9,447 research outputs found
W. Lewis Civil War letter
No full textThis collection contains a letter written in November 1864 by W. Lewis, then stationed at DeValls, Bluff, Ark. The author is believed to be Walter Lewis of Company F of the 20th Iowa Infantry
Super-Resolution Land Cover Pattern Prediction Using a Hopfield Neural Network
No full textLandscape pattern represents a key variable in management and understanding of the environment, as well as driving many environmental models. Remote sensing can be used to provide information on the spatial pattern of land cover features, but analysis and classification of such imagery suffers from the problem of class mixing within pixels. Fuzzy classification techniques can estimate the class composition of image pixels. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques to provide an improved spatial representation of land cover targets larger than the size of a pixel have been developed, however, the mapping of sub-pixel scale land cover features has yet to be investigated. We recently described the application of a Hopfield neural network technique to super-resolution mapping of land cover features larger than a pixel (Tatem et al., 2000), using information of pixel composition determined from fuzzy classification, and (was but) now show how our approach can be extended in a new way (added) to predict the spatial pattern of sub-pixel scale features. The network converges to a minimum of an energy function defined as a goal and several constraints. Prior information on the typical spatial arrangement of the particular land cover types is incorporated into the energy function as a constraint. This produces a prediction of the spatial pattern of the land cover in question, at the sub-pixel scale. The technique is applied to synthetic and simulated Landsat TM imagery, and compared to results of an existing super-resolution target identification technique. Results show that the new approach (was Hopfield neural network) represents a simple, robust and efficient tool for super-resolution land cover pattern prediction from remotely sensed imagery
Cloud motion analysis
No full textContentsTechniques for the Analysis of Spatial Data (P. Atkinson N. Tate); Land Cover Classification Revisited (P. Mather); Image Classification with a Neural Network: From Completely-Crisp to Fully-Fuzzy Situations (G. Foody); Cloud Motion Analysis (H. Lewis, et al.); Methods for Estimating Image Signal-to-Noise Ratio (SNR) (G. Smith P. Curran); Modelling and Efficient Mapping of Snow Cover in the UK for Remote Sensing Validation (R. Kelly P. Atkinson); Using Variograms to Evaluate a Model for the Spatial Prediction of Minimum Air Temperature (D. Cornford); Modelling the Distribution of Cover Fraction of a Geophysical Field (J. Collins C. Woodcock); Classification of Digital Image Texture Using Variograms (J. Carr); Geostatistical Approaches for Image Classification and Assessment of Uncertainty in Geologic Processing (F. van der Meer); A Syntactic Pattern-Recognition Paradigm for the Derivation of Second-Order Thematic Information from Remotely Sensed Images (S. Barr M. Barnsley); The Rôle of Classified Imagery in Urban Spatial Analysis (V. Mesev P. Longley); Image Classification and Analysis Using Integrated GIS (J. Hinton); Per-Field Classification of Land Use Using the Forthcoming Very Fine Spatial Resolution Satellite Sensors: Problems and Potential Solutions (P. Aplin, et al.); Modelling Soil Erosion at Global and Regional Scales Using Remote Sensing and GIS Techniques (N. Drake, et al. ); Extracting Information from Remotely Sensed and GIS Data (P. Atkinson N. Tate
[Reg Lewis and band members] [picture] .
No full textTitle devised by cataloguer from information on negative bag label.; Condition: Good.; Lent for copying.; Part of the collection: Lewis collection of photographs. Group portrait of from L to R: Arthur Slade, Tom Coughlan, Ron Wills, Bob Atkinson, Sam Lawrence, Len Vial, Ted Bevan. Piano, Reg Lewis
Geostatistical classification for remote sensing: an introduction
No full textTraditional spectral classification of remotely sensed images applied on a pixel-by-pixel basis ignores the potentially useful spatial information between the values of proximate pixels. For some 30 years the spatial information inherent in remotely sensed images has been employed, albeit by a limited number of researchers, to enhance spectral classification. This has been achieved primarily by filtering the original imagery to (i) derive texture ‘wavebands’ for subsequent use in classification or (ii) smooth the imagery prior to (or after) classification. Recently, the variogram has been used to represent formally the spatial dependence in remotely sensed images and used in texture classification in place of simple variance filters. However, the variogram has also been employed in soil survey as a smoothing function for unsupervised classification. In this review paper, various methods of incorporating spatial information into the classification of remotely sensed images are considered. The focus of the paper is on the variogram in classification both as a measure of texture and as a guide to choice of smoothing function. In the latter case, the paper focuses on the technique developed for soil survey and considers the modification that would be necessary for the remote sensing case. <br/
Superresolution mapping using a hopfield neural network with fused images
No full textSuperresolution mapping is a set of techniques to increase the spatial resolution of a land cover map obtained by soft classification methods. In addition to the information from the landcover proportion images, supplementary information at the subpixel level can be used to produce more detailed and accurate land cover maps. The proposed method in this research aims to use fused imagery as an additional source of information for superresolution mapping using the Hopfield neural network (HNN). Forward and inverse models were incorporated in the HNN to support a new reflectance constraint added to the energy function. The value of the function was calculated based on a linear mixture model. In addition, a new model was used to calculate the local end member spectra for the reflectance constraint. A set of simulated images was used to test the new technique. The results suggest thatfine spatial resolution fused imagery can be used as supplementary data for superresolution mapping from a coarser spatial resolution land cover proportion imagery
Michael Lewis: Journalist and Bestselling Author
No full textMichael Lewis is a New York Times bestselling author who has written more than a dozen books on subjects ranging from politics to Wall Street. His recently released book, Fifth Risk, explores mismanagement in federal government.
His other books include The Big Short, Moneyball and The Blind Side - all of which were made into movies. Another, Liar\u27s Poker, was based partly on his experience as a bond salesman at Salomon Brothers.
Lewis is a sharp observer of politics, finance and the evolution of American culture, combining keen insight with a sharp sense of humor. He is a columnist for Bloomberg News and a contributing writer to Vanity Fair. His articles have also appeared in The New York Times Magazine, The New Yorker and Sports Illustrated
Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network
No full textLand cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery.We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool formapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale
On the influence of impact effect modelling for global asteroid impact risk distribution
No full textThe collision of an asteroid with Earth can potentially have significant consequences for the human population. The European and United States space agencies (ESA and NASA) maintain asteroid hazard lists that contain all known asteroids with a non-zero chance of colliding with the Earth in the future. Some software tools exist that are, either, capable of calculating the impact points of those asteroids, or that can estimate the impact effects of a given impact incident. However, no single tool is available that combines both aspects and enables a comprehensive risk analysis. The question is, thus, whether tools that can calculate impact location may be used to obtain a qualitative understanding of the asteroid impact risk distribution. To answer this question, two impact risk distributions that control for impact effect modelling were generated and compared. The Asteroid Risk Mitigation Optimization and Research (ARMOR) tool, in conjunction with the freely available software OrbFit, was used to project the impact probabilities of listed asteroids with a minimum diameter of 30 m onto the surface of the Earth representing a random sample (15% of all objects) of the hazard list. The resulting 261 impact corridors were visualized on a global map. Furthermore, the impact corridors were combined with Earth population data to estimate the “simplified” risk (without impact effects) and “advanced” risk (with impact effects) associated with the direct asteroid impacts that each nation faces from present to 2100 based on this sample. The relationship between risk and population size was examined for the 40 most populous countries and it was apparent that population size is a good proxy for relative risk. The advanced and simplified risk distributions were compared and the alteration of the results based on the introduction of physical impact effects was discussed. Population remained a valid proxy for relative impact risk, but the inclusion of impact effects resulted in significantly different risks, especially when considered at the national level. Therefore, consideration of physical impact effects is essential in estimating the risk to specific nations of the asteroid threat
Betty Atkinson
No full textBetty grew up with her older sister Donna, on her parents' pastoral property Humbert River Station in the Northern Territory. Like many children in the outback her schooling was done through correspondence and supervised by her mother initially and then later by a governesses. She completed her high school education at a boarding school in Adelaide. Betty commenced training as a nurse in 1965 at the Darwin Hospital. After completing her studies Betty worked overseas for a couple of years and then returned to Darwin. In 1973 she became a successful business woman owning and operating a variety of businesses in partnership with her sister Donna.
On 17th August 1974 Betty married John Cecil Atkinson. Together they owned and operated a number of businesses which included: Motorways Marine, Boatland, N.T. Pump Sales and Service, Enzed and Boatland Winnellie which provided employment and apprenticeships for many locals over the years. Their business enterprises expanded to fishing charters, waterskiing, parasailing and they also sponsored the 'Barra Fishing Classics'. Betty now resides in Proserpine and is involved with a Brahman Cattle Stud in partnership with her husband John.Business Wome
- …
