Why are probabilistic laws governing quantum mechanics and neurobiology?

We address the question: Why are dynamical laws governing in quantum mechanics and in neuroscience of probabilistic nature instead of being deterministic? We discuss some ideas showing that the probabilistic option offers advantages over the deterministic one.Comment: 40 pages, 8 fig

Radar polarimetry and interferometry for remote sensing of boreal forest

Forest biomass is a key parameter of the global biosphere which is linked to many fields of research. Modeling addressing climate, ecology, and economics as well as many other prediction frameworks require an accurate assessment of global forest biomass. Methods for producing forest information are rapidly developing and traditional forest inventory by visual estimation has been gradually replaced by the use of airborne and spaceborne instruments. Nevertheless, the estimation of biomass on a global basis including boreal, temperate, and tropical forests, is still a major challenge. Among other spaceborne sensors, synthetic aperture radar (SAR) is one of the most suitable tools for large scale mapping and it has also been often used for forest mapping. However, commonly used backscattering intensity based methods do not provide a satisfactory accuracy for biomass estimation; hence, the scientific radar community has been developing more accurate means based on advanced SAR imaging and analyzing techniques, such as SAR polarimetry and interferometry. The work within this thesis contributes to this effort specifically in the field of remote sensing with the emphasis on SAR polarimetry and interferometry for boreal forest applications. The study concentrates on three main topics: polarimetric SAR image analysis, retrieval of forest height by means of SAR interferometry, and modeling of radar backscattering from trees. The main contributions of this work include a new effective approach in polarimetric target decomposition, novel polarimetric visualization schemes, an improved interferometric tree height estimation method suitable for boreal forest, interferometric tree height estimation capability demonstration for X-band, a novel method for relating SAR measurements to single tree scattering modeling, and taking the scattering modeling from a pine tree to the single needle level with accurate field models. Furthermore, the forest height estimation scheme proposed in this work potentially enables tree height estimation with existing spaceborne interferometric X-band SAR systems. The proposed method uses an interferometric coherence model and a ground elevation model to produce accurate tree height maps from single polarization interferometric SAR data. The method is demonstrated with airborne SAR measurements and will be tested in the near future with satellite data. Since tree height is related to forest biomass through tree allometry, tree height measurements from space would enable more accurate global forest biomass maps

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

Land cover and forest mapping in boreal zone using polarimetric and interferometric SAR data

Remote sensing offers a wide range of instruments suitable to meet the growing need for consistent, timely and cost-effective monitoring of land cover and forested areas. One of the most important instruments is synthetic aperture radar (SAR) technology, where transfer of advanced SAR imaging techniques from mostly experimental small test-area studies to satellites enables improvements in remote assessment of land cover on a global scale. Globally, forests are very suitable for remote sensing applications due to their large dimensions and relatively poor accessibility in distant areas. In this thesis, several methods were developed utilizing Earth observation data collected using such advanced SAR techniques, as well as their application potential was assessed. The focus was on use of SAR polarimetry and SAR interferometry to improve performance and robustness in assessment of land cover and forest properties in the boreal zone. Particular advances were achieved in land cover classification and estimating several key forest variables, such as forest stem volume and forest tree height. Important results reported in this thesis include: improved polarimetric SAR model-based decomposition approach suitable for use in boreal forest at L-band; development and demonstration of normalization method for fully polarimetric SAR mosaics, resulting in improved classification performance and suitable for wide-area mapping purposes; establishing new inversion procedure for robust forest stem volume retrieval from SAR data; developing semi-empirical method and demonstrating potential for soil type separation (mineral soil, peatland) under forested areas with L-band polarimetric SAR; developing and demonstrating methodology for simultaneous retrieval of forest tree height and radiowave attenuation in forest layer from inter-ferometric SAR data, resulting in improved accuracy and more stable estimation of forest tree height

Interferometric Processing of TanDEM-X Images for Forest Height Estimation

Biomass is one of the most desired parameters for applications like climate modelling, resource assessment or wood industry. By using allometry equations (82) it is possible to obtain biomass information from canopy height. Some studies have demonstrated that current interferometric techniques applied to airborne Synthetic Aperture Radar (SAR) images can provide fairly accurate estimates of tree height (45, 52, 53, 54). Space based interferometric methods can provide global estimates of canopy height but they require very accurate orbit information. In this work the ability of the recently launched SAR satellites TerraSAR-X and TanDEM-X to estimate canopy height is evaluated.To do this, a complete interferometric processing chain is created including SAR data reading into memory, complex interferogram calculation, interferogram flattening by at Earth approximation and image transformation to geographical coordinates.Finally the resulting phase height maps are compared with a digital elevation model and a canopy height model of the terrain under study as well as with X-band E-SAR data from the FINSAR campaign (52, 53, 54) of the same area

Vegetation/Forest Effects in Microwave Remote Sensing of Soil Moisture

This thesis includes (1) the distorted Born approximation (DBA) and an improved coherent model for vegetation-covered surfaces at L-band for data-cube based soil moisture retrieval; (2) a unified approach for combined active and passive remote sensing of vegetation-covered surfaces with the same input physical parameters; (3) Numerical Maxwell Model in 3D (NMM3D) simulations of a vegetation canopy comprising randomly distributed dielectric cylinders; and (4) a hybrid method based on the generalized T matrix of single objects and Foldy-Lax equations for NMM3D full-wave simulations of the realistic vegetation/forest with vector spherical, spheroidal and cylindrical wave expansoins. The main contributions and novelty of this thesis are NMM3D full-wave simulations of vegetation/forest canopy using the generalized T matrix of the single object and Foldy-Lax equations of multiple scattering among many objects. Before this work, the large-scale full-wave simulations of vegetation/forst such as many tree trunks were deemed very difficult. The NMM3D full-wave simulation results showed that the results of past models significantly overestimate attenuation in a vegetation/forest canopy. The NMM3D full-wave models predict transmissions that are several times greater than that of past models. A much greater microwave transmission means the microwave can better penetrate a vegetation/forest canopy and thus it can be used to retrieve soil moisture. The thesis starts with the DBA to compute the backscattering coefficients for various kinds of vegetation-covered surfaces such as pasture, wheat and canola fields. For the soybean fields, an improved coherent branching model is used. The novel feature of the analytic coherent model consists of conditional probability functions to eliminate the overlapping effects of branches in the former branching models. In order to make use of complex physical models for real time retrieval for satellite missions, the outputs of the physical model are provided as lookup-tables (data-cubes). By inverting the lookup-tables, time-series retrieval of soil moisture is performed. Next, the DBA is extended to calculate the bistatic scattering coefficients. Emissivities are calculated by integrating the bistatic scattering coefficients over the hemispherical solid angle. The backscattering coefficients and emissivities calculated using this approach form a consistent model for combined active and passive microwave remote sensing. In the analytical physical models mentioned above, as well as in another commonly used approach of the radiative transfer equation (RTE), the attenuation of the wave is accounted for by the attenuation rate per unit distance, which originates from the concept of an “effective medium”. Such a model is unsuitable for a vegetation canopy. Because of these issues, NMM3D full-wave simulations of vegetation are pursued. Firstly, the scattering of a vegetation canopy consisting of cylindrical scatterers is calculated. The approach for solving Maxwell’s equations is based on the Foldy-Lax multiple scattering equations (FL) combined with the body of revolution (BOR). For a layer of extended-cylinders distributed in clusters, the NMM3D simulations at C-band show very different results from DBA/RTE. The method FL-BOR is limited for rotationally symmetric objects such as cylinders and circular disks. To perform NMM3D full-wave simulations for realistic vegetation/forests, a hybrid method is used, which is a hybrid of the off-the-shelf techniques and newly developed techniques. The newly developed techniques are the three key steps of the hybrid method: (1) extracting the generalized T matrix of each single object using vector spheroidal/cylindrical waves, (2) vector wave transformations, and (3) solving FL for all the objects.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153347/1/huanght_1.pd

Compréhension de la rétrodiffusion des micro-ondes sur le sol nu en utilisant l'inversion des paramètres de surface, les réseaux de neurones et l'algorithme génétique

Estimates of the physical parameters of the soil surface, namely moisture content and surface roughness, are important for hydrological and agricultural studies, as they appear to be the two major parameters for runoff forecasting in an agricultural watershed. Radar has high potentiality for the remote measurement of soil surface parameters. In particular, the investigation of the radar backscattering response of bare soil surfaces is an important issue in remote sensing because of its capacity for retrieving the desired physical parameters of the surface. The objective of this study is to formulate and to constrain a methodology for solving the inverse problem for the operational retrieval of soil surface roughness and moisture. To separate the effects of the different parameters on the measured signal over complex areas, multi-technique concepts (multi-polarization, multi-angular, multi-sensor, multi-frequency, and multi-temporal) are the main solution. In this work, based on a simulation study, three different configurations, multi-polarization, multi-frequency and multi-angular, are compared to obtain the best configuration for estimating surface parameters and the multi-angular configuration gives the best results. Based on these results, this study was continued according to five different phases: (1) A new index, the NBRI (Normalized radar Backscatter soil Roughness Index), using the multi-angular approach was presented. This index can estimate and classify surface roughness in agricultural fields using two radar images with different incidence angles. (2) A new linear empirical model to estimate soil surface moisture using RADARSAT-1 data was proposed. This model can provide soil moisture with reduced errors of estimation compared to other linear models. (3) Inversion of the surface parameters using nonlinear classical methods. In this case, the Newton-Raphson method, an iterative numerical method, was used in the retrieval algorithm to solve the inverse problem. (4) In this phase, the neural network technique, with a dynamic learning method, was applied to invert the soil surface parameters from the radar data. The results were obtained through performance testing on two different input schemes (one and two data series) and two different databases (theoretical and empirical). The advantage of the multi-angular set with measured data is apparent. These results are the best in this study. (5) Finally, a novel genetic algorithm (GA) was developed to retrieve soil surface parameters. In this study, it is shown that the genetic algorithms, as an optimization technique, can estimate simultaneously soil moisture and surface roughness from only one radar image