Electronic Technology (2015)
Cao, W., Cai, Z., Tang, Z.: Lunar surface roughness based on multiscale morphological method. Planet. Space Sci. 108, 13–23 (2015)
Li, J., Tian, X.: Analysis of all-in-focus image processing in image restoration. Multimed. Tools Appl. 76, 1–25 (2016)
Acknowledgements
This work is supported by the Science and Technology Development Fund of Macao (FDCT-17-009-FI/No. 099/2016/A3). Some experimental lunar data are derived from publicly OPEN data by NASA website.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, J., Cao, W. & Tian, X. Topographic surface roughness analysis based on image processing of terrestrial planet. Cluster Comput 22 (Suppl 4), 8689–8702 (2019). https://doi.org/10.1007/s10586-018-1943-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-1943-3