Abstract
This paper presents a summary of our recent research in the granular approach of multi-scale analysis methods for object-oriented remote sensing image classification. The promoted granular Hough Transform strengthens its ability of recognize lines with different width and length in remote sensing image, while the proposed granular watershed algorithm performs much more coherently with human visual characteristic in the segmentation. Rough Set is introduced into the remote sensing image classification, involving in the procedures of feature selection, classification rule mining and uncertainty assessment. Hence, granular computing runs through the complete remote sensing image classification and promotes an innovative granular approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chen, M.: Optimal bands selection of remote sensing image based on core attribute of rough set theory (in Chinese). Journal of Ningde Teachers College (Natural Science) 18(4), 378–380 (2006)
Ge, Y., Bai, H., Li, S., Li, D., Ge, Y.: Exploring the Sample Quality Using Rough Sets Theory for the Supervised Classification of Remotely Sensed Imagery. Geo-spatial Information Science 11(2), 95–102 (2008)
Li, L., Ma, J., Ouyang, Y.: Tolerant Rough Set Processing on Uncertainty of Satellite Remote Sensing Data Classification (in Chinese). Computer Engineering 34(6), 1–2 (2008)
Qin, M.: High Spatial Resolution Remote Sensing Images Segmentation Based On Granularity (in Chinese), Master Dissertation, Wuhan University (2008)
Wu, Z.: RBFNN Representation Based on Rough Sets and Its Application to Remote Sensing Image Classification (in Chinese). Acta Geodaetica et Cartographica 32(1), 53–57 (2003a)
Wu, Z.: Remote Sensing Image Classification and Rule Induction Based on Rough Sets. Computer Science 30(5), 93–95 (2003b)
Wu, Z., Li, D.: Neural Network Based on Rough Sets and Its Application to Remote Sensing Image Classification. Geo-spatial Information Science 5(2), 17–21 (2002)
Wu, Z., Wan, Q., Liang, J., Zhou, Z.: Line Detection in Remote Sensing Image Using Hough Transform Based on Granular Computing (in Chinese). Geomatics and Information Science of Wuhan University 32(10), 860–863 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhaocong, W., Lina, Y., Maoyun, Q. (2009). Granular Approach to Object-Oriented Remote Sensing Image Classification. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_71
Download citation
DOI: https://doi.org/10.1007/978-3-642-02962-2_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02961-5
Online ISBN: 978-3-642-02962-2
eBook Packages: Computer ScienceComputer Science (R0)