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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change

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A change detection model based on neighborhood correlation image analysis and decision tree classification

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Title
A change detection model based on neighborhood correlation image analysis and decision tree classification
Author
Im, JunghoJensen, JR
Keywords
Im, J. , Jensen, J.R.
Issue Date
2005-11
Publisher
ELSEVIER SCIENCE INC
Citation
REMOTE SENSING OF ENVIRONMENT, v.99, no.3, pp.326 - 340
Abstract
This study introduces a change detection model based on Neighborhood Correlation Image (NCI) logic. It is based on the fact that the same geographic area (e.g., a 3 × 3 pixel window) on two dates of imagery will tend to be highly correlated if little change has occurred, and uncorrelated when change occurs. Computing the piecewise correlation between two data sets provides valuable information regarding the location and numeric change value derived using contextual information within the specified neighborhood. Various neighborhood configurations (i.e., multi-level NCIs) were explored in the study using high spatial resolution multispectral imagery: smaller neighborhood sizes provided some detailed change information (such as a new patios added to an existing building) at the cost of introducing some noise (such as changes in shadows). Larger neighborhood sizes were useful for removing this noise but introduced some inaccurate change information (such as removing some linear feature changes). When combined with image classification using a machine learning decision tree (C5.0), classifications based on multi-level NCIs yielded superior results (e.g., using a 3-pixel circular radius neighborhood had a Kappa of 0.94), compared to the classification that did not incorporate NCIs (Kappa = 0.86).
URI
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DOI
10.1016/j.rse.2005.09.008
ISSN
0034-4257
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UEE_Journal Papers
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