Polarimetric synthetic-aperture radar (PolSAR) is an important emerging technology that is currently being developed and/or deployed for new manned/unmanned airborne systems and commercial spaceborne systems as well (e.g. Radarsat-2). PolSAR's major advantage over traditional synthetic-aperture radar (SAR) is its ability to measure all polarization characteristics of a radar signature. This additional polarization information is typically not readily discernable in the raw radar imagery by human visual analysis. Accordingly, fully automated strategies for target and feature recongition using sophisticated processing is required to enable geospatial or imagery analysts to make effective use of the additional polarization information. While commercial satellite imagery (CSI) already provides great value, automated fusion of high-resolution CSI with polarimetric synthetic aperture radar (PolSAR) data can provide additional scene information for analyst exploitation. Since Radarsat-2 (est. launch in 2006) will have polSAR capability, enormous amounts of polSAR data will be available in the near future.

POLARIMETRIC DECOMPOSITIONS

Polarimetric decomposition techniques may be used to investigate the intrinsic physical properties of point targets by evaluating the underlying scattering mechanisms, which are strongly related to size, shape, geometrical structure, and dielectric composition. There are many polSAR decomposition techniques such as H/alpha [1], F/D [2], A/S [3] and subaperture [4][5].

The F/D technique fits a physically-based, three-component scattering mechanism model to polSAR data without requiring any ground truth measurements. The three scattering mechanism components included in the model are volume scatter (fv) from randomly oriented dipoles, first-order Bragg surface scatter (fs), and a double-bounce scattering mechanism (fd). The model fit yields an estimate of the contribution to the total backscatter of each of the three components. The backscatter contributions can also be compared to give the relative percentage weight of each. Thus, the components provide three values for each polSAR image pixel that can be used in automated land cover classification and data fusion algorithms.

To illustrate the F/D technique, we processed a JPL AirSAR polSAR image. Radar power data over an urban location of about 1 km by 1 km is shown in Fig. 1a. The scene is labeled with the following annotations: mall, parking lot, residential, small buildings, and undeveloped. The F/D technique results are shown in Fig. 1b, where red corresponds to double-bounce scattering, green corresponds to volume scattering, and blue corresponds to surface scattering. A simple majority approach of the F/D results is shown in Fig. 1c. In Fig. 1c, the mall is dominated by double-bounce scattering; therefore, the mall area is mainly red. A mix of vegetation (green volume scattering) and homes (red double-bounce scattering) dominate the residential area. In the power image both the parking lot and small building areas are dark (low radar backscatter), but the F/D results differentiate between the parking lot (mainly blue surface scattering) and the small buildings (about equal mix of blue surface scattering and red double bounce scattering, i.e. combination of roads and buildings). The undeveloped area is vegetation cover (green volume scattering) that is distinguishable from both the parking lot and small building areas. Thus, the polSAR F/D results illustrate the usefulness of the polarization information for fully automated characterization of terrain/land cover.

Subaperture processing of polSAR imagery examines target scatter during integration of the synthetic aperture. Each target is observed by the polSAR under a set of azimuthal look angles, defined by the antenna's azimuthal aperture [4][5]. Complex targets (e.g. man-made targets, periodic structures, linear alignments of strong scatterers, etc.) are characterized by anisotropic geometrical structures. Thus, as complex targets are illuminated from different positions, they show a varying electromagnetic scattering signature. In addition, the azimuthal scattering variation can be exploited to detect complex targets in the presence of environmental clutter (e.g. vegetation). Fig. 2 illustrates this concept.

GROUND COVER SUPPRESSION USING POLARIMETRIC INTERFEROMETRY

Polarimetric Interferometry (PolInSAR) is sensitive to changes in surface scattering, even in the presence of significant volume scattering. Recently, the negative alpha filter [6] was introduced for estimating subcanopy surface parameters based on the polarimetric variation of interferometric coherence. L-band surfaces are only weak depolarizers with moderate to low entropy in single-bounce and double-bounce scattering mechanisms. Most of the high entropy contributions in the polSAR imagery come from the volume scattering component. Thus, the negative alpha filter can null out much of the polarized subcanopy surface response. Because the filter alpha value is sensitive to surface changes and insensitive to volume variations, the method may be used to isolate the surface components in mixed volume/surface scattering scenarios. A ground cover suppression example [7] is shown in Fig. 3.

REFERENCES

[1] Cloude, S. R. and E. Pottier (1997) "An entropy based classification scheme for land applications of polarimetric SAR," IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, No. 1, pp. 68-78.

[2] Freeman, A. and S. L. Durden (1998) "A three-component scattering model for polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 36, No. 3, pp. 963-973.

[3] Cameron, W. L., N. N. Youssef and L. K. Leung (1996) "Simulated polarimetric signatures of primitive Geometrical shapes," IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 3, pp. 793-803.

[4] Ferro-Famil, L., A. Reigber, E Pottier, and W.-M. Boerner (2003), "Scene characterization using subaperture polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 10, pp. 2264-2276.

[5] Souyris, J. C., C. Henry and F. Adragna (2003) "On the use of complex SAR image spectral analysis for target detection: assessment of polarimetry," IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 12, pp. 2725-2734.

[6] Cloude, S. and M. L. Williams (2005) "The negative alpha filter: a new processing technique for polarimetric SAR interferometry," IEEE Geoscience and Remote Sensing Letters, Vol. 2, No. 2.

[7] Cloude, S., M. Hellmann and M. L. Williams (2004) "Polarimetric interferometry: theory and applications," NATO LS, Vol. 81.