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.
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© 2011 Springer-Verlag Berlin Heidelberg
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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
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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
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