Learning a spatial ensemble of classifiers for raster classification: A summary of results

Zhe Jiang, Shashi Shekhar, Azamat Kamzin, Joseph Knight

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Given a spatial raster framework F, a set of explanatory feature maps, training and test samples with class labels on F, as well as a base classifier type, the problem of ensemble learning in raster classification aims to learn a collection of base classifiers to minimize classification errors. The problem has important societal applications such as land cover classification but is challenging due to existence of class ambiguity from spatial heterogeneity, i.e., Samples with the same feature values may have distinct class labels in different areas. Many existing approaches are non-spatial ensembles (e.g., Bagging, boosting, random forest), which assume that learning samples follow an identical distribution. Some spatial ensemble approaches also exist, which simply partition the raster framework into several regular sub-blocks and combine classification results on each sub-block. However, these existing approaches can not address the class ambiguity issue among pixels. In contrast, this paper proposes a new spatial ensemble approach, which partitions the spatial framework into several spatial footprints to minimize class ambiguity of training samples and then learns a base classifier for each footprint. Experimental evaluations on a real world remote sensing dataset show that the proposed spatial ensemble approach outperforms existing approaches when strong class ambiguity exists.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
EditorsZhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherIEEE Computer Society
Pages15-18
Number of pages4
EditionJanuary
ISBN (Electronic)9781479942749
DOIs
StatePublished - Jan 26 2015
Event14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China
Duration: Dec 14 2014 → …

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
NumberJanuary
Volume2015-January
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Other

Other14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
CountryChina
CityShenzhen
Period12/14/14 → …

Keywords

  • class ambiguity
  • raster classification
  • remote sensing
  • spatial ensemble
  • spatial heterogeneity

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  • Cite this

    Jiang, Z., Shekhar, S., Kamzin, A., & Knight, J. (2015). Learning a spatial ensemble of classifiers for raster classification: A summary of results. In Z-H. Zhou, W. Wang, R. Kumar, H. Toivonen, J. Pei, J. Zhexue Huang, & X. Wu (Eds.), Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 (January ed., pp. 15-18). [7022572] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2015-January, No. January). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2014.166