Semantic Segmentation of Remote-Sensing Images Through Fully Convolutional Neural Networks and Hierarchical Probabilistic Graphical Models

Deep learning (DL) is currently the dominant approach to image classification and segmentation, but the performances of DL methods are remarkably influenced by the quantity and quality of the ground truth (GT) used for training. In this article, a DL method is presented to deal with the semantic segmentation of very-high-resolution (VHR) remote-sensing data in the case of scarce GT. The main idea is to combine a specific type of deep convolutional neural networks (CNNs), namely fully convolutional networks (FCNs), with probabilistic graphical models (PGMs). Our method takes advantage of the intrinsic multiscale behavior of FCNs to deal with multiscale data representations and to connect them to a hierarchical Markov model (e.g., making use of a quadtree). As a consequence, the spatial information present in the data is better exploited, allowing a reduced sensitivity to GT incompleteness to be obtained. The marginal posterior mode (MPM) criterion is used for inference in the proposed framework. To assess the capabilities of the proposed method, the experimental validation is conducted with the ISPRS 2D Semantic Labeling Challenge datasets on the cities of Vaihingen and Potsdam, with some modifications to simulate the spatially sparse GTs that are common in real remote-sensing applications. The results are quite significant, as the proposed approach exhibits a higher producer accuracy than the standard FCNs considered and especially mitigates the impact of scarce GTs on minority classes and small spatial details

Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for Remote Sensing Image Classification

The method presented in this paper for semantic segmentation of multiresolution remote sensing images involves convolutional neural networks (CNNs), in particular fully convolutional networks (FCNs), and hierarchical probabilistic graphical models (PGMs). These approaches are combined to overcome the limitations in classification accuracy of CNNs for small or non-exhaustive ground truth (GT) datasets. Hierarchical PGMs, e.g., hierarchical Markov random fields (MRFs), are structured output learning models that exploit information contained at different image scales. This perfectly matches the intrinsically multiscale behavior of the processes of a CNN (e.g., pooling layers). The framework consists of a hierarchical MRF on a quadtree and a planar Markov model on each layer, modeling the interactions among pixels and accounting for both the multiscale and the spatial-contextual information. The marginal posterior mode criterion is used for inference. The adopted FCN is the U-Net and the experimental validation is conducted on the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, with some modifications to approach the case of scarce GTs and to assess the classification accuracy of the proposed technique. The proposed framework attains a higher recall compared to the considered FCNs, progressively more relevant as the training set is further from the ideal case of exhaustive GTs

Automatic Image Selection in Photogrammetric Multi-view Stereo Methods

This paper brings together a team of specialists in optical metrology, museum curation, collection digitization and 3D development to describe and illustrate by example a method for the selection of the most suitable camera views, vantage viewpoints, from a large image dataset intended for metric 3D artefact reconstruction. The presented approach is capable of automatically identifying and processing the most appropriate images from a multi-image photogrammetric network captured by an imaging specialist. The aim is to produce a 3D model suited to a wide range of museum uses, including visitor interactives. The approach combines off-the-shelf imaging equipment with rigorous photogrammetric bundle adjustment and multi-view stereo (MVS), supported by an image selection process that is able to take into account range-related and visibility-related constraints. The paper focusses on the two key steps of image clustering and iterative image selection. The developed method is illustrated by the 3D recording of four ancient Egyptian artefacts from the Petrie Museum of Egyptian Archaeology at UCL, with an analysis taking into account completeness, coordination uncertainty and required number of images. Comparison is made against the baseline of the established CMVS (Clustering Views for Multi-view Stereo), which is a free package for selecting vantage images within a huge image collection. For the museum, key outputs from the 3D recording process are visitor interactives which are built around high quality textured mesh models. The paper therefore considers the quality of the output from each process as input to texture model generation. Results demonstrate that whilst both methods can provide high quality records, our new method, Image Network Designer (IND), can provide a better image selection for MVS than CMVS in terms of coordination uncertainty and completeness of the final model for the museum recording of artefacts. Furthermore, the improvements gained, particularly in model completeness, minimise the significant overhead in mesh editing needed to provide a more direct and economical route to 3D model output

Spectral breaks as a signature of cosmic ray induced turbulence in the Galaxy

We show that the complex shape of the cosmic ray (CR) spectrum, as recently measured by PAMELA and inferred from Fermi-LAT gamma-ray observations of molecular clouds in the Gould belt, can be naturally understood in terms of basic plasma astrophysics phenomena. A break from a harder to a softer spectrum at blue rigidity R\simeq 10 GV follows from a transition from transport dominated by advection of particles with Alfven waves to a regime where diffusion in the turbulence generated by the same CRs is dominant. A second break at R\simeq 200 GV happens when the diffusive propagation is no longer determined by the self-generated turbulence, but rather by the cascading of externally generated turbulence (for instance due to supernova (SN) bubbles) from large spatial scales to smaller scales where CRs can resonate. Implications of this scenario for the cosmic ray spectrum, grammage and anisotropy are discussed.Comment: 4 pages, 3 figures, to appear in Phys. Rev. Letter

The Cosmological Evolution of the Average Mass Per Baryon

Subsequent to the early Universe quark-hadron transition the universal baryon number is carried by nucleons: neutrons and protons. The total number of nucleons is preserved as the Universe expands, but as it cools lighter protons are favored over heavier neutrons reducing the average mass per baryon. During primordial nucleosynthesis free nucleons are transformed into bound nuclides, primarily helium, and the nuclear binding energies are radiated away, further reducing the average mass per baryon. In particular, the reduction in the average mass per baryon resulting from Big Bang Nucleosynthesis (BBN) modifies the numerical factor relating the baryon (nucleon) mass and number densities. Here the average mass per baryon, m_B, is tracked from the early Universe to the present. The result is used to relate the present ratio of baryons to photons (by number) to the present baryon mass density at a level of accuracy commensurate with that of recent cosmological data, as well as to estimate the energy released during post-BBN stellar nucleosynthesis.Comment: 5 pages; no figures; updated references; final version published in JCAP, 10 (2006) 01

Using BBN in cosmological parameter extraction from CMB: a forecast for Planck

Data from future high-precision Cosmic Microwave Background (CMB) measurements will be sensitive to the primordial Helium abundance $Y_p$. At the same time, this parameter can be predicted from Big Bang Nucleosynthesis (BBN) as a function of the baryon and radiation densities, as well as a neutrino chemical potential. We suggest to use this information to impose a self-consistent BBN prior on $Y_p$ and determine its impact on parameter inference from simulated Planck data. We find that this approach can significantly improve bounds on cosmological parameters compared to an analysis which treats $Y_p$ as a free parameter, if the neutrino chemical potential is taken to vanish. We demonstrate that fixing the Helium fraction to an arbitrary value can seriously bias parameter estimates. Under the assumption of degenerate BBN (i.e., letting the neutrino chemical potential $\xi$ vary), the BBN prior's constraining power is somewhat weakened, but nevertheless allows us to constrain $\xi$ with an accuracy that rivals bounds inferred from present data on light element abundances.Comment: 14 pages, 4 figures; v2: minor changes, matches published versio

Magnetization reversal driven by low dimensional chaos in a nanoscale ferromagnet

Energy-efficient switching of magnetization is a central problem in nonvolatile magnetic storage and magnetic neuromorphic computing. In the past two decades, several efficient methods of magnetic switching were demonstrated including spin torque, magneto-electric, and microwave-assisted switching mechanisms. Here we experimentally show that low-dimensional magnetic chaos induced by alternating spin torque can strongly increase the rate of thermally-activated magnetic switching in a nanoscale ferromagnet. This mechanism exhibits a well-pronounced threshold character in spin torque amplitude and its efficiency increases with decreasing spin torque frequency. We present analytical and numerical calculations that quantitatively explain these experimental findings and reveal the key role played by low-dimensional magnetic chaos near saddle equilibria in enhancement of the switching rate. Our work unveils an important interplay between chaos and stochasticity in the energy assisted switching of magnetic nanosystems and paves the way towards improved energy efficiency of spin torque memory and logic

Nutation Spectroscopy of a Nanomagnet Driven into Deeply Nonlinear Ferromagnetic Resonance

Strongly out-of-equilibrium regimes in magnetic nanostructures exhibit novel properties, linked to the nonlinear nature of magnetization dynamics, which are of great fundamental and practical interest. Here, we demonstrate that ferromagnetic resonance driven by microwave magnetic fields can occur with substantial spatial coherency at an unprecedented large angle of magnetization precessions, which is normally prevented by the onset of spin-wave instabilities and magnetization turbulent dynamics. Our results show that this limitation can be overcome in nanomagnets, where the geometric confinement drastically reduces the density of spin-wave modes. When the obtained deeply nonlinear ferromagnetic resonance regime is perturbed, the magnetization undergoes eigenoscillations around the steady state due to torques tending to restore the stable large-angle periodic trajectory. These eigenoscillations are substantially different from the usual spin-wave modes around the ground state because their existence is connected to the presence of a large coherent precession. They are experimentally investigated by a new spectroscopic technique based on the application of a second microwave excitation field that is tuned to resonantly drive them. This two-tone spectroscopy enables us to show that they consist in slow coherent magnetization nutations around the large-angle steady precession, whose frequencies are set by the balance of restoring torques. Our experimental findings are well accounted for by an analytical model derived for systems with uniaxial symmetry. They also provide a new means for controlling highly nonlinear magnetization dynamics in nanostructures, opening interesting applicative opportunities in the context of magnetic nanotechnologies

WMAP 5-year constraints on lepton asymmetry and radiation energy density: Implications for Planck

In this paper we set bounds on the radiation content of the Universe and neutrino properties by using the WMAP-5 year CMB measurements complemented with most of the existing CMB and LSS data (WMAP5+All),imposing also self-consistent BBN constraints on the primordial helium abundance. We consider lepton asymmetric cosmological models parametrized by the neutrino degeneracy parameter and the variation of the relativistic degrees of freedom, due to possible other physical processes occurred between BBN and structure formation epochs. We find that WMAP5+All data provides strong bounds on helium mass fraction and neutrino degeneracy parameter that rivals the similar bounds obtained from the conservative analysis of the present data on helium abundance. We also find a strong correlation between the matter energy density and the redshift of matter-radiation equality, z_re, showing that we observe non-zero equivalent number of relativistic neutrinos mainly via the change of the of z_re, rather than via neutrino anisotropic stress claimed by the WMAP team. We forecast that the CMB temperature and polarization measurements observed with high angular resolutions and sensitivities by the future Planck satellite will reduce the errors on these parameters down to values fully consistent with the BBN bounds

Organic residue analysis of Egyptian votive mummies and their research potential

YesVast numbers of votive mummies were produced in Egypt during the Late Pharaonic, Ptolemaic, and Roman periods. Although millions remain in situ, many were removed and have ultimately entered museum collections around the world. There they have often languished as uncomfortable reminders of antiquarian practices with little information available to enhance their value as artefacts worthy of conservation or display. A multi-disciplinary research project, based at the University of Manchester, is currently redressing these issues. One recent aspect of this work has been the characterization of natural products employed in the mummification of votive bundles. Using gas chromatography–mass spectrometry and the well-established biomarker approach, analysis of 24 samples from 17 mummy bundles has demonstrated the presence of oils/fats, natural waxes, petroleum products, resinous exudates, and essential oils. These results confirm the range of organic materials employed in embalming and augment our understanding of the treatment of votives. In this first systematic initiative of its kind, initial findings point to possible trends in body treatment practices in relation to chronology, geography, and changes in ideology which will be investigated as the study progresses. Detailed knowledge of the substances used on individual bundles has also served to enhance their value as display items and aid in their conservation.RCB is supported by a PhD studentship from the Art and Humanities Research Council (43019R00209). L.M. and S.A.W. are supported by a Leverhulme Trust Research Project Award (RPG-2013-143)