A new scattering mechanism enhancement scheme for polarimetric SAR images

A new scattering mechanism enhancement scheme has been developed for natural (distributed) targets based on the eigenvalues and corresponding eigenvectors of the covariance matrix in order to identify different scattering events. First, three new vectors (v/sub 1/, v/sub 2/, and v/sub 3/) were constructed from the eigenvectors of the covariance matrix with some modifications. Then, those modified vectors were weighted by the eigenvalues of the covariance matrix as a weighting function. Thus, three vertices (A, B, and C) could be obtained in the three-dimensional space. In order to utilize them equally, a triangle (/spl Delta/ABC) was constructed by connecting these three vertices. The shape of the triangle may be changed due to the different scattering mechanisms because the vertices are obtained from the combination of eigenvalues and eigenvectors. The result indicated that different scattering mechanisms can be represented by using an exterior angle derived from the interior angles of the triangle. Results obtained from the newly developed scattering enhancement scheme, when compared with the results derived from existing schemes, were in agreement in terms of the dominant scattering mechanisms, including surface scattering, double-bounce scattering, and volume scattering. The experimental results with the Spaceborne Imaging Radar version C (SIR-C) L-band full polarimetric data demonstrate the effectiveness of the new scattering enhancement scheme.

[1]  T. G. Manjunath Polarimetric radar imaging of the atmosphere , 1990 .

[2]  Eric Pottier,et al.  A review of target decomposition theorems in radar polarimetry , 1996, IEEE Trans. Geosci. Remote. Sens..

[3]  T. Tadono,et al.  Multipixel-Based, Unsupervised Classification of Polarimetric SAR Images. , 2001 .

[4]  Eric Pottier Advanced concepts in polarimetric SAR image analysis. , 2004 .

[5]  Thuy Le Toan,et al.  Polarimetric discriminators for SAR images , 1992, IEEE Trans. Geosci. Remote. Sens..

[6]  Yunhan Dong,et al.  A new decomposition of radar polarization signatures , 1998, IEEE Trans. Geosci. Remote. Sens..

[7]  Muhtar Qong Evaluation of JERS-1/SAR data for vegetation types in arid regions , 1998, Asia-Pacific Environmental Remote Sensing.

[8]  Yunhan Dong,et al.  Segmentation and classification of vegetated areas using polarimetric SAR image data , 2001, IEEE Trans. Geosci. Remote. Sens..

[9]  Leslie M. Novak,et al.  Optimal speckle reduction in polarimetric SAR imagery , 1990 .

[10]  Muhtar Qong,et al.  Sand Dune Attributes Estimated from SAR Images , 2000 .

[11]  Stephen L. Durden,et al.  Modeling and observation of the radar polarization signature of forested areas , 1989 .

[12]  Ron Kwok,et al.  Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution , 1994 .

[13]  Alois Josef Sieber,et al.  Polarimetric contrast classification of agricultural fields using MAESTRO 1 AIRSAR data , 1994 .

[14]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

[15]  Kamal Sarabandi,et al.  Knowledge-based classification of polarimetric SAR images , 1994, IEEE Trans. Geosci. Remote. Sens..

[16]  G. Liu,et al.  Study on segmentation and interpretation of multi-look polarimetric SAR images , 2000 .

[17]  Thomas L. Ainsworth,et al.  Unsupervised classification using polarimetric decomposition and the complex Wishart classifier , 1999, IEEE Trans. Geosci. Remote. Sens..

[18]  J. Zyl,et al.  Unsupervised classification of scattering behavior using radar polarimetry data , 1989 .

[19]  H. Takamura,et al.  Formation and internal structure of Tamarix cones in the Taklimakan Desert , 2002 .

[20]  Fawwaz T. Ulaby,et al.  Knowledge-based land-cover classification using ERS-1/JERS-1 SAR composites , 1996, IEEE Trans. Geosci. Remote. Sens..

[21]  Thomas L. Ainsworth,et al.  Polarimetric SAR data compensation for terrain azimuth slope variation , 2000, IEEE Trans. Geosci. Remote. Sens..

[22]  J. Zyl,et al.  Bayesian classification of polarimetric SAR images using adaptive a priori probabilities , 1992 .

[23]  S. Cloude Uniqueness of Target Decomposition Theorems in Radar Polarimetry , 1992 .