Combination of object-based and pixel-based image analysis for classification of vhr imagery over urban areas

American Society for Photogrammetry and Remote Sensing Annual Conference 2011

Volumn , Issue , 2011, Pages 454-460

  Salehi, Bahram ^a   Zhang, Yun ^a   Zhong, Ming ^a  

^a UNIVERSITY OF NEW BRUNSWICK   (Canada)

Author keywords

Class separability; Feature extraction; Morphology; Pixel based classification; Segmentation; Texture

Indexed keywords

CLASS SEPARABILITY; CLASSIFICATION ACCURACY; CLASSIFICATION PROCEDURE; DIFFERENT SIZES; LAND COVER CLASSIFICATION; LAND-COVER TYPES; MAXIMUM LIKELIHOOD CLASSIFICATIONS; MULTIRESOLUTION SEGMENTATION; OBJECT BASED; OBJECT BASED IMAGE ANALYSIS; OPTIMAL WINDOW SIZE; SIZE AND SHAPE; SPATIAL MEASURES; SPECTRAL MEASURE; URBAN AREAS; URBAN LAND COVER; VERY HIGH RESOLUTION; VHR IMAGERY; VHR IMAGES;

FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE SEGMENTATION; LANDFORMS; MORPHOLOGY; OPTIMIZATION; PHOTOGRAMMETRY; PIXELS; REMOTE SENSING; TEXTURES;

IMAGE TEXTURE;

EID: 84868613965     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Ben-Dor, E., 2001. Imaging Spectroscopy for Urban Applications in Imaging Spectroscopy (F. van Der Meer. And S.M. de Jong eds), Kluwer Academic Press, pp. 243-281.
2. Carleer, A.P., E., Wolff, 2006. Urban land cover multi-level region-based classification of VHR data by selecting relevant features. International Journal of Remote Sensing 27 (6), 1035-1051.
3. Herold, M., M.E. Gardner, D.A., Roberts, 2003. Spectral resolution requirements for mapping urban areas. IEEE Transactions on Geoscience and Remote Sensing 41 (9 PART I), 1907-1919.
4. Jensen, J.R., 2005. Introductory digital image processing: a remote sensing perspective, 3rd ed. Prentice Hall, Upper Saddle River, N.J.
5. Lu, D., S., Hetrick, E. Moran, 2010. Land cover classification in a complex urban-rural landscape with Quickbird imagery. Photogrammetric Engineering and Remote Sensing 76(10), 1159-1168.
6. Myint, S.W., 2007. Urban mapping with geospatial algorithms. In: Weng, Q, Quattrochi, D, A. Urban remote sensing. CRC press, Taylor & Francis group; Boca Raton, Florida, pp. 109-135.
7. Salehi, B., M.J. Valadan Zouj, M. Varshosaz, 2008. Projection Pursuit and Lowpass Filtering for Preprocessing of Hyperspectral Images. World Applied Sciences Journal, 3(5): 785-796.
8. Warner, T.A., F. Nerry, 2009. Does single broadband or multispectral thermal data add information for classification of visible, near and shortwave infrared imagery of urban areas? International Journal of Remote Sensing 74(7), 2155-2171.
9. Watanachaturaporn, P., M.K. Arora, P.K. Varshney, 2008. Multisource classification using support vector machines: An empirical comparison with decision tree and neural network classifiers. Photogrammetric Engineering and Remote Sensing 74 (2), 239-246.
10. Zhang, Y., T. Maxwell, H. Tong, V. Dey, 2010. Developement of supervised software tool for automated determination of optimal segmentation parameters for ecognition. ISPRS TC VII symposium-100 Years ISPRS, Vienna, Austria, July 5-7, 2010.
11. Zhang, Y., T. Maxwell, 2006. A trained segmentation technique for optimization of object-oriented classification. ISPRS commission VII Mid -term Symposium-Remote Sensing from Pixels to Processes, Enschede, the Netherland, 8-11 May 2006.
12. Zhang, Y. 2004. The Front Cover: Standard and Colour Enhanced Pansharpening of Quickbird Imagery - Sydney, Australia. Photogrammetric Engineering & Remote Sensing, Vol. 70, No. 6.