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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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Object-based change detection using correlation image analysis and image segmentation

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Title
Object-based change detection using correlation image analysis and image segmentation
Author
Im, JunghoJensen, J. R.Tullis, J. A.
Keywords
REMOTE-SENSING DATA; CLASSIFICATION; MODEL
Issue Date
2008-01
Publisher
TAYLOR & FRANCIS LTD
Citation
INTERNATIONAL JOURNAL OF REMOTE SENSING, v.29, no.2, pp.399 - 423
Abstract
This study introduces change detection based on object/neighbourhood correlation image analysis and image segmentation techniques. The correlation image analysis is based on the fact that pairs of brightness values from the same geographic area (e.g. an object) between bi-temporal image datasets tend to be highly correlated when little change occurres, and uncorrelated when change occurs. Five different change detection methods were investigated to determine how new contextual features could improve change classification results, and if an object-based approach could improve change classification when compared with per-pixel analysis. The five methods examined include (1) object-based change classification incorporating object correlation images (OCIs), (2) object-based change classification incorporating neighbourhood correlation images (NCIs), (3) object-based change classification without contextual features, (4) per-pixel change classification incorporating NCIs, and (5) traditional per-pixel change classification using only bi-temporal image data. Two different classification algorithms (i.e. a machine-learning decision tree and nearest-neighbour) were also investigated. Comparison between the OCI and the NCI variables was evaluated. Object-based change classifications incorporating the OCIs or the NCIs produced more accurate change detection classes (Kappa approximated 90%) than other change detection results (Kappa ranged from 80 to 85%).
URI
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DOI
10.1080/01431160601075582
ISSN
0143-1161
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UEE_Journal Papers
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