Customizing kernel functions for SVM-based hyperspectral image classification

Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating ";relevance"; between band information and ground trut

Adaptive sampling in context-aware systems: a machine learning approach

As computing systems become ever more pervasive, there is an increasing need for them to understand and adapt to the state of the environment around them: that is, their context. This understanding comes with considerable reliance on a range of sensors. However, portable devices are also very constrained in terms of power, and hence the amount of sensing must be minimised. In this paper, we present a machine learning architecture for context awareness which is designed to balance the sampling rates (and hence energy consumption) of individual sensors with the significance of the input from that sensor. This significance is based on predictions of the likely next context. The architecture is implemented using a selected range of user contexts from a collected data set. Simulation results show reliable context identification results. The proposed architecture is shown to significantly reduce the energy requirements of the sensors with minimal loss of accuracy in context identification

Increased parental effort fails to buffer the cascading effects of warmer seas on common guillemot demographic rates

Research Funding Natural Environment Research Council Award. Grant Number: NE/R016429/1 UK-SCAPE Programme Delivering National Capability Joint Nature Conservation Committee EU ‘The Effect of Large-scale Industrial Fisheries On Non-Target Species’ FP5 Project ‘Interactions between the Marine environment, PREdators and Prey: Implications for Sustainable Sandeel Fisheries’. Grant Numbers: MS21-013, Q5RS-2000-30864 Ministry of Universities-University of ValenciaPeer reviewedPublisher PD

Ensemble Properties of Comets in the Sloan Digital Sky Survey

We present the ensemble properties of 31 comets (27 resolved and 4 unresolved) observed by the Sloan Digital Sky Survey (SDSS). This sample of comets represents about 1 comet per 10 million SDSS photometric objects. Five-band (u,g,r,i,z) photometry is used to determine the comets' colors, sizes, surface brightness profiles, and rates of dust production in terms of the Af{\rho} formalism. We find that the cumulative luminosity function for the Jupiter Family Comets in our sample is well fit by a power law of the form N(< H) \propto 10(0.49\pm0.05)H for H < 18, with evidence of a much shallower fit N(< H) \propto 10(0.19\pm0.03)H for the faint (14.5 < H < 18) comets. The resolved comets show an extremely narrow distribution of colors (0.57 \pm 0.05 in g - r for example), which are statistically indistinguishable from that of the Jupiter Trojans. Further, there is no evidence of correlation between color and physical, dynamical, or observational parameters for the observed comets.Comment: 19 pages, 8 tables, 11 figures, to appear in Icaru

SDSS Standard Star Catalog for Stripe 82: the Dawn of Industrial 1% Optical Photometry

We describe a standard star catalog constructed using multiple SDSS photometric observations (at least four per band, with a median of ten) in the $ugriz$ system. The catalog includes 1.01 million non-variable unresolved objects from the equatorial stripe 82 ($|\delta_{J2000}|<$ 1.266$^\circ$) in the RA range 20h 34m to 4h 00m, and with the corresponding $r$ band (approximately Johnson V band) magnitudes in the range 14--22. The distributions of measurements for individual sources demonstrate that the photometric pipeline correctly estimates random photometric errors, which are below 0.01 mag for stars brighter than (19.5, 20.5, 20.5, 20, 18.5) in $ugriz$, respectively (about twice as good as for individual SDSS runs). Several independent tests of the internal consistency suggest that the spatial variation of photometric zeropoints is not larger than $\sim$0.01 mag (rms). In addition to being the largest available dataset with optical photometry internally consistent at the $\sim$1% level, this catalog provides practical definition of the SDSS photometric system. Using this catalog, we show that photometric zeropoints for SDSS observing runs can be calibrated within nominal uncertainty of 2% even for data obtained through 1 mag thick clouds, and demonstrate the existence of He and H white dwarf sequences using photometric data alone. Based on the properties of this catalog, we conclude that upcoming large-scale optical surveys such as the Large Synoptic Survey Telescope will be capable of delivering robust 1% photometry for billions of sources.Comment: 63 pages, 24 figures, submitted to AJ, version with correct figures and catalog available from http://www.astro.washington.edu/ivezic/sdss/catalogs/stripe82.htm

The Milky Way Tomography with SDSS: III. Stellar Kinematics

We study Milky Way kinematics using a sample of 18.8 million main-sequence stars with r<20 and proper-motion measurements derived from SDSS and POSS astrometry, including ~170,000 stars with radial-velocity measurements from the SDSS spectroscopic survey. Distances to stars are determined using a photometric parallax relation, covering a distance range from ~100 pc to 10 kpc over a quarter of the sky at high Galactic latitudes (|b|>20 degrees). We find that in the region defined by 1 kpc <Z< 5 kpc and 3 kpc <R< 13 kpc, the rotational velocity for disk stars smoothly decreases, and all three components of the velocity dispersion increase, with distance from the Galactic plane. In contrast, the velocity ellipsoid for halo stars is aligned with a spherical coordinate system and appears to be spatially invariant within the probed volume. The velocity distribution of nearby ($Z<1$ kpc) K/M stars is complex, and cannot be described by a standard Schwarzschild ellipsoid. For stars in a distance-limited subsample of stars (<100 pc), we detect a multimodal velocity distribution consistent with that seen by HIPPARCOS. This strong non-Gaussianity significantly affects the measurements of the velocity ellipsoid tilt and vertex deviation when using the Schwarzschild approximation. We develop and test a simple descriptive model for the overall kinematic behavior that captures these features over most of the probed volume, and can be used to search for substructure in kinematic and metallicity space. We use this model to predict further improvements in kinematic mapping of the Galaxy expected from Gaia and LSST.Comment: 90 pages, 26 figures, submitted to Ap

A signal theory approach to support vector classification: the sinc kernel

Fourier-based regularisation is considered for the support vector machine classification problem over absolutely integrable loss functions. By invoking the modest assumption that the decision function belongs to a Paley–Wiener space, it is shown that the classification problem can be developed in the context of signal theory. Furthermore, by employing the Paley–Wiener reproducing kernel, namely the sinc function, it is shown that a principled and finite kernel hyper-parameter search space can be discerned, a priori. Subsequent simulations performed on a commonly-available hyperspectral image data set reveal that the approach yields results that surpass state-of-the-art benchmarks

The Milky Way Tomography With SDSS. III. Stellar Kinematics

We study Milky Way kinematics using a sample of 18.8 million main-sequence stars with r 20 degrees). We find that in the region defined by 1 kpc < Z < 5 kpc and 3 kpc < R < 13 kpc, the rotational velocity for disk stars smoothly decreases, and all three components of the velocity dispersion increase, with distance from the Galactic plane. In contrast, the velocity ellipsoid for halo stars is aligned with a spherical coordinate system and appears to be spatially invariant within the probed volume. The velocity distribution of nearby (Z < 1 kpc) K/M stars is complex, and cannot be described by a standard Schwarzschild ellipsoid. For stars in a distance-limited subsample of stars (< 100 pc), we detect a multi-modal velocity distribution consistent with that seen by HIPPARCOS. This strong non-Gaussianity significantly affects the measurements of the velocity-ellipsoid tilt and vertex deviation when using the Schwarzschild approximation. We develop and test a simple descriptive model for the overall kinematic behavior that captures these features over most of the probed volume, and can be used to search for substructure in kinematic and metallicity space. We use this model to predict further improvements in kinematic mapping of the Galaxy expected from Gaia and the Large Synoptic Survey Telescope.NSF AST-615991, AST-0707901, AST-0551161, AST-02-38683, AST-06-07634, AST-0807444, PHY05-51164NASA NAG5-13057, NAG5-13147, NNXO-8AH83GPhysics Frontier Center/Joint Institute for Nuclear Astrophysics (JINA) PHY 08-22648U.S. National Science FoundationMarie Curie Research Training Network ELSA (European Leadership in Space Astrometry) MRTN-CT-2006-033481Fermi Research Alliance, LLC, United States Department of Energy DE-AC02-07CH11359Alfred P. Sloan FoundationParticipating InstitutionsJapanese MonbukagakushoMax Planck SocietyHigher Education Funding Council for EnglandMcDonald Observator

The First Hour of Extra-galactic Data of the Sloan Digital Sky Survey Spectroscopic Commissioning: The Coma Cluster

On 26 May 1999, one of the Sloan Digital Sky Survey (SDSS) fiber-fed spectrographs saw astronomical first light. This was followed by the first spectroscopic commissioning run during the dark period of June 1999. We present here the first hour of extra-galactic spectroscopy taken during these early commissioning stages: an observation of the Coma cluster of galaxies. Our data samples the Southern part of this cluster, out to a radius of 1.5degrees and thus fully covers the NGC 4839 group. We outline in this paper the main characteristics of the SDSS spectroscopic systems and provide redshifts and spectral classifications for 196 Coma galaxies, of which 45 redshifts are new. For the 151 galaxies in common with the literature, we find excellent agreement between our redshift determinations and the published values. As part of our analysis, we have investigated four different spectral classification algorithms: spectral line strengths, a principal component decomposition, a wavelet analysis and the fitting of spectral synthesis models to the data. We find that a significant fraction (25%) of our observed Coma galaxies show signs of recent star-formation activity and that the velocity dispersion of these active galaxies (emission-line and post-starburst galaxies) is 30% larger than the absorption-line galaxies. We also find no active galaxies within the central (projected) 200 h-1 Kpc of the cluster. The spatial distribution of our Coma active galaxies is consistent with that found at higher redshift for the CNOC1 cluster survey. Beyond the core region, the fraction of bright active galaxies appears to rise slowly out to the virial radius and are randomly distributed within the cluster with no apparent correlation with the potential merger of the NGC 4839 group. [ABRIDGED]Comment: Accepted in AJ, 65 pages, 20 figures, 5 table

Automated processing of oceanic bubble images for measuring bubble size distributions underneath breaking waves

Accurate in situ measurements of oceanic bubble size distributions beneath breaking waves are needed for a better understanding of air–sea gas transfer and aerosol production processes. To achieve this goal, a novel high-resolution optical instrument for imaging oceanic bubbles was designed and built in 2013 for the High Wind Gas Exchange Study (HiWinGS) campaign in the North Atlantic Ocean. The instrument is able to operate autonomously and can continuously capture high-resolution images at 15 frames per second over an 8-h deployment. The large number of images means that it is essential to use an automated processing algorithm to process these images. This paper describes an automated algorithm for processing oceanic images based on a robust feature extraction technique. The main advantages of this robust algorithm are it is significantly less sensitive to the noise and insusceptible to the background changes in illumination, can extract circular bubbles as small as one pixel (approximately 20 μm) in radius accurately, has low computing time (approximately 5 seconds per image), and is simple to implement. The algorithm was successfully used to analyze a large number of images (850 000 images) from deployment in the North Atlantic Ocean as part of the HiWinGS campaign in 2013