NDLI: Learning a Spatial Ensemble of Classifiers for Raster Classification: A Summary of Results

Content Provider	IEEE Xplore Digital Library
Author	Jiang, Zhe Shekhar, Shashi Kamzin, Azamat Knight, Joseph
Copyright Year	2014
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.
Starting Page	15
Ending Page	18
File Size	886454
Page Count	4
File Format	PDF
e-ISBN	9781479942749
DOI	10.1109/ICDMW.2014.166
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Image color analysis Vegetation Boosting remote sensing spatial ensemble spatial heterogeneity Decision trees raster classification class ambiguity Remote sensing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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