期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

多尺度自适应恒虚警率(CFAR)自动检测与重构多方位SAR图像中不同尺度目标物

Automatic Detection and Reconstruction of Different-size Objects from Multi-aspect SAR Images Using Multi-scale Adaptive CFAR
原文传递
导出
摘要 提出一种多尺度自适应恒虚警率(CFAR)检测方法,用于SAR图像中城区环境不同尺度目标物的自动检测,同时完成对目标物尺寸大小的估算。以目标物几何尺度及其成像位置作为依据,实现不同方位飞行的SAR对同一景物的图像上目标物像的自动匹配,以相对飞行机载PI-SAR图像为例,检测机动车目标和建筑物(群)目标,并实现该两类目标物景象的立体重构。 A multi-scale adaptive CFAR method is proposed to automatic detection and reconstruction of the objects with different size in urban area from multi-aspect SAR images.The sizes and locations of the objects such as the building and vehiles are evaluated and recorded during detection.Based on these records,the objects can be automatically matched in multi-aspect SAR images.As example,two images of opposite-aspect airborne PI-SAR flights are applied to scene reconstruction of the buildings and vehiles.
作者 戴尔燕 金亚秋
机构地区 复旦大学波散射与遥感信息教育部重点实验室
出处 《遥感技术与应用》 CSCD 北大核心 2009年第6期722-730,I0001,共10页 Remote Sensing Technology and Application
基金 国家自然科学基金项目(NSFC40637033 60571050)
关键词 多方位SAR 多尺度自适应恒虚警率 自动检测 目标重构 Multi-aspect SAR Multi-scale adaptive CFAR Object detection 3D reconstruction
分类号 TP79 [自动化与计算机技术—检测技术与自动化装置] TP75 [自动化与计算机技术—检测技术与自动化装置]
  • 相关文献

参考文献24

  • 1Jin Y, Friedlander B. A CFAR Adaptive Subspace Detector for Second-order Gaussian Signals[J]. IEEE Transaction on Signal Processing,2005,53(3) :871-884.
  • 2Smith M E, Varshney P K. Intelligent CFAR Processor Based on Data Variability[J]. IEEE Transaction on Aerospace and Electronic Systems,2000,36(3):837- 847.
  • 3di Bisceglie M,Galdi C. CFAR Detection of Extended Objects in High resolution SAR Images[J]. IEEE Transaction on Geoscience and Remote Sensing, 2005,43(4) : 833- 843.
  • 4Maitre H,孙洪等译.合成孔径雷达图像处理[M].北京:电子工业出版社,2005:67-103.
  • 5Hamasaki A. Polarimetric SAR Stereo Using Pi SAR Square Loop Path[C]. Proceedings of IGARSS'05,2005,1 : 25-29.
  • 6Bolter R. Reconstruction of Man made Objects from High Resolution SAR Images[C]. IEEE Aerospace Conference Pro ceedings,2000,3 : 18- 25.
  • 7Balz T, Haala N. SAR-based 3D-reconstruction of Complex Urban Environments[C]. IAPRS Workshop on 3-Dreconstruction from Airborne Laser Scanner and InSAR Data, 2003,34(3) :W13.
  • 8Tupin F, Roux M. Markov Random Field on Region Adjacency Graph for the Fusion of SAR and Optical Data in Radar Grammetric Applications[J]. IEEE Transactions on Geosci ence and Remote Sensing,2005,43(8):1920-1928.
  • 9Liwit M M, Matej S. Overview of Methods for Image Reconstruction from Projections in Emission Computed Tomography[C]. Proceedings of IEEE,2003,91(10) :1588 -1611.
  • 10Xia Z G, Henderson F M. Understanding the Relationship Between Radar Response Patterns and the Bio-and Geophysical Parameters of Urban Areas[J]. IEEE Transactions on Geo science and Remote Sensing, 1997,35 (1) : 93-101.

二级参考文献12

  • 1Kong J A, Swartz A A. Identification of terrain cover using the optimal terrain classifier. J Electronmagn. Waves Applicat, 1988, 2: 171～194.
  • 2Lee J S, Grunes M R, Kowk R, et al. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution. Int J Remote Sensing, 1994, 15(11): 2299～2311.
  • 3Kwok R, Hara Y, Atkins R G, et al. Application of neural networks to sea ice classification using polarimetric SAR images. In: Proceedings of IGARSS'91, 1991. 1: 85～88.
  • 4Tzeng Y C. A dynamic learning neural network for remote sensing application. IEEE Trans Geosci Remote Sensing, 1995, 32(5): 1096～1102.
  • 5Chen K S, Huang W P, Tsay D H, et al. Classification of multifrequency polarimtric SAR imagery using a dynamic learning neural network. IEEE Trans Geosci Remote Sensing, 1996, 34(3): 814～820.
  • 6Tzeng Y C, Chen K S. A fuzzy neural network to SAR image classification. IEEE Trans Geosci Remote Sensing, 1998, 36(1): 301～307.
  • 7Hellmann M, Jager G, Kraetzschmar E, et al. Classification of full polarimetric SAR-data using artificial neural networks and fuzzy algorithms. In: Proceedings of IGARSS'99, 1999. 1995～1997.
  • 8Lueneburg E, Chandra M, Boerner W M. Random target approximations. In: Proc PIERS Progress in Electromagnetic Research Symposium, Noordwijk, The Netherlands, 1994. 1366～1369.
  • 9Yang J, Peng Y N, Lin S M. Similarity between two scattering matrices, electron Letters, 2001, 37(3): 193～194.
  • 10Huynen J R. Phenomenological theory of radar targets. Ph D Dissertation, 1970.

共引文献13

遥感技术与应用
2009年 第6期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部