A noise-removal approach for lidar intensity images using anisotropic diffusion filtering to preserve object shape characteristics

American Society for Photogrammetry and Remote Sensing - ASPRS Annual Conference 2007: Identifying Geospatial Solutions

Volumn 2, Issue , 2007, Pages 471-481

  Nobrega, Rodrigo A A ^a,b   Quintanilha, Jose A ^a   O'Hara, Charles G ^b  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANISOTROPIC DIFFUSION FILTERING; BLURRED EDGES; FILTER PARAMETER; FILTERED IMAGES; INTENSITY DATA; LASER SIGNALS; LIDAR INTENSITY IMAGE; LIDAR SENSORS; LOW-PASS; OBJECT BASED; OBJECT SHAPE; PRIMARY DATA; REAL OBJECTS; REFLECTED LASER; SEGMENTATION PROCESSING; SURFACE FEATURE; TEST IMAGES; VISUAL INSPECTION;

DISSIMILAR MATERIALS; IMAGE SEGMENTATION; OPTICAL ANISOTROPY; OPTICAL RADAR; REMOTE SENSING; SIGNAL FILTERING AND PREDICTION; SIGNAL RECEIVERS;

IMAGE DENOISING;

EID: 84868622098     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (12)

1. Acton S. T., Bovik, A. C. (1992). Crawford M. M. Anisotropic diffusion pyramids for image segmentation. In: Proceedings of IEEE International Conference of Image Processing. Austin-TX. pp 478-482.
2. Black, M. J., Sapiro, G., Marimont, D. H., Heeger, D. (1998). Robust anisotropic diffusion. IEEE Transactions on Image Processing, vol 7, n. 3. pp. 421-432.
3. Blaschke, T.; Strobl, J. (2001). What's Wrong With Pixels? Some Recent Developments Interfacing Remote Sensing and GIS. In GIS-Zeitschrift fur Geoinformationsysteme, Helt 6, p 12-17.
4. Charaniya, A. P.; Manduchi, R.; Lodha, S. K. (2004). Supervised parametric classification of aerial LiDAR data. Proceedings of IEEE Comtuter Society Conference o Computer Vision and Pattern Recognition Workshops CVPRW'04, Vol. 3. (http://ieeexplore.ieee.org/iel5/9515/30163/01384821.pdf).
5. Coren, F., Visintini, D., Prearo, G., Sterzai, P. (2005) Integrating LiDAR intensity measures and hyper-spectral data for extracting of cultural heritage. In: Workshop Italy-Canada for 3D Digital Imaging and Modeling: applications of heritage, industry, medicine and land.
6. Giacomantone, J. O. (2005) Ressonância magnética funcional com filtragem pela difusão anisotrópica robusta. Dissertacao de Mestrado, Escola Politécnica da Universidade de Sao Paulo. Departamento de Engenharia de Sistemas Eletrônicos. Sao Paulo, 103 p.
7. Gonzales, R. C.; Woods, R. E. (1993) Digital Image Processing. 3rd. ed. Addison-Wesley Reading, Massachusetts, 716 p.
8. Gutierrez, R., Gibeaut, J. C., Smyth, R. C., Hepner, T. L., Andrews, J. R. (2001) Precise airborne LiDAR surveying for coastal research and geohazards applications. In: Inernational Archives of Photogrammetry and Remote Sensing, vol. 34-3/W4. Annapolis-MD. October, 22-24. pp 185-192.
9. Kim, H. Y.; Giacomantone, J. O.; Cho, Z. H. (2005). Robust Anisotropic Diffusion to Produce Enhanced Statistical Parametric Map from Noisy fMRI, Computer Vision and Image Understanding, vol. 99, pp. 435-452.
10. Perona, P.; Malik, J. (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 12, n. 7. pp. 629-639.
11. Sithole, G. (2005) Segmentation and classification of airborne laser scanner data. Publication on Geodesy 59, Nederlandse Commissie voor Geodesie, Delft, The Netherlands. 184 p.
12. Song, J-H., Han, S-H., Yu, K., Kim, Y. (2002). Assessing the possibility of land-cover classification using LiDAR intensity data. In: International Archives of Photogrammetry and Remote Sensing, IAPRS, vol. 34, September 9-13, Graz. 4 p.