A New Enhancement Filtering Approach for the Automatic Vector Conversion of the UAV Photogrammetry Output

  • Maria AlicandroEmail author
  • Donatella Dominici
  • Paolo Massimo Buscema
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11196)


In the last decades the photogrammetry has undergone interesting innovation, both in terms of data processing and acquisition mode, to allow obtaining detailed 3D models useful for complete survey and important support for the management and recovery of cultural heritage and buildings. However, despite recent developments, the main photogrammetry outputs are raster data (ortophoto and DEM) and point clouds characterized by high informative content, but they are not typically extracted automatically. Automated feature detection is yet manual, time-consuming procedure and an active area of research. The raster to vector conversion is not direct, but transformations must be performed on the input data to convert the pixel values into features. Always, segmentations are preceded by filter technique to remove noise and to improve the conversion phase. However, remote sensing data and especially UAV photogrammetry output are the most complex to treat because of their heterogeneity (presence of different objects and shapes), the nature of sensor used and the different scale. In this work we experiment new enhancement filter to improve the automatic extraction of vector information for a UAV photogrammetry results of the facing walls of eminent church, symbol of the city of L’Aquila, the” Basilica of Santa Maria di Collemaggio”.


Photogrammetry UAV Segmentation Filter enhancement 


  1. 1.
    Barazzetti, L., Forlani, G., Remondino, F., Roncella, R., Scaioni, M.: Experiences and achievements in automated image sequence orientation for close-range photogrammetric projects. In: Remondino, F., Shortis, M.R. (eds.) (2011).
  2. 2.
    Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1362–1376 (2010)CrossRefGoogle Scholar
  3. 3.
    Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008)CrossRefGoogle Scholar
  4. 4.
    Dominici, D., Baiocchi, V., Zavino, A., Alicandro, M., Elaiopoulos, M.: Micro UAV for post seismic hazards surveying in old city center of L’Aquila. In: FIG Working Week 2012 Knowing to Manage the Territory, Protect the Environment, Evaluate the Cultural Heritage, Roma (2012)Google Scholar
  5. 5.
    Piras, M., Taddia, G., Forno, M.G., Gattiglio, M., Aicardi, I., Dabove, P., et al.: Detailed geological mapping in mountain areas using an unmanned aerial vehicle: application to the Rodoretto Valley, NW Italian Alps. Geomat. Nat. Hazards Risk 8, 1–13 (2016)Google Scholar
  6. 6.
    Aicardi, I., Chiabrando, F., Lingua, A.M., Noardo, F., Piras, M., Vigna, B.: A methodology for acquisition and processing of thermal data acquired by UAVs: a test about subfluvial springs’ investigations. Geomat. Nat. Hazards Risk 8, 1–13 (2016)Google Scholar
  7. 7.
    Baiocchi, V., Dominici, D., Mormile, M.: UAV application in post-seismic environment. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XL-1/W2, 21–25 (2013)CrossRefGoogle Scholar
  8. 8.
    Matta, S.: Review: various image segmentation techniques. Swati Matta/(IJCSIT) Int. J. Comput. Sci. Inf. Technol. 5(6), 7536–7539 (2014)Google Scholar
  9. 9.
    Roushdy, M.: Comparative study of edge detection algorithms applying on the grayscale noisy image using morphological filter. GVIP J. 6(4), 17–23 (2006)Google Scholar
  10. 10.
    Shrivakshan, G.T., Chandrasekar, C., et al.: A comparison of various edge detection techniques used in image processing. IJCSI Int. J. Comput. Sci. Issues 9(5), 272–276 (2012)Google Scholar
  11. 11.
    Maini, R.: Study and comparison of various image edge detection techniques. Int. J. Image Process. 3(1), 12 (2009)Google Scholar
  12. 12.
    Schiewe, J.: Segmentation of high-resolution remotely sensed data-concepts, applications and problems. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 34(4), 380–385 (2002)Google Scholar
  13. 13.
    Baiocchi, V., Brigante, R., Dominici, D., Milone, M.V., Radicioni, F.: Multispectral automatic feature extraction methodologies comparison. In: Proceedings of 33rd EARSeL Symposium (2013)Google Scholar
  14. 14.
    Chirici, G., Di Martino, P., Garfì, V., Ottaviano, M., Tonti, D., Giongo Alves, M., et al.: Tecniche avanzate di cartografia degli ambienti forestali su base tipologica in italia centrale. In: Atti del Terzo Congresso Nazionale di Selvicoltura. Accademia Italiana di Scienze Forestali, Taormina (2009)Google Scholar
  15. 15.
    Pastore, V., Sole, A., Telesca, V.: Classificazione object-oriented e tecniche di segmentazione per la derivazione di cartografia di uso/copertura del suolo multiscala (2010)Google Scholar
  16. 16.
    Forlani, G., Nardinocchi, C., Scaioni, M., Zingaretti, P.: Building reconstruction and visualization from lidar data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 34(5/W12), 151–156 (2003)Google Scholar
  17. 17.
    Dominici, D., Alicandro, M., Massimi, V.: UAV photogrammetry in the post-earthquake scenario: case studies in L’Aquila. Geomat. Nat. Hazards Risk 8, 1–17 (2016). Scholar
  18. 18.
    Dominici, D., Alicandro, M., Rosciano, E., Massimi, V.: Multiscale documentation and monitoring of L’aquila historical centre using UAV photogrammetry. ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 42W4, 365–371 (2017). Scholar
  19. 19.
    User Manuals. Accessed 20 Apr 2018
  20. 20.
    Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J., Reynolds, J.M.: Structure-from-Motion’photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179, 300–314 (2012)CrossRefGoogle Scholar
  21. 21.
    Fraser, C.S.: Digital camera self-calibration. ISPRS J. Photogramm. Remote. Sens. 52(4), 149–159 (1997). Scholar
  22. 22.
    Buscema, P.M.: Sistemi ACM e Imaging Diagnostico. Springer, Milan (2006). Scholar
  23. 23.
    McClelland, J.L., Rumelhart, D.E.: A simulation-based tutorial system for exploring parallel distributed processing. Behav. Res. Methods Instrum. Comput. 20(2), 263–275 (1988). Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of L’AquilaL’AquilaItaly
  2. 2.Semeion Research Center of Sciences of CommunicationRomeItaly
  3. 3.Department of Mathematical and Statistical SciencesUniversity of ColoradoDenverUSA

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