Home > Research > Publications & Outputs > A multiple-point spatially weighted k-NN classi...

Electronic data

  • AAM_IJRS

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 27/07/2016, available online: http://www.tandfonline.com/doi/full/10.1080/01431161.2016.1214300

    Accepted author manuscript, 1.98 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

A multiple-point spatially weighted k-NN classifier for remote sensing

Research output: Contribution to journalJournal articlepeer-review

Published
Close
<mark>Journal publication date</mark>08/2016
<mark>Journal</mark>International Journal of Remote Sensing
Issue number18
Volume37
Number of pages19
Pages (from-to)4441-4459
Publication StatusPublished
Early online date27/07/16
<mark>Original language</mark>English

Abstract

A novel classification method based on multiple-point statistics (MPS) is proposed in this article. The method is a modified version of the spatially weighted k-nearest neighbour (k-NN) classifier, which accounts for spatial correlation through weights applied to neighbouring pixels. The MPS characterizes the spatial correlation between multiple points of land-cover classes by learning local patterns in a training image. This rich spatial information is then converted to multiple-point probabilities and incorporated into the k-NN classifier. Experiments were conducted in two study areas, in which the proposed method for classification was tested on a WorldView-2 sub-scene of the Sichuan mountainous area and an IKONOS image of the Beijing urban area. The multiple-point weighted k-NN method (MPk-NN) was compared to several alternatives; including the traditional k-NN and two previously published spatially weighted k-NN schemes; the inverse distance weighted k-NN, and the geostatistically weighted k-NN. The classifiers using the Bayesian and Support Vector Machine (SVM) methods, and these classifiers weighted with spatial context using the Markov random field (MRF) model, were also introduced to provide a benchmark comparison with the MPk-NN method. The proposed approach increased classification accuracy significantly relative to the alternatives, and it is, thus, recommended for the identification of land-cover types with complex and diverse spatial distributions.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 27/07/2016, available online: http://www.tandfonline.com/doi/full/10.1080/01431161.2016.1214300