Polarimetric Coherence Research Articles

Inversion of surface parameters from polarimetric SAR

Proposes a new model for the inversion of surface roughness and soil moisture from polarimetric synthetic aperture radar (SAR) data, based on the eigenvalues and eigenvectors of the polarimetric coherency matrix. It demonstrates how three polarimetric parameters, namely the scattering entropy (H), the scattering anisotropy (A), and the alpha angle (/spl alpha/) may be used in order to decouple surface roughness from moisture content estimation offering the possibility of a straightforward inversion of these two surface parameters. The potential of the proposed inversion algorithm is investigated using fully polarimetric laboratory measurements as well as airborne L-band SAR data and ground measurements from two different test sites in Germany, the Elbe-Auen site and the Weiherbach site.

Biophysical forest type characterization in the Colombian Amazon by airborne polarimetric SAR

Fully polarimetric C-, L-, and P-band data were collected by NASA's AirSAR system in May 1993 at the Araracuara test site, a well-surveyed forest reserve in the center of the Colombian Amazon. The area is characterized by a high diversity of forest types, soil types, and flooding conditions. In this paper a polarimetric classification technique is used to assess AirSAR's potential for forest structural type mapping and, indirectly, forest biophysical characterization. Field observations were made at 23 0.1 ha plots to obtain additional quantitative descriptions on forest structure and ground surface conditions, but also to assess the suitability of existing map legends for synthetic aperture radar (SAR) mapping. It could be shown that a new type of legend leads to physically better interpretable results. it method based on iterated conditional modes is introduced and is shown to yield radar-derived classifications with a high level of agreement with the landscape-ecological map, as well as with the ground observations. The following results may indicate the high level of accuracy obtained: 15 classes can be differentiated, the average radar classification agreement ranges from 68% to 94% (depending on the type of classification and approach), and for only a few classes the agreement is less than 70%. The relation between physical forest structure and polarimetric signal properties is studied explicitly using polarimetric decomposition. A new method is introduced based on the decomposition of polarimetric coherence, instead of power. It is based on simple physical descriptions of the wave-object interaction. The accuracy of the complex coherence estimation is described using the complex Wishart distribution. Thus, several interesting physical relations between polarimetric signal and forest structure can be revealed. The physical limitations of this technique and its relation with sample size are indicated.

Fuzzy rule based classification of polarimetric SAR data

We present a classification approach for full polarimetric SAR data based on Cloude's Decomposition Theorem. The approach is rule based, making use of knowledge of both the scattering properties contained in the entropy and α-angle values plus the backscatter intensities, which lie behind the first eigenvalue of the polarimetric coherency matrix. In order to overcome imprecise decision boundaries we make use of fuzzy logic. In a final step, the derived rulebase can be supervisedly optimized by a neuro-fuzzy approach. We show the performance of our approach on a data set taken by DLR's Experimental SAR (E-SAR) in L-band.

Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier

Introduces a new classification scheme for dual frequency polarimetric SAR data sets. A (6/spl times/6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6/spl times/6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the cross-correlation matrix. These classification schemes are applied to full polarimetric P, L, and C-band SAR images of the Nezer Forest, France, acquired by the NASA/JPL AIRSAR sensor in 1989.

Dual Frequency Polarimetric SAR Data Classification and Analysis

In this paper, we introduce a new classification scheme for dual frequency polarimetric SAR data sets. A (6×6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6 by 6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class characteristics. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the crosscorrelation matrix. The analysis of the resulting clusters is realized by determining the rigorous change in polarimetric properties from one image to the other. The polarimetric variations are parameterized by 8 real coefficients derived from the decomposition of a special unitary operator on the Gell-Mann basis. These classification and analysis schemes are applied to full polarimetric P, L, and C bands SAR images of the Nezer forest acquired by NASA/JPL AIRSAR sensor (1989). 248 Ferro-Famil and Pottier

Dual Frequency Polarimetric Sar Data Classification and Analysis - Abstract *

In this paper, we introduce a new classification scheme for dual frequency polarimetric SAR data sets. A (6 x 6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6 by 6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class characteristics. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the cross-correlation matrix. The analysis of the resulting clusters is realized by determining the rigorous change in polarimetric properties from one image to the other. The polarimetric variations are parameterized by 8 real coefficients derived from the decomposition of a special unitary operator on the Gell-Mann basis. These classification and analysis schemes are applied to full polarimetric P, L, and C bands SAR

Polarimetric coherence multiplexing using high-birefringence optical-fiber sensors and short coherence sources

We describe a polarimetric coherence multiplexed system that uses a source with a short coherence length to reduce the effect of cross talk and of phase-induced intensity noise. The system is constructed with sections of highbirefringence optical fibers, which allow the differential path lengths to be matched within micrometer accuracy. A minimum detectable phase sensitivity of 3 microrad/ radicalHz has been demonstrated.