Kernel methods for remote sensing data analysis

@inproceedings{CampsValls2009KernelMF,
  title={Kernel methods for remote sensing data analysis},
  author={G. Camps-Valls and Lorenzo Bruzzone},
  year={2009}
}
About the editors. List of authors. Preface. Acknowledgments. List of symbols. List of abbreviations. I Introduction. 1 Machine learning techniques in remote sensing data analysis (Bjorn Waske, Mathieu Fauvel, Jon Atli Benediktsson and Jocelyn Chanussot). 1.1 Introduction. 1.2 Supervised classification: algorithms and applications. 1.3 Conclusion. Acknowledgments. References. 2 An introduction to kernel learning algorithms (Peter V. Gehler and Bernhard Scholkopf). 2.1 Introduction. 2.2 Kernels… 

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References

SHOWING 1-10 OF 87 REFERENCES

Classification of hyperspectral remote sensing images with support vector machines

This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines by understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces and concludes that SVMs are a valid and effective alternative to conventional pattern recognition approaches.

Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

A general framework based on kernel methods for the integration of heterogeneous sources of information for multitemporal classification of remote sensing images and the development of nonlinear kernel classifiers for the well-known difference and ratioing change detection methods is presented.

Signal Theory Methods in Multispectral Remote Sensing

This work focuses on the development of a Models for Multispectral Image Data Preprocessing, which combines Hyperspectral Data Characteristics with Probability Theory.

Some issues in the classification of DAIS hyperspectral data

There was no evidence to support the view that classification accuracy inevitably declines as the data dimensionality increases, and it is suggested here that greater attention should be given to the collection of training and test data that represent the range of land surface variability at the spatial scale of the image.

Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines

Here, urban hyperspectral data is spectrally classified using support vector machines (SVM) and the influence of the spectral generalization during image segmentation is directly investigated.

Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal

  • M. ChiL. Bruzzone
  • Computer Science
    IEEE Transactions on Geoscience and Remote Sensing
  • 2007
This paper addresses classification of hyperspectral remote sensing images with kernel-based methods defined in the framework of semisupervised support vector machines by considering different (S3VMs) techniques that solve optimization directly in the primal formulation of the objective function.

Investigation of the random forest framework for classification of hyperspectral data

This work investigates two approaches based on the concept of random forests of classifiers implemented within a binary hierarchical multiclassifier system, with the goal of achieving improved generalization of the classifier in analysis of hyperspectral data, particularly when the quantity of training data is limited.

Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries

  • N. Keshava
  • Computer Science
    IEEE Transactions on Geoscience and Remote Sensing
  • 2004
This paper derives a technique called band add-on (BAO) that iteratively selects bands to increase the angular separation between two spectra and demonstrates that band selection can improve the discrimination of very similar targets, while using only a fraction of the available spectral bands.

A Gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data

The performance of the Gaussian ARTMAP classification algorithm in terms of classification accuracy using independent validation data indicated was over 70% accurate when applied to an annual series of monthly 1-km advanced very high resolution radiometer (AVHRR) satellite normalized difference vegetation index (NDVI) data.

Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles

An approach has been proposed which is based on using several principal components from the hyperspectral data and build morphological profiles which can be used all together in one extended morphological profile for classification of urban structures.
...