Urban Area Man-Made Target Detection for PolSAR Data Based on a Nonzero-Mean Statistical Model

Numerous opportunities for advancement exist in the field of man-made target detection using synthetic aperture radar (SAR). With the development of SAR sensors, the high spatial resolution violates the validity of the zero-mean assumption, particularly for urban applications. In 2012, we refined the conventional azimuth stationarity extraction method by adopting a nonzero-mean model-the Rician distribution-to better adapt to the high-resolution SAR imagery of urban areas and improved the man-made target detection result significantly. However, the model cannot make full use of the multipolarization information because it is calculated with the amplitude data. This study extends the method by employing another nonzero-mean model-the multivariate normal distribution-to consider the scattering vector of polarimetric SAR (PolSAR) images. The proposed method achieves an excellent performance with overall accuracy of 88.76%. Two previous methods, including the PolSAR method using the zero-mean model and the single polarization method using the nonzero-mean model, are also involved in the comparisons. The effectiveness of applying nonzero-mean models to urban area SAR images is well demonstrated by experimental results.

[1]  R. Bolter,et al.  Reconstruction of man-made objects from high resolution SAR images , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[2]  Xinwu Li,et al.  Man-Made Target Detection in Urban Areas Based on a New Azimuth Stationarity Extraction Method , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Thomas Esch,et al.  High-Resolution Global Monitoring of Urban Settlements , 2013 .

[4]  Adriaan van den Bos,et al.  The multivariate complex normal distribution-a generalization , 1995, IEEE Trans. Inf. Theory.

[5]  R. Garello,et al.  Radargrammetric processing for 3-D building extraction from high-resolution airborne SAR data , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[6]  Hélène Oriot,et al.  Rectangular building extraction from stereoscopic airborne Radar images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[7]  J. S. Lee,et al.  Polarimetric SAR characterization of man-made structures in urban areas using normalized circular-pol correlation coefficients , 2008 .

[8]  Thomas Esch,et al.  Unsupervised high-resolution global monitoring of urban settlements , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[9]  Laurent Ferro-Famil,et al.  Scene characterization using subaperture polarimetric SAR data , 2003, IEEE Trans. Geosci. Remote. Sens..

[10]  E. Pottier,et al.  Polarimetric Radar Imaging: From Basics to Applications , 2009 .

[11]  L. Ferro-Famil,et al.  Urban area remote sensing from L-band PolSAR data using Time-Frequency techniques , 2007, 2007 Urban Remote Sensing Joint Event.

[12]  Richard Bamler,et al.  Ghost Persistent Scatterers Related to Multiple Signal Reflections , 2011, IEEE Geoscience and Remote Sensing Letters.

[13]  E. S. Pearson,et al.  On the Problem of the Most Efficient Tests of Statistical Hypotheses , 1933 .

[14]  R. Horn.  The DLR airborne SAR project E-SAR , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[15]  S. S. Wilks.  The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses , 1938 .

[16]  Gui Gao,et al.  Statistical Modeling of SAR Images: A Survey , 2010, Sensors.

[17]  Xinwu Li,et al.  Non-zero mean statistical models for urban area polarization SAR images , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[18]  Andreas Schenk,et al.  Delineation of Urban Footprints From TerraSAR-X Data by Analyzing Speckle Characteristics and Intensity Information , 2010, IEEE Transactions on Geoscience and Remote Sensing.