Abstract-In this paper, a new method to filter coherency matrices of polarimetric or interferometric data is presented. For each pixel, an adaptive neighborhood (AN) is determined by a region growing technique driven exclusively by the intensity image information. All the available intensity images of the polarimetric and interferometric terms are fused in the region growing process to ensure the validity of the stationarity assumption. Afterward, all the pixels within the obtained AN are used to yield the filtered values of the polarimetric and interferometric coherency matrices, which can be derived either by direct complex multilooking or from the locally linear minimum mean-squared error (LLMMSE) estimator. The entropy/alpha/anisotropy decomposition is then applied to the estimated polarimetric coherency matrices, and coherence optimization is performed on the estimated polarimetric and interferometric coherency matrices. Using this decomposition, unsupervised classification for land applications by an iterative algorithm based on a complex Wishart density function is also applied. The method has been tested on airborne high-resolution polarimetric interferometric synthetic aperture radar (POL-InSAR) images (Oberpfaffenhofen area-German Space Agency). For comparison purposes, the two estimation techniques (complex multilooking and LLMMSE) were tested using three different spatial supports: a fix-sized symmetric neighborhood (boxcar filter), directional nonsymmetric windows, and the proposed AN. Subjective and objective performance analysis, including coherence edge detection, receiver operating characteristics plots, and bias reduction tables, recommends the proposed algorithm as an effective POL-InSAR postprocessing technique.Index Terms-Coherency estimation, interferometry, multivariate region growing, polarimetric synthetic aperture radar.

This paper presents an application of the recent advances in the field of Spherically Invariant Random Vectors (SIRV) modelling for coherency matrix estimation in heterogeneous clutter. The complete description of the POLSAR data set is achieved by estimating the span and the normalized coherency independently. The normalized coherency describes the polarimetric diversity, while the span indicates the total received power. The main advantages of the proposed Fixed Point estimator are that it does not require any "a priori" information about the probability density function of the texture (or span) and it can be directly applied on adaptive neighborhoods. Interesting results are obtained when coupling this Fixed Point estimator with an adaptive spatial support based on the scalar span information. Based on the SIRV model, a new maximum likelihood distance measure is introduced for unsupervised POLSAR classification. The proposed method is tested with both simulated POLSAR data and airborne POLSAR images provided by the RAMSES system. Results of entropy/alpha/anisotropy decomposition, followed by unsupervised classification, allow to discuss the use of the normalized coherency and the span as two separate descriptors of POLSAR data sets.

International audienceA new generation of space-borne SAR sensors were launched in 2006-2007 with ALOS, TerraSAR-X, COSMO-Sky-Med and RadarSat-2 satellites. The data available in different bands (L, C and X bands), with High Resolution (HR) or multi-polarization modes offer new possibilities to monitor glacier displacement and surface evolution by SAR remote sensing. In this paper, the first results obtained with TerraSAR-X HR SAR image time series acquired over the temperate glaciers of the Chamonix Mont-Blanc test site are presented. This area involves well-known temperate glaciers which have been monitored and instrumented i.e. stakes for annual displacement/ablation, GPS for surface displacement and cavitometer for basal displacement, for more than 50 years. The potential of 11-day repeated X-band HR SAR data for Alpine glacier monitoring is investigated by a combined use of in situ measurements and multi-temporal images. Interpretations of HR images, analysis of interferometric pairs and performance assessments of target/texture tracking methods for glacier motion estimation are presented. The results obtained with four time series covering the Chamonix Mont-Blanc glaciers over one year show that the phase information is rarely preserved after 11 days on such glaciers, whereas the high resolution intensity information allows the main glacier features to be observed and displacement fields on the textured areas to be derived

Abstract-The frequency modulated continuous wave (FMCW) radar is an alternative to the pulse radar when the distance to the target is short. Typical FMCW radar implementations have a homodyne architecture transceiver which limits the performances for short-range applications: the beat frequency can be relatively small and placed in the frequency range affected by the specific homodyne issues (DC offset, self-mixing and 1/f noise). Additionally, one classical problem of a FMCW radar is that the voltage controlled oscillator adds a certain degree of nonlinearity which can cause a dramatic resolution degradation for wideband sweeps. This paper proposes a short-range X-band FMCW radar platform which solves these two problems by using a heterodyne transceiver and a wideband nonlinearity correction algorithm based on high-order ambiguity functions and time resampling. The platform's displacement measurement capability was tested on range profiles and synthetic aperture radar (SAR) images acquired for various targets. The displacements were computed from the interferometric phase and the measurement errors were situated below 0.1 mm for metal bar targets placed at a few meters from the radar.