Maneuvering target recognition of high resolution SAR images based on multi-scale feature

被引：1
|
作者：
Liu A.-P. [1 ,2 ]
Fu K. [2 ]
Zhang L.-L. [2 ]
You H.-J. [2 ]
Liu Z. [1 ]
机构：
[1] Electronic Engineering Coll., Navy Engineering Univ.
[2] Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Inst. of Electronics, Chinese Acad. of Sci.
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2010年 / 32卷 / 06期
关键词：
Multi-scale feature; SAR image processing; Scale-space; Target recognition;
D O I：
10.3969/j.issn.1001-506X.2010.06.012
中图分类号：
学科分类号：
摘要：
The real-world itself has multi-scales characters, and objects only make sense in special scales. A method of targets recognition in SAR images based on multi-scale information is proposed, in which objects' macroscopic and microcosmic characters are well presented by multi-scale analysis. The SAR images are segmented by introducing a priori knowledge in order to reflect the targets' shape information. According to the different response characters of targets corresponding multi-scales Gabor filters, multi-scale information and the pertinence between multi-scales are adopted to extract new multi-scale features. Finally, the new multi-scale features database is established according to the azimuth angles of targets to guide targets recognition. Multi-scale features extraction and targets recognition are tested on MSTAR SAR data. The experimental results show the validity of this method.
引用
收藏
页码：1161 / 1166
页数：5
相关论文
共 18 条
  • [1] 8, 10, pp. 1115-1120, (2003)
  • [2] 36, 6, pp. 1041-1046, (2008)
  • [3] Zhang L., Zhang Y., Li Y., Et al., Fast detection of bridges in SAR images, Chinese Journal of Electronics, 16, 3, pp. 481-483, (2007)
  • [4] Ross T., Worrell S., Velten V., Et al., Standard SAR ATR evaluation experiment using the MSTAR public release data set, SPIE Conference on Algorithms for SAR, 3770, pp. 566-573, (1998)
  • [5] 34, 13, pp. 24-26, (2008)
  • [6] 30, 7, pp. 1722-1726, (2008)
  • [7] Felzenszwalb P., McAllester D., Ramanan D., Adiscriminatively trained, multiscale, deformable part model, IEEE Conference on Computervision and Pattern Recognition, (2008)
  • [8] Galun M., Basri R., Brandt A., Multiscale edge detection and fiber enhancement using differences of oriented means, IEEE 11th International Conference on Computer Vision, (2007)
  • [9] de Lima Filho E.B., da Silva E.A.B., de Carvalho M.B., Et al., Universal image compression using multiscale recurrent patterns with adaptive probability model, IEEE Trans. on Image Processing, 17, 4, pp. 512-527, (2008)
  • [10] Veniste A., Nikoukhah R., Willsky A.S., Multiscale system theory, Proc. of 29th IEEE Conference on Decision and Control, pp. 2484-2487, (1990)
12