Jet Propulsion Laboratory AIRSAR Research Articles

Topography Retrieval From Single-Pass POLSAR Data Based on the Polarization-Dependent Intensity Ratio

At present, many polarimetric synthetic aperture radar (POLSAR) remote sensing applications still perform radio metric terrain correction, whereas other types of terrain slope analysis rely on Shuttle Radar Topography Mission and Advanced Spaceborne Thermal Emission and Reflection Radiometer digital elevation model (DEM) databases. However, the terrain relief characteristics present in fully POLSAR images can also be used for topography retrieval from single-flight data. Based on the analysis of the polarization signature peak shift, we propose a new topography retrieval algorithm based on the polarization-dependent intensity ratio. The intensity term conforms to both the polarimetric orientation angle shift model and the Lambertian scattering model, which is refined for synthetic aperture radar (SAR) imaging geometry. This is useful in avoiding solving an ill-conditioned problem, and it is more practical than considering the two models independently. Another key idea of this algorithm is the derivation of a second-order residual error equation for revising the ground-range slope error with homogeneous media using the full multigrid (FMG) technique. Furthermore, the “fake” topographic relief effect induced by geophysical terrain variations is also considered and reduced by including an $\bar{\alpha} $ scattering angle-based weighting coefficient in the second-order residual error equation. In addition, a first-order residual error equation combined with tie points is proposed for calculating the least squares estimation of the height from slope integration by FMG. National Aeronautics and Space Administration Jet Propulsion Laboratory AIRSAR and UAVSAR L-band POLSAR and interferometry SAR DEM data are used to illustrate and validate this algorithm. Our results demonstrate the high potential of this method for employing a low- resolution DEM to match the corresponding high-resolution POLSAR data.

Integration of optical and radar classifications for mapping pasture type in Western Australia

In this study, independent classifications of Landsat Thematic Mapper imagery and Jet Propulsion Laboratory AirSAR were combined to create an integrated classification of pasture and other vegetation types for a study area in the agricultural zone of Western Australia. The resulting classification combines greenness and brightness information from optical data with structure and water content information from synthetic aperture radar (SAR). Field observations of vegetation type, botanical composition, ground cover percentage, wet and dry biomass, canopy height, and soil water content were collected at 34 sites representing a range of pastures, browse shrubs, and crops. An unsupervised version of the Complex Wishart classification procedure, based on preserving scattering characteristics from the Freeman and Durden backscatter decomposition, was applied to the C-, L-, and P-band polarimetric SAR data. The optical classification was carried out using a principle component analysis on the green, red, and near-infrared bands and clustering on the basis of a class centroid distance measure and knowledge of ground targets. These two classification results were then fused together. Assessment of a confusion matrix using the individual sites showed that identification of more uniform, dense, and structurally distinct canopies was better than that of more diverse, sparse, and structurally ambiguous canopies, as the former were better represented by the canopy height attribute used in the SAR classification component. The optical classification enabled correction of SAR misclassification of vegetation due to surface roughness and soil moisture effects, or similar backscatter responses from herbaceous or arboreal canopies. The results show that simplification of vegetation into groups based upon properties with sensitive responses in both the optical and SAR domains, and combination of separate SAR and optical classifications, has potential for improving classification of diverse and heterogeneous herbaceous and browse cover in grazing lands. However, collection of ground calibration data must be at an appropriate spatial scale and include canopy and surface measurements directly related to backscatter mechanisms and spectral sensitivity.

Compact polarimetry based on symmetry properties of geophysical media: the /spl pi//4 mode

We assess the performance of synthetic aperture radar (SAR) compact polarimetry architectures based on mixed basis measurements, where the transmitter polarization is either circular or orientated at 45/spl deg/(/spl pi//4), and the receivers are at horizontal and vertical polarizations with respect to the radar line of sight. An original algorithm is proposed to reconstruct the full polarimetric (FP) information from this architecture. The performance assessment is twofold: it first concerns the level of information preserved in comparison with FP, both for point target analysis and crop fields classification, using L-band SIRC/XSAR images acquired over Landes forest and Jet Propulsion Laboratory AIRSAR images acquired over Flevoland. Then, it addresses the space implementation complexity, in terms of processed swath, downloading features, power budget, calibration, and ionospheric effects. The polarization uniqueness in transmission of this mixed basis mode, hereafter referred to as the /spl pi//4 mode, maintains the standard lower pulse repetition frequency operation and hence maximizes the coverage of the sensor. Because of the mismatch between transmitter and receiver basis, the power budget is deteriorated by a factor of 3 dB, but it can partly be compensated.

Unsupervised terrain classification preserving polarimetric scattering characteristics

In this paper, we proposed an unsupervised terrain and land-use classification algorithm using polarimetric synthetic aperture radar data. Unlike other algorithms that classify pixels statistically and ignore their scattering characteristics, this algorithm not only uses a statistical classifier, but also preserves the purity of dominant polarimetric scattering properties. This algorithm uses a combination of a scattering model-based decomposition developed by Freeman and Durden and the maximum-likelihood classifier based on the complex Wishart distribution. The first step is to apply the Freeman and Durden decomposition to divide pixels into three scattering categories: surface scattering, volume scattering, and double-bounce scattering. To preserve the purity of scattering characteristics, pixels in a scattering category are restricted to be classified with other pixels in the same scattering category. An efficient and effective class initialization scheme is also devised to initially merge clusters from many small clusters in each scattering category by applying a merge criterion developed based on the Wishart distance measure. Then, the iterative Wishart classifier is applied. The stability in convergence is much superior to that of the previous algorithm using the entropy/anisotropy/Wishart classifier. Finally, an automated color rendering scheme is proposed, based on the classes' scattering category to code the pixels to resemble their natural color. This algorithm is also flexible and computationally efficient. The effectiveness of this algorithm is demonstrated using the Jet Propulsion Laboratory's AIRSAR and the German Aerospace Center's (DLR) E-SAR L-band polarimetric synthetic aperture radar images.

Texture and speckle statistics in polarimetric SAR synthesized images

We investigate in this paper the one-point statistical properties of the backscattered power derived by polarization synthesis of polarimetric synthetic aperture radar (SAR) observations. In particular, we focus our attention on the normalized second moment of intensity and its dependency on the polarization state. For the analysis of this dependency, a novel graphical representation - an extension of the polarization response - is introduced: the polarimetric texture signature. The second moment of backscattered power characterizes statistically the variation of the radar signal due to speckle and the underlying radar cross section. The classical texture product model with a scalar radar reflectivity implies that the normalized second moment of intensity does not depend on the polarization state. However, such dependency is found in experimental observations, a fact that calls for further investigation of the phenomenon. Considering at first speckle statistics for homogeneous areas having no texture, it is demonstrated that correlation among the single-look speckle patterns, which are added on an intensity basis in a multilook operation, is responsible for a weak polarization dependency of the normalized second moment. Concerning the textural properties, a new model is proposed - the mixture model - where it is assumed that polarimetrically diverse scattering mechanisms contribute to the total return from an ensemble of resolution elements. Numerical simulations are used to reconstruct the texture signatures according to the mixture model, starting from simple assumptions related to scattering processes from natural targets. It is found that the polarimetric texture signature can be an interesting discriminator of weak targets against clutter, when only polarimetric diversity and not radiometric diversity plays a role. The effects predicted by the theory are confirmed by experimental analysis of polarimetric data acquired by the Jet Propulsion Laboratory AIRSAR sensor. Finally a classification scheme based on the polarimetric texture signature is proposed.

Estimation of canopy water content in Konza Prairie grasslands using synthetic aperture radar measurements during FIFE

This paper presents the development of an algorithm to retrieve the canopy water content of natural grasslands and pasture from synthetic aperture radar (SAR) measurements. The development of this algorithm involves three interrelated steps: (1) calibration of SAR data for ground topographic variations, (2) development and validation of backscatter model for grass canopies, and (3) estimation of canopy water content by inverting a backscattered model for cross‐polarized ratio. The polarimetric radar data acquired by the Jet Propulsion Laboratory AIRSAR system during the 1989 First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) are used for this study. The SAR data have been calibrated and corrected for the topographical effects by using the digital elevation map of the study area. The backscattering coefficients obtained from the SAR data for each pixel are related to the canopy and soil parameters by employing a discrete random media model for vegetation. The model simulations indicate that biomass variations and surface treatments (burned and unburned) of grass canopies affect the C‐band backscatter signal but does not influence the L‐band signal. This model is then validated and adjusted over training areas where ground measurements were collected. An inversion technique is proposed to estimate the canopy water content by using the cross‐polarized and copolarized ratios of the SAR data at C band. The result of the inversion algorithm shows a good agreement with the grass biomass data collected during FIFE 1989 intensive field campaign.

Maximum likelihood estimation of K distribution parameters for SAR data

The K distribution has proven to be a promising and useful model for backscattering statistics in synthetic aperture radar (SAR) imagery. However, most studies to date have relied on a method of moments technique involving second and fourth moments to estimate the parameters of the K distribution. The variance of these parameter estimates is large in cases where the sample size is small and/or the true distribution of backscattered amplitude is highly non-Rayleigh. The present authors apply a maximum likelihood estimation method directly to the K distribution. They consider the situation for single-look SAR data as well as a simplified model for multilook data. They investigate the accuracy and uncertainties in maximum likelihood parameter estimates as functions of sample size and the parameters themselves. They also compare their results with those from a new method given by Raghavan (1991) and from a nonstandard method of moments technique; maximum likelihood parameter estimates prove to be at least as accurate as those from the other estimators in all cases tested, and are more accurate in most cases. Finally, they compare the simplified multilook model with nominally four-look SAR data acquired by the Jet Propulsion Laboratory AIRSAR over sea ice in the Beaufort Sea during March 1988. They find that the model fits data from both first-year and multiyear ice well and that backscattering statistics from each ice type are moderately non-Rayleigh. They note that the distributions for the data set differ too little between ice types to allow discrimination based on differing distribution parameters.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>