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

Very High Resolution Tomographic SAR Inversion for Urban Infrastructure Monitoring — A Sparse and Nonlinear Tour

The topic of this thesis is very high resolution (VHR) tomographic SAR inversion for urban infrastructure monitoring. To this end, SAR tomography and differential SAR tomography are demonstrated using TerraSAR-X spotlight data for providing 3-D and 4-D (spatial-temporal) maps of an entire high rise city area including layover separation and estimation of deformation of the buildings. A compressive sensing based estimator (SL1MMER) tailored to VHR SAR data is developed for tomographic SAR inversion by exploiting the sparsity of the signal. A systematic performance assessment of the algorithm is performed regarding elevation estimation accuracy, super-resolution and robustness. A generalized time warp method is proposed which enables differential SAR tomography to estimate multi-component nonlinear motion. All developed methods are validated with both simulated and extensive processing of large volumes of real data from TerraSAR-X

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

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

Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations

Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space

Error Propagation Analysis for Remotely Sensed Aboveground Biomass

Edited version available. Full version will remain embargoed due to copyright. AS DCAbstract Above-Ground Biomass (AGB) assessment using remote sensing has been an active area of research since the 1970s. However, improvements in the reported accuracy of wide scale studies remain relatively small. Therefore, there is a need to improve error analysis to answer the question: Why is AGB assessment accuracy still under doubt? This project aimed to develop and implement a systematic quantitative methodology to analyse the uncertainty of remotely sensed AGB, including all perceptible error types and reducing the associated costs and computational effort required in comparison to conventional methods. An accuracy prediction tool was designed based on previous study inputs and their outcome accuracy. The methodology used included training a neural network tool to emulate human decision making for the optimal trade-off between cost and accuracy for forest biomass surveys. The training samples were based on outputs from a number of previous biomass surveys, including 64 optical data based studies, 62 Lidar data based studies, 100 Radar data based studies, and 50 combined data studies. The tool showed promising convergent results of medium production ability. However, it might take many years until enough studies will be published to provide sufficient samples for accurate predictions. To provide field data for the next steps, 38 plots within six sites were scanned with a Leica ScanStation P20 terrestrial laser scanner. The Terrestrial Laser Scanning (TLS) data analysis used existing techniques such as 3D voxels and applied allometric equations, alongside exploring new features such as non-plane voxel layers, parent-child relationships between layers and skeletonising tree branches to speed up the overall processing time. The results were two maps for each plot, a tree trunk map and branch map. An error analysis tool was designed to work on three stages. Stage 1 uses a Taylor method to propagate errors from remote sensing data for the products that were used as direct inputs to the biomass assessment process. Stage 2 applies a Monte Carlo method to propagate errors from the direct remote sensing and field inputs to the mathematical model. Stage 3 includes generating an error estimation model that is trained based on the error behaviour of the training samples. The tool was applied to four biomass assessment scenarios, and the results show that the relative error of AGB represented by the RMSE of the model fitting was high (20-35% of the AGB) in spite of the relatively high correlation coefficients. About 65% of the RMSE is due to the remote sensing and field data errors, with the remaining 35% due to the ill-defined relationship between the remote sensing data and AGB. The error component that has the largest influence was the remote sensing error (50-60% of the propagated error), with both the spatial and spectral error components having a clear influence on the total error. The influence of field data errors was close to the remote sensing data errors (40-50% of the propagated error) and its spatial and non-spatial Overall, the study successfully traced the errors and applied certainty-scenarios using the software tool designed for this purpose. The applied novel approach allowed for a relatively fast solution when mapping errors outside the fieldwork areas.HCED iraq, Middle Technical Universit

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Air Force Institute of Technology Research Report 2015

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics