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In this paper, a new framework to filter speckle noise in polarimetric synthetic aperture radar (PolSAR) data is presented. The proposed filter, named the model-based PolSAR (MBPolSAR) filter, is based on exploiting the multiplicative--additive speckle noise model for multidimensional SAR data. The entries of the sample covariance matrix are processed according to this multiplicative--additive speckle noise model as a function of the complex correlation coefficient. Hence, the covariance matrix elements are processed differently. This filtering scheme improves the reduction of speckle noise and ameliorates the estimation of the polarimetric information. The filtering performances of the MBPolSAR approach are first tested quantitatively by means of simulated PolSAR data. In a second stage, they are evaluated by means of experimental SAR data acquired by the Deutsches Zentrum fur Luft-und Raumfahrt. Index Terms--Filtering, polarimetric radar, polarimetry, speckle, synthetic aperture radar (SAR).

Precise estimation of propagation parameters in precipitation media is of interest to improve the performance of communications systems and in remote sensing applications. In this paper, we present maximum-likelihood estimators of specific attenuation and specific differential phase in rain. The model used for obtaining the cited estimators assumes coherent propagation, reflection symmetry of the medium, and Gaussian statistics of the scattering matrix measurements. No assumptions about the microphysical properties of the medium are needed. The performance of the estimators is evaluated through simulated data. Results show negligible estimators bias and variances close to Cramer-Rao bounds. Index Terms--Maximum-likelihood (ML) estimation, propagation parameters, weather radar.

Synthetic aperture radar (SAR) data are affected by speckle noise, originated by the SAR system's coherent nature. The problem of speckle noise in one-dimensional (1-D) data is already solved, as speckle has a multiplicative characteristic. SAR polarimetry represents an extension to multidimensional data by the use of polarization wave diversity. As a consequence of the existence of a correlation degree between the SAR images, the 1-D speckle noise model cannot be extended to multidimensional SAR data. This paper is devoted to present a completely new speckle noise model for the complex covariance matrix describing polarimetric SAR data in the distributed scatterers case. As will be shown, this new model is able to identify which are the noise mechanisms in all the covariance matrix elements. The speckle noise model is validated by using real L-band polarimetric data acquired with the German E-SAR sensor. Index Terms--Covariance matrix, noise modeling, speckle noise, synthetic aperture radar (SAR) polarimetry.

This paper addresses the problem of interferometric phase noise reduction in synthetic aperture radar interferometry. A new phase noise model in the complex domain is introduced and validated by using both simulated and real interferograms. This noise model is also derived in the complex wavelet domain, where a novel noise reduction algorithm, which is not based on a windowing process and without the necessity of phase unwrapping, is addressed. The use of the wavelet transform allows to maintain the spatial resolution in the filtered phase image and prevents to filter low coherence areas. By using both, simulated as well as real interferometric phase images, the performance of this algorithm, in terms of noise reduction, spatial resolution maintenance, and computational efficiency, is reported and compared with other conventional filtering approaches. Index Terms--Phase noise modeling, phase noise reduction, SAR interferometry, wavelet transform.

A recently developed technique, polarimetric radar interferometry, is applied to tackle the problem of the detection of buried objects embedded in surface clutter. An experiment with a fully polarimetric radar in an anechoic chamber has been carried out using different frequency bands and baselines. The processed results show the ability of this technique to detect buried plastic mines and to measure their depth. This technique enables the detection of plastic mines even if their backscatter response is much lower than that of the surface clutter. Index Terms--Interferometry, radar polarimetry, subsurface sensing, synthetic aperture radar (SAR).

A coherence optimization method, which makes use of polarimetry to enhance the quality of SAR interferograms, has been experimentally tested under laboratory conditions in an anechoic chamber. By carefully selecting the polarization in both images, the resulting interferogram exhibits an improved coherence above the standard HH or VV channel. This higher coherence produces a lower phase variance, thus estimating the underlying topography more accurately. The potential improvement that this technique provides in the generation of digital elevation models (DEM) of non-vegetated natural surfaces has been observed for the first time on some artificial surfaces created with gravel. An experiment on a true outdoor DEM has not been accomplished yet, but the first laboratory results show that the height error for an almost planar surface can be drastically reduced within a wide range of baselines by using the optimization algorithm. This algorithm leads to three possible interferograms associated with statistically independent scattering mechanisms. The phase difference between those interferograms has been employed for extracting the height of vegetation samples. This retrieval technique has been tested on three different samples: maize, rice, and young fir trees. The inverted heights are compared with ground truth for different frequency bands. The estimates are quite variable with frequency, but their complete physical justification is still in progress. Finally, an alternative simplified scheme for the optimization is proposed. The new approach (called polarization subspace method) yields suboptimum results but is more intuitive and has been used for illustrating the working principle of the original optimization algorithm. Index Terms--Digital elevation models, interferometry, inverse problems, radar polarimetry, synthetic aperture radar, vegetation.

Ground-based synthetic aperture radar (SAR) (GB-SAR) sensors represent an effective solution for the monitoring of ground displacement episodes. Initially, the most GB-SAR sensors were based on vector network analyzers (VNA). This type of solution, characterized by a slow scanning time comparable to the decorrelation of the troposphere medium, compromised in many cases the quality of final products for the application of persistent scatterer interferomerty (PSI) techniques. The development of GB-SAR sensors based on the use of stepped linear frequency modulated continuous wave (SLFMCW) signals has led to significant improvements during the last years. They have allowed fulfilling the need of temporal homogeneity of the troposphere during the acquisition time and, moreover, they have favored the acquisition of reliable polarimetric SAR (PolSAR) measurements without drastically increasing the scanning time. This fact has boosted the inclusion of polarimetric SAR interferometry (PolInSAR) algorithms in PSI processing chains, which are demonstrating to outperform classical single-polarimetric performances. The objective of this paper is twofold. On the one hand, a general overview of the polarimetric RiskSAR sensor, developed by the Universitat Politècnica de Catalunya (UPC), is put forward as an example of SLFMCW GB-SAR system implementation. On the other hand, a complete theoretical description of ground-based SAR (GB-SAR) interferometry (GB-InSAR) techniques for PSI purposes is widely discussed. The adaptation of the Coherent Pixels Technique to obtain the linear and nonlinear components of ground displacement phenomena is proposed. In the second part of this paper, the displacement maps and time series over two very different scenarios are presented in order to show the feasibility of GB-SAR sensors for terrain displacement monitoring applications. [ABSTRACT FROM PUBLISHER]

Urban subsidence and landslides are among the greatest hazards for people and infrastructure safety and they require an especial attention to reduce their associated risks. In this framework, ground-based synthetic aperture radar (SAR) interferometry (GB-InSAR) represents a cost-effective solution for the precise monitoring of displacements. This work presents the application of GB-InSAR techniques, particularly with the RiskSAR sensor and its processing chain developed by the Remote Sensing Laboratory (RSLab) of the Universitat Politècnica de Catalunya (UPC), for the monitoring of two different types of ground displacement. An example of urban subsidence monitoring over the village of Sallent, northeastern of Spain, and an example of landslide monitoring in El Forn de Canillo, located in the Andorran Pyrenees, are presented. In this framework, the key processing particularities for each case are deeply analyzed and discussed. The linear displacement maps and time series for both scenarios are showed and compared with in-field data. For the study, fully polarimetric data acquired at X-band with a zero-baseline configuration are employed in both scenarios. The displacement results obtained demonstrate the capabilities of GB-SAR sensors for the precise monitoring of ground displacement phenomena. [ABSTRACT FROM PUBLISHER]