25 research outputs found
A model-free ratio based nonlocal framework for denoising of SAR and TomoSAR data
This paper introduces a general patch-based model-free framework for despeckling of single and multi-baseline synthetic aperture radar (SAR) image. Particularly, the method is based on the empirical distribution similarity between the patch containing the pixel to be restored and the patch containing a candidate similar pixel. In order to decide whether the patches follow a similar distribution, the Kolmogorov-Smirnov test is adapted. Finally, within the restoration process, the selected similar pixels are aggregated based on their relative importance obtained according to their distribution similarities. Experimental validation of the proposed methodology is provided using different real data sets and compared with existing NLSAR approach in relation to single SAR image despeckling and tomographic application for the 3D reflectivity reconstruction of volumetric media as well as permanent scatterer detection in urban environments
An Unsupervised Generative Neural Approach for InSAR Phase Filtering and Coherence Estimation
Phase filtering and pixel quality (coherence) estimation is critical in
producing Digital Elevation Models (DEMs) from Interferometric Synthetic
Aperture Radar (InSAR) images, as it removes spatial inconsistencies (residues)
and immensely improves the subsequent unwrapping. Large amount of InSAR data
facilitates Wide Area Monitoring (WAM) over geographical regions. Advances in
parallel computing have accelerated Convolutional Neural Networks (CNNs),
giving them advantages over human performance on visual pattern recognition,
which makes CNNs a good choice for WAM. Nevertheless, this research is largely
unexplored. We thus propose "GenInSAR", a CNN-based generative model for joint
phase filtering and coherence estimation, that directly learns the InSAR data
distribution. GenInSAR's unsupervised training on satellite and simulated noisy
InSAR images outperforms other five related methods in total residue reduction
(over 16.5% better on average) with less over-smoothing/artefacts around branch
cuts. GenInSAR's Phase, and Coherence Root-Mean-Squared-Error and Phase Cosine
Error have average improvements of 0.54, 0.07, and 0.05 respectively compared
to the related methods.Comment: to be published in a future issue of IEEE Geoscience and Remote
Sensing Letter
Adaptive Multitemporal SAR Image Filtering Based on the Change Detection Matrix
5 pagesInternational audienceThis letter presents an adaptive filtering approach of synthetic aperture radar (SAR) image times series based on the analysis of the temporal evolution. First, change detection matrices (CDMs) containing information on changed and unchanged pixels are constructed for each spatial position over the time series by implementing coefficient of variation (CV) cross tests. Afterwards, the CDM provides for each pixel in each image, an adaptive spatiotemporal neighborhood which is used to derive the filtered value. The proposed approach is illustrated on a time series of 25 ascending TerraSAR-X images acquired from November 6, 2009 to September 25, 2011 over the Chamonix-MontBlanc test-site which includes different kinds of change such as parking occupation, glacier surface evolution, etc
Accurate Despeckling and Estimation of Polarimetric Features by Means of a Spatial Decorrelation of the Noise in Complex PolSAR Data
In this work, we extended a procedure for the spatial decorrelation of fully-developed speckle, originally developed for single-polarization SAR data, to fully-polarimetric SAR data. The spatial correlation of the noise depends on the tapering window in the Fourier domain used by the SAR processor to avoid defocusing of targets caused by Gibbs effects. Since each polarimetric channel is focused independently of the others, the noise-whitening procedure can be performed applying the decorrelation stage to each channel separately. Equivalently, the noise-whitening stage is applied to each element of the scattering matrix before any multilooking operation, either coherent or not, is performed. In order to evaluate the impact of a spatial decorrelation of the noise on the performance of polarimetric despeckling filters, we make use of simulated PolSAR data, having user-defined polarimetric features. We optionally introduce a spatial correlation of the noise in the simulated complex data by means of a 2D separable Hamming window in the Fourier domain. Then, we remove such a correlation by using the whitening procedure and compare the accuracy of both despeckling and polarimetric features estimation for the three following cases: uncorrelated, correlated, and decorrelated images. Simulation results showed a steady improvement of performance scores, most notably the equivalent number of looks (ENL), which increased after decorrelation and closely attained the value of the uncorrelated case. Besides ENL, the benefits of the noise decorrelation hold also for polarimetric features, whose estimation accuracy is diminished by the correlation. Also, the trends of simulations were confirmed by qualitative results of experiments carried out on a true Radarsat-2 image
SMF-POLOPT: an adaptive multitemporal pol(DIn)SAR filtering and phase optimization algorithm for PSI applications
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Speckle noise and decorrelation can hamper the application and interpretation of PolSAR images. In this paper, a new adaptive multitemporal Pol(DIn)SAR filtering and phase optimization algorithm is proposed to address these limitations. This algorithm first categorizes and adaptively filters permanent scatterer (PS) and distributed scatterer (DS) pixels according to their polarimetric scattering mechanisms [i.e., the scattering-mechanism-based filtering (SMF)]. Then, two different polarimetric DInSAR (POLDInSAR) phase OPTimization methods are applied separately on the filtered PS and DS pixels (i.e., POLOPT). Finally, an inclusive pixel selection approach is used to identify high-quality pixels for ground deformation estimation. Thirty-one full-polarization Radarsat-2 SAR images over Barcelona (Spain) and 31 dual-polarization TerraSAR-X images over Murcia (Spain) have been used to evaluate the performance of the proposed algorithm. The PolSAR filtering results show that the speckle of PolSAR images has been well reduced with the preservation of details by the proposed SMF. The obtained ground deformation monitoring results have shown significant improvements, about ×7.2 (the full-polarization case) and ×3.8 (the dual-polarization case) with respect to the classical full-resolution single-pol amplitude dispersion method, on the valid pixels' densities. The excellent PolSAR filtering and ground deformation monitoring results achieved by the adaptive Pol(DIn)SAR filtering and phase optimization algorithm (i.e., the SMF-POLOPT) have validated the effectiveness of this proposed scheme.Peer ReviewedPostprint (author's final draft
deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling
Deep learning (DL) has proven to be a suitable approach for despeckling
synthetic aperture radar (SAR) images. So far, most DL models are trained to
reduce speckle that follows a particular distribution, either using simulated
noise or a specific set of real SAR images, limiting the applicability of these
methods for real SAR images with unknown noise statistics. In this paper, we
present a DL method, deSpeckNet1, that estimates the speckle noise distribution
and the despeckled image simultaneously. Since it does not depend on a specific
noise model, deSpeckNet generalizes well across SAR acquisitions in a variety
of landcover conditions. We evaluated the performance of deSpeckNet on single
polarized Sentinel-1 images acquired in Indonesia, The Democratic Republic of
Congo and The Netherlands, a single polarized ALOS-2/PALSAR-2 image acquired in
Japan and an Iceye X2 image acquired in Germany. In all cases, deSpeckNet was
able to effectively reduce speckle and restor
Recent advances in optical metasurfaces for polarization detection and engineered polarization profiles
Like amplitude, phase and frequency, polarization is one of the fundamental properties of light, which can be used to record, process and store information. Optical metasurfaces are ultrathin inhomogeneous media with planar nanostructures that can manipulate the optical properties of light at the subwavelength scale, which have become a current subject of intense research due to the desirable control of light propagation. The unprecedented capability of optical metasurfaces in the manipulation of the light’s polarization at subwavelength resolution has provided an unusual approach for polarization detection and arbitrary manipulation of polarization profiles. A compact metasurface platform has been demonstrated to detect polarization information of a light beam and to arbitrarily engineer a polarization profile that is very difficult or impossible to realize with conventional optical elements. This review will focus on the recent progress on ultrathin metasurface devices for polarization detection and realization of customized polarization profiles. Optical metasurfaces have provided new opportunities for polarization detection and manipulation, which can facilitate real-world deployment of polarization-related devices and systems in various research fields, including sensing, imaging, encryption, optical communications, quantum science, and fundamental physics
Nonlocal noise reduction method based on fringe frequency compensation for SAR interferogram
Phase noise reduction is one of the key steps for synthetic aperture radar interferometry data processing. In this article, a novel phase filtering method is proposed. The main innovation and contribution of this research is to 1) incorporate local fringe frequency (LFF) compensation technique into the nonlocal phase filtering method to include more independent and identically distributed samples for filtering; 2) modify the nonlocal phase filter from three aspects: 1) executing nonlocal filtering in the complex domain of the residual phase to avoid gray jumps in phase, 2) adaptively calculating the smoothing parameter based on the LFF and the coherence coefficient, and 3) using the integral image in similarity calculation to improve the efficiency; 3) perform Goldstein filter in high coherence areas to reduce the computation expense. Experiments based on both simulated and real data have shown that the proposed method has achieved a better performance in terms of both noise reduction and edge preservation than some existing phase filtering methods