P
Peter M. Atkinson
Researcher at Lancaster University
Publications - 507
Citations - 22158
Peter M. Atkinson is an academic researcher from Lancaster University. The author has contributed to research in topics: Land cover & Population. The author has an hindex of 72, co-authored 470 publications receiving 18015 citations. Previous affiliations of Peter M. Atkinson include Chinese Academy of Sciences & University of Sheffield.
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Introduction Neural networks in remote sensing
TL;DR: The feed-forward back-propagation multi-layer perceptron (MLP) is the type of neural network most commonly encountered in remote sensing and is used in many of the papers in this special issue.
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Urbanization, malaria transmission and disease burden in Africa
Simon I. Hay,Carlos A Guerra,Andrew J. Tatem,Peter M. Atkinson,Robert W. Snow,Robert W. Snow +5 more
TL;DR: The effect of urbanization on both the impact of malaria transmission and the concomitant improvements in access to preventative and curative measures is described.
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Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy
Peter M. Atkinson,R. Massari +1 more
TL;DR: In this paper, a logistic regression was used to model the relation between landsliding and several independent variables (geology, dip, strike, strata-slope interaction, aspect, density of lineaments and slope angle) for a small area of the central Apennines, Italy.
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Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
Victor Rodriguez-Galiano,Mario Chica-Olmo,F. Abarca-Hernandez,Peter M. Atkinson,C. Jeganathan +4 more
TL;DR: In this article, a random forest classifier was applied to spectral as well as mono- and multi-seasonal textural features extracted from Landsat TM imagery to increase the accuracy of land cover classification over a complex Mediterranean landscape.
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Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology
TL;DR: In this article, the authors assessed four techniques: Fourier analysis, asymmetric Gaussian model, double logistic model and the Whittaker filter for smoothing multi-temporal satellite sensor observations with the ultimate purpose of deriving an appropriate annual vegetation growth cycle and estimating phenological parameters reliably.