13 research outputs found
Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review
This paper is focused on considering the effects of
speckle noise on the eigen decomposition of the co-
herency matrix. Based on a perturbation analysis of the
matrix, it is possible to obtain an analytical expression for
the mean value of the eigenvalues and the eigenvectors,
as well as for the Entropy, the Anisotroopy and the dif-
ferent a angles. The analytical expressions are compared
against simulated polarimetric SAR data, demonstrating
the correctness of the different expressions.Peer ReviewedPostprint (published version
Analyse et traitement de signaux partiellement polariseÌs SyntheÌse des travaux de recherche en vue de lâobtention du diploÌme dâhabilitation aÌ diriger des recherches
La syntheÌse dâune activiteÌ scientifique meneÌe pendant une dizaine dâanneÌes est lâoccasion dâeffectuer un bilan sur la strateÌgie de recherche conduite. Depuis ma theÌse en sismique jusquâaÌ mes travaux actuels en imagerie RADAR et en optique statistique, le fil conducteur est la prise en compte de la polarisation des signaux pour leur analyse et leur traitement.Ma motivation scientifique est de montrer quâune analyse rigoureuse de signaux polarimeÌtriques contribue au deÌveloppement dâun traitement adapteÌ aÌ ces donneÌes et peut aider aÌ la conception des systeÌmes dâacquisition. Les deÌveloppements meÌthodologiques preÌsenteÌs ont pour objectif de caracteÌriser lâinformation contenue dans les donneÌes polarimeÌtriques en sâappuyant sur des outils statistiques et en prenant en compte lâanalyse des pheÌnomeÌnes physiques.Pour la reÌdaction de ce document, il mâa sembleÌ inteÌressant de commencer par un premier chapitre introductif sur la polarisation. Dans ce chapitre, dâune part jâexplique pourquoi je me suis inteÌresseÌ aÌ la polarisation lors de mon doctorat portant sur lâanalyse de signaux sismiques. Dâautre part, jây preÌsente un rapide historique sur la polarisation en optique et ainsi que les principaux concepts lieÌs aÌ lâanalyse des proprieÌteÌs de polarisation en optique et en imagerie RADAR aÌ syntheÌse dâouverture.Le deuxieÌme chapitre porte sur lâanalyse de la coheÌrence de la lumieÌre partiellement polariseÌe. Depuis 2003, cette probleÌmatique motive de nombreux travaux en optique statistique. Lors de mon arriveÌe aÌ lâinstitut Fresnel en novembre 2005, Philippe ReÌfreÌgier mâa rapidement associeÌ aÌ ses travaux sur ce sujet. Contrairement aÌ ce que lâon pourrait croire, les proprieÌteÌs de coheÌrence de la lumieÌre partiellement polariseÌe ont eÌteÌ relativement peu exploreÌes. En effet, meÌme si, dâune part, lâanalyse polarimeÌtrique a connu ces dernieÌres anneÌes un deÌveloppement treÌs important et que, dâautre part, la coheÌrence des ondes totalement polariseÌes est exploiteÌe depuis de treÌs nombreuses anneÌes, le meÌlange de ces deux caracteÌristiques a eÌteÌ peu eÌtudieÌ jusquâaÌ preÌsent.Le troisieÌme chapitre porte sur lâestimation de parameÌtres de veÌgeÌtation en imagerie Radar aÌ syntheÌse dâouverture polarimeÌtrique et interfeÌromeÌtrique. Il sâagit dâun domaine ouÌ la polarisation et la coheÌrence partielle des ondes sont exploiteÌes pour une application dont lâenjeu socieÌtal est important puisquâil sâagit de lâeÌtude de la biomasse aÌ lâeÌchelle planeÌtaire. Depuis 2009, date aÌ laquelle jâai commenceÌ aÌ mâinteÌresser aÌ cette theÌmatique, nous avons obtenu avec Philippe ReÌfreÌgier, AureÌlien Arnaubec et Pascale Dubois-Fernandez plusieurs reÌsultats sur la caracteÌrisation des performances de cette technique dâimagerie. Avoir un systeÌme polarimeÌtrique et interfeÌromeÌtrique fournit des donneÌes riches, mais complexes aÌ interpreÌter. Depuis que ce type de donneÌes est accessible dans le cadre de lâanalyse environnementale de la biomasse, la plupart des eÌtudes se sont focaliseÌes : soit sur la proposition de nouveaux algorithmes de traitement pour lâestima- tion des parameÌtres de veÌgeÌtation, soit sur lâameÌlioration des modeÌles de description des meÌca- nismes de reÌtro-diffusion. Comme cela est expliqueÌ dans le troisieÌme chapitre, notre contribution est compleÌmentaire aÌ ces travaux puisquâelle consiste aÌ quantifier la preÌcision des algorithmes dâestimation au vu de la quantiteÌ dâinformation disponible dans les donneÌes, et en fonction du modeÌle physique utiliseÌ pour deÌcrire ces donneÌes
Coherent Change Detection Under a Forest Canopy
Coherent change detection (CCD) is an established technique for remotely monitoring landscapes with minimal vegetation or buildings. By evaluating the local complex correlation between a pair of synthetic aperture radar (SAR) images acquired on repeat passes of an airborne or spaceborne imaging radar system, a map of the scene coherence is obtained. Subtle disturbances of the ground are detected as areas of low coherence in the surface clutter. This thesis investigates extending CCD to monitor the ground in a forest. It is formulated as a multichannel dual-layer coherence estimation problem, where the coherence of scattering from the ground is estimated after suppressing interference from the canopy by vertically beamforming multiple image channels acquired at slightly different grazing angles on each pass. This 3D SAR beamforming must preserve the phase of the ground response. The choice of operating wavelength is considered in terms of the trade-off between foliage penetration and change sensitivity. A framework for comparing the performance of different radar designs and beamforming algorithms, as well as assessing the sensitivity to error, is built around the random-volume-over-ground (RVOG) model of forest scattering. If the ground and volume scattering contributions in the received echo are of similar strength, it is shown that an L-band array of just three channels can provide enough volume attenuation to permit reasonable estimation of the ground coherence. The proposed method is demonstrated using an RVOG clutter simulation and a modified version of the physics-based SAR image simulator PolSARproSim. Receiver operating characteristics show that whilst ordinary single-channel CCD is unusable when a canopy is present, 3D SAR CCD permits reasonable detection performance. A novel polarimetric filtering algorithm is also proposed to remove contributions from the ground-trunk double-bounce scattering mechanism, which may mask changes on the ground near trees. To enable this kind of polarimetric processing, fully polarimetric data must be acquired and calibrated. Motivated by an interim version of the Ingara airborne imaging radar, which used a pair of helical antennas to acquire circularly polarised data, techniques for the estimation of polarimetric distortion in the circular basis are investigated. It is shown that the standard approach to estimating cross-talk in the linear basis, whereby expressions for the distortion of reflection-symmetric clutter are linearised and solved, cannot be adapted to the circular basis, because the first-order effects of individual cross-talk parameters cannot be distinguished. An alternative approach is proposed that uses ordinary and gridded trihedral corner reflectors, and optionally dihedrals, to iteratively estimate the channel imbalance and cross-talk parameters. Monte Carlo simulations show that the method reliably converges to the true parameter values. Ingara data is calibrated using the method, with broadly consistent parameter estimates obtained across flights. Genuine scene changes may be masked by coherence loss that arises when the bands of spatial frequencies supported by the two passes do not match. Trimming the spatial-frequency bands to their common area of support would remove these uncorrelated contributions, but the bands, and therefore the required trim, depend on the effective collection geometry at each pixel position. The precise dependence on local slope and collection geometry is derived in this thesis. Standard methods of SAR image formation use a flat focal plane and allow only a single global trim, which leads to spatially varying coherence loss when the terrain is undulating. An image-formation algorithm is detailed that exploits the flexibility offered by back-projection not only to focus the image onto a surface matched to the scene topography but also to allow spatially adaptive trimming. Improved coherence is demonstrated in simulation and using data from two airborne radar systems.Thesis (Ph.D.) -- University of Adelaide, School of Electrical & Electronic Engineering, 202
Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery
Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100â200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps
Polarimetric Synthetic Aperture Radar
This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans
Evaluation of the potential of ALOS PALSAR L-band quadpol radar data for the retrieval of growing stock volume in Siberia
Because of the massive wood trade, illegal logging and severe damages due to fires, insects and pollution, it is necessary to monitor Siberian forests on a large-scale, frequently and accurately. One possible solution is to use synthetic aperture radar (SAR) remote sensing technique, in particular by combining polarimetric technique. In order to evaluate the potentiality of ALOS PALSAR L-band full polarimetric radar for estimation of GSV, a number of polarimetric parameters are investigated to characterise the polarisation response of forest cover. Regardless of the weather conditions, a high correlation (R=-0.87) is achieved between polarimetric coherence and GSV. The coherence in sparse forest is always higher than in dense forest. The coherence level and the dynamic range strongly depends on the weather conditions. The four-component polarimetric decomposition method has been applied to the ALOS PALSAR L-band data to compare the decomposition powers with forest growing stock volume (GSV). Double-bounce and volume scattering powers show significant correlation with GSV. The correlation between polarimetric decomposition parameters and GSV is enhanced if the ratio of ground-to-volume scattering is used instead of considering polarimetric decomposition powers separately. Two empirical models have been developed that describe the ALOS PALSAR L-band polarimetric coherence and ground-to-volume scattering ratio as a function of GSV. The models are inverted to retrieve the GSV for Siberian forests. The best RMSE of 38 mÂł/ha and RÂČ=0.73 is obtained based on polarimetric coherence. On the other hand, using the ratio of ground-to-volume scattering the best retrieval accuracy of 44 mÂł/ha and RÂČ=0.62 is achieved. The best retrieval results for both cases are observed under unfrozen condition. Saturation effects for estimated GSV versus ground-truth GSV are not observed up to 250 mÂł/ha
Polarimetric Synthetic Aperture Radar, Principles and Application
Demonstrates the benefits of the usage of fully polarimetric synthetic aperture radar data in applications of Earth remote sensing, with educational and development purposes. Includes numerous up-to-date examples with real data from spaceborne platforms and possibility to use a software to support lecture practicals. Reviews theoretical principles in an intuitive way for each application topic. Covers in depth five application domains (forests, agriculture, cryosphere, urban, and oceans), with reference also to hazard monitorin
Understanding forest health with Remote sensing-Part II-A review of approaches and data models
Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time- and labour-inte
Complex land cover classifications and physical properties retrieval of tropical forests using multi-source remote sensing
The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and synthetic aperture radar (SAR) features, and (2) to develop a non-destructive approach for above ground biomass (AGB) and forest attributes estimation employing multi-source remote sensing data (i.e. optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is useful for management of forest resources to support sustainable forest management, whereas the generation of the non-destructive approach to model forest biophysical properties (e.g. AGB and stem volume) is required to assess the forest resources more efficiently and cost-effective, and coupled with remote sensing data the model can be applied over large forest areas. This work considers study sites over tropical rain forest landscape in Indonesia characterized by different successional stages and complex vegetation structure including tropical peatland forests. The thesis begins with a brief introduction and the state of the art explaining recent trends on monitoring and modeling of forest resources using remote sensing data and approach. The research works on the integration of spectral information and texture features for forest cover mapping is presented subsequently, followed by development of a non-destructive approach for AGB and forest parameters predictions and modeling. Ultimately, this work evaluates the potential of mosaic SAR data for AGB modeling and the fusion of optical and SAR data for peatlands discrimination. The results show that the inclusion of geostatistics texture features improved the classification accuracy of optical Landsat ETM data. Moreover, the fusion of SAR and optical data enhanced the peatlands discrimination over tropical peat swamp forest. For forest stand parameters modeling, neural networks method resulted in lower error estimate than standard multi-linear regression technique, and the combination of non-destructive measurement (i.e. stem number) and remote sensing data improved the model accuracy. The up scaling of stem volume and biomass estimates using Kriging method and bi-temporal ETM image also provide favorable estimate results upon comparison with the land cover map.Die in dieser Dissertation prĂ€sentierten Ergebnisse konzentrieren sich hauptsĂ€chlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von OberflĂ€chenbedeckung basierend auf der VerknĂŒpfung von optischen Sensoren, Textureigenschaften erzeugt durch Spektraldaten und Synthetic-Aperture-Radar (SAR) features und 2) die Entwicklung eines nichtdestruktiven Verfahrens zur Bestimmung oberirdischer Biomasse (AGB) und weiterer Waldeigenschaften mittels multi-source Fernerkundungsdaten (optische Daten, SAR RĂŒckstreuung) sowie in-situ Daten. Eine zuverlĂ€ssige Karte der Landbedeckung dient der UnterstĂŒtzung von nachhaltigem Waldmanagement, wĂ€hrend eine nichtdestruktive Herangehensweise zur Modellierung von biophysikalischen Waldeigenschaften (z.B. AGB und Stammvolumen) fĂŒr eine effiziente und kostengĂŒnstige Beurteilung der Waldressourcen notwendig ist. Durch die Kopplung mit Fernerkundungsdaten kann das Modell auf groĂe WaldflĂ€chen ĂŒbertragen werden. Die vorliegende Arbeit berĂŒcksichtigt Untersuchungsgebiete im tropischen Regenwald Indonesiens, welche durch verschiedene Regenerations- und Sukzessionsstadien sowie komplexe Vegetationsstrukturen, inklusive tropischer TorfwĂ€lder, gekennzeichnet sind. Am Anfang der Arbeit werden in einer kurzen Einleitung der Stand der Forschung und die neuesten Forschungstrends in der Ăberwachung und Modellierung von Waldressourcen mithilfe von Fernerkundungsdaten dargestellt. AnschlieĂend werden die Forschungsergebnisse der Kombination von Spektraleigenschaften und Textureigenschaften zur Waldbedeckungskartierung erlĂ€utert. Desweiteren folgen Ergebnisse zur Entwicklung eines nichtdestruktiven Ansatzes zur Vorhersage und Modellierung von AGB und Waldeigenschaften, zur Auswertung von Mosaik- SAR Daten fĂŒr die Modellierung von AGB, sowie zur Fusion optischer mit SAR Daten fĂŒr die Identifizierung von TorfwĂ€ldern. Die Ergebnisse zeigen, dass die Einbeziehung von geostatistischen Textureigenschaften die Genauigkeit der Klassifikation von optischen Landsat ETM Daten gesteigert hat. Desweiteren fĂŒhrte die Fusion von SAR und optischen Daten zu einer Verbesserung der Unterscheidung zwischen TorfwĂ€ldern und tropischen SumpfwĂ€ldern. Bei der Modellierung der Waldparameter fĂŒhrte die Neural-Network-Methode zu niedrigeren FehlerschĂ€tzungen als die multiple Regressions. Die Kombination von nichtdestruktiven Messungen (z.B. Stammzahl) und Fernerkundungsdaten fĂŒhrte zu einer Steigerung der Modellgenauigkeit. Die Hochskalierung des Stammvolumens und SchĂ€tzungen der Biomasse mithilfe von Kriging und bi-temporalen ETM Daten lieferten positive SchĂ€tzergebnisse im Vergleich zur Landbedeckungskarte
Invariant Contrast Parameters of PolInSAR Homogenous RVoG Model
International audienceIt has been shown that the CramerâRao bound (CRB) can be helpful to characterize vegetation and ground height estimations based on the homogenous random volume over ground (RVoG) model and polarimetric interferometric SAR techniques. However, this model is a function of 20 unknown parameters, which makes the performance analysis a tedious task. We show that the group invariance property of the RVoG model can greatly reduce the complexity of the analysis since the CRB of the vegetation and ground heights only depends on four unknown parameters instead of 20. Furthermore, for the considered situations analyzed in this letter, only three of these four parameters have a nonnegligible influence and can be interpreted as contrast parameters. Index TermsâCramerâRao bound (CRB), polarimetric inter-ferometric SAR (PolInSAR), random volume over ground (RVoG) model