Abstract
This paper studies the gray feature and texture feature including initial moment, energy based on gray level co-occurrence. An approach is proposed that feature is extracted and selected. Furthermore the BP neural network is applied to the image supervised classification. At least, the small areas are removed by morphological open operator. Considering the gray feature and texture feature of the SAR image , the method is more suitable for SAR image classification than the traditional method, which uses the texture feature only. The experimental results show the method can solve the airborne high resolution SAR image classification perfectly.
This work is partially supported by Grant #20070031 from key laboratory of Geo-Informatics of State Bureau of Surveying and Mapping.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Sun, J.: Remote Sensing Principle and Application, pp. 204–210. WuHan University Press, Wuhan (2005)
Segl, K., Kaufman, H.: Detection of Small Objects From High-Resolution Panchromatic Satellite Imagery Based on Supervised Image Segmentation. IEEE Transaction on Geoscience and Remote Sensing 39(9), 89–112 (2001)
Guo, D.: Terrain Observation Theory and Application of Using Radar, pp. 214–217. Science Press, Beijing (2000)
Fan, W., Chao, W., Hong, Z.: Based-on texture features Residential area extraction from High resolution SAR image. Remote Sensing Technology and Application 20(1), 148–152 (2005)
Zeng, G.: Terrain classification based on multi-scale texture analysis using SAR image. The Master degree thesis of Wuhan University, pp.19–21 (2004)
Li, Y., Shi, Q., Zhang, Y., Zhao, R.: Study of SAR image automatic division methods. Journal of Electronic and information 28(5), 932–934 (2006)
Kohonen, T., Somervno, P.: How to Make Large Self-Organizing Maps for Nonvectorial Data. Neural Network 15, 945–952 (2002)
Cigizoglu, H.K.: Generalized Regression Neural Network in Modelling River Sediment Yield. Advances in Engineering Software 37, 63–68 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Duan, L., Yang, L., Wang, J., An, Z. (2011). Research on Airborne High Resolution SAR Image Classification. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23220-6_86
Download citation
DOI: https://doi.org/10.1007/978-3-642-23220-6_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23219-0
Online ISBN: 978-3-642-23220-6
eBook Packages: Computer ScienceComputer Science (R0)