Efficient statistical classification of satellite measurements

@article{Mills2011EfficientSC,
  title={Efficient statistical classification of satellite measurements},
  author={P. Mills},
  journal={International Journal of Remote Sensing},
  year={2011},
  volume={32},
  pages={6109 - 6132}
}
  • P. Mills
  • Published 2011
  • Mathematics, Physics
  • International Journal of Remote Sensing
Supervized statistical classification is a vital tool for satellite image processing. It is useful not only when a discrete result, such as feature extraction or surface type, is required, but also for continuum retrievals by dividing the quantity of interest into discrete ranges. Because of the high resolution of modern satellite instruments and because of the requirement for real-time processing, any algorithm has to be fast to be useful. Here we describe an algorithm based on kernel… Expand
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References

SHOWING 1-10 OF 23 REFERENCES
Isoline retrieval: An optimal sounding method for validation of advected contours
  • P. Mills
  • Geology, Computer Science
  • Comput. Geosci.
  • 2009
TLDR
In this study, isoline retrieval is shown to be a highly effective technique for atmospheric sounding, showing good agreement with both European Centre for Medium-range Weather Forecasts (ECMWF) assimilation data and radiosonde measurements. Expand
Sea ice remote sensing using AMSR‐E 89‐GHz channels
[1] Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave ScanningExpand
Inverse Methods for Atmospheric Sounding: Theory and Practice
TLDR
This book treats the inverse problem of remote sounding comprehensively, and discusses a wide range of retrieval methods for extracting atmospheric parameters of interest from the quantities such as thermal emission that can be measured remotely. Expand
Sea ice remote sensing using AMSR-E data: surface roughness and refractive index
Sea ice is a good indicator to monitor the global climate change. Many of previous studies using the satellite observations show a steady decline in Arctic sea ice. The study investigates theExpand
An introduction to kernel-based learning algorithms
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods.Expand
Automated Detection of Canola/Rapeseed Cultivation from Space: Application of new Algorithms for the Identi cation of Agricultural Plants with Multispectral Satellite Data on the Example of Canola Cultivation
The advances in biotechnology allow the use of genetically modified plants in agriculture. Whereas in the EU, this is still limited to experimental sowing, it is already practised commercially inExpand
LIBSVM: A library for support vector machines
TLDR
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail. Expand
Self-Organizing Maps
  • T. Kohonen
  • Computer Science
  • Springer Series in Information Sciences
  • 1995
TLDR
The mathematical preliminaries, background, basic ideas, and implications of the Self-Organising Map algorithm are expounded in a manner which is accessible without prior expert knowledge. Expand
A mathematical theory of communication
  • C. Shannon
  • Computer Science, Mathematics
  • Bell Syst. Tech. J.
  • 1948
In this final installment of the paper we consider the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now. To aExpand
Machine Learning, Neural and Statistical Classification
Survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning onExpand
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3
...