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Titre du document / Document title

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

Auteur(s) / Author(s)

IM J. (1) ; JENSEN J. R. (1) ; TULLIS J. A. (2) ;

Affiliation(s) du ou des auteurs / Author(s) Affiliation(s)

(1) Center for GIS and Remote Sensing, Department of Geography, University of South Carolina, Columbia, SC 29208, ETATS-UNIS
(2) Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, ETATS-UNIS

Résumé / 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 (I) 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%).

Revue / Journal Title

International journal of remote sensing    ISSN  0143-1161   CODEN IJSEDK 

Source / Source

2008, vol. 29, no1-2, pp. 399-423 [25 page(s) (article)] (1 p.1/4)

Langue / Language

Anglais

Editeur / Publisher

Taylor & Francis, Abingdon, ROYAUME-UNI  (1980) (Revue)

Mots-clés anglais / English Keywords

algorithms

;

image processing

;

artificial intelligence

;

methodology

;

Pixel

;

classification

;

brightness

;

techniques

;

segmentation

;

imagery

;

image analysis

;

correlation

;

remote sensing

;

Mots-clés français / French Keywords

Prétraitement

;

Détection changement

;

Algorithme apprentissage

;

Arbre décision

;

Plus proche voisin

;

Algorithme

;

Traitement image

;

Intelligence artificielle

;

Méthodologie

;

Pixel

;

Classification

;

Brillance

;

Technique

;

Segmentation

;

Imagerie

;

Analyse image

;

Corrélation

;

Télédétection

;

Mots-clés espagnols / Spanish Keywords

Algoritmo

;

Metodología

;

Pixel

;

Clasificación

;

Imaginería

;

Análisis imagen

;

Correlación

;

Detección a distancia

;

Localisation / Location

INIST-CNRS, Cote INIST : 19437, 35400017395287.0240

Nº notice refdoc (ud4) : 19984913



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