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

Differentiating Fissure-Fed Lava Flow Types and Facies Using RADAR and LiDAR: An Example from the 2014–2015 Holuhraun Lava Flow-field

Distinguishing between lava types and facies using remote sensing data is important for interpreting the emplacement history of lava flow-fields on Earth and other planetary bodies. Lava facies typically include a mixture of lava types and record the collective emplacement history of material preserved at a particular location. We seek to determine if lava facies in the 2014–2015 Holuhraun lava flow-field are discernible using radar roughness analysis. Furthermore, we also seek to distinguish between lava types using high resolution Light Detection and Ranging (LiDAR) data. We extracted circular polarization ratios (CPR) from the Uninhabited Aerial Vehicle Synthetic Aperture Radar and cross-polarization (VH/VV) data from the Sentinel-1 satellite to analyze the surface roughness of three previously mapped lava facies: rubbly, spiny, and undifferentiated rubbly–spiny. Using the Kruskal-Wallis test, we reveal that all but one pair of the facies are statistically separable. However, the populations overlap by 88%–89% for CPR and 64%–67% for VH/VV. Therefore, owing to large sample populations (n \u3e 2 × 105), slight differences in radar data may be used to probabilistically infer the presence of a particular facies, but not directly map them. We also calculated the root-mean-square slope and Hurst exponents of five different lava types using LiDAR topography (5 cm/pixel). Our results show minute differences between most of the lava types, with the exception of the rubbly pāhoehoe, which is discernible at 1σ. In brief, the presence of “transitional” lava types (e.g., rubbly pāhoehoe) within fissure-fed lava flow-fields complicates remote sensing-based mapping

Multidimensional SAR data representation and processing based on Binary Partition Trees

English: A novel multidimensional SAR data abstraction is presented, based on Binary Partition Trees (BPT). This data abstraction is employed for different applications, as data filtering and segmentation, change detection, etc. The BPT can be contructed from a Polarimetric SAR (PolSAR) image or from a serie of coregistered acquisitions, conforming a tool that enables the systematic exploitation of PolSAR datasets simultaneously in the space and time dimensions.Castellano: na nueva abstracción de datos SAR multidimensionales es presentada, basada en Árboles de Partición Binaria (BPT). Esta abstracción de datos se emplea para distintas aplicaciones, como filtrado, segmentación, detección de cambios, etc. El BPT puede construirse a partir de una imagen SAR polarimétrica o de una serie temporal de imágenes, siendo una herramienta que permite la explotación sistemática de sets de datos PolSAR simultáneamente en espacio y tiempo.Català: Una nova abstracció de dades SAR multidimensionals és presentada, basada en Arbres de Partició Binària (BPT). Aquesta abstracció de dades s'empra per a diferents aplicacions, com filtrat, segmentació, detecció de canvis, etc. El BPT es pot construir a partir d'una imatge SAR polarimètrica o d'una sèrie temporal d'imatges, sent una eina que permet l'explotació sistemàtica de sets de dades PolSAR simultàniament en espai i temps

The Physical Properties of Volcanic and Impact Melt

The emplacement mechanisms of lunar impact melt flows, that form from hypervelocity impact events, have been a subject of debate in the lunar science community, because of their unique physical properties that separate them from other geologic features. Understanding how lunar impact melt flows were emplaced on the surface of the Moon will not only grant us new information about the flow dynamics of impact melt but provide insight into the production and distribution of impact melt and how it built and modified the surfaces of planetary surfaces. Lunar impact melt flows exhibit surface roughness textures and morphologies that are analogous to terrestrial lava flows. For this reason, we seek to quantify the surface roughness of terrestrial lava flows using synthetic aperture radar (SAR) at two localities, Craters of the Moon National Monument and Preserve, Idaho and the 2014-2015 Holuhraun lava flow-field. We focus on using SAR data in this study for two reasons, (1) improve our understanding on how radar surface roughness can be connected to the emplacement mechanisms of volcanic and impact melt, and (2) to highlight the techniques capabilities and limitations for differentiating different lava flow types and lava facies. Impact melt has contrasting intrinsic properties and geologic origins to lava flows, so we include the analysis of a physical property of impact melts that influences melt behaviour. To complement our radar surface roughness analysis, we seek to constrain the temperature of the Mistastin Lake impact structure impact melt deposits by analyzing the crystallographic orientations and microstructures of zircon grains and zirconia crystals encased in melt-bearing impactites. We demonstrate in this work that without entirely understanding the capabilities and limitations of using SAR for lava flow differentiation, we will struggle to interpret the eruption dynamics and history of volcanic landforms on terrestrial bodies, which in turn limits what we can learn about impact melt emplacement. Furthermore, we discover that high temperature and pressure conditions can be constrained from an impact environment that was once superheated, which has strong implications for discovering high P-T shock indicators in other terrestrial impact structures and also in lunar impactites. In addition, our work has strong applications towards addressing high priority science goals established by research groups such as the Lunar Exploration Analysis Group