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Object-based land cover change detection for cross-sensor images
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
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2013 (English)In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 34, no 19, 6723-6737 p.Article in journal (Refereed) Published
Abstract [en]

Accurate and timely land cover change detection at regional and global scales is necessary for both natural resource management and global environmental change studies. Satellite remote sensing has been widely used in land cover change detection over the past three decades. The variety of satellites which have been launched for Earth Observation (EO) and the large volume of remotely sensed data archives acquired by different sensors provide a unique opportunity for land cover change detection. This article introduces an object-based land cover change detection approach for cross-sensor images. First, two images acquired by different sensors were stacked together and principal component analysis (PCA) was applied to the stacked data. Second, based on the Eigen values of the PCA transformation, six principal bands were selected for further image segmentation. Finally, a land cover change detection classification scheme was designed based on the land cover change patterns in the study area. An image-object classification was implemented to generate a land cover change map. The experiment was carried out using images acquired by Landsat 5 TM and IRS-P6 LISS3 over Daqing, China. The overall accuracy and kappa coefficient of the change map were 83.42% and 0.82, respectively. The results indicate that this is a promising approach to produce land cover change maps using cross-sensor images.

Place, publisher, year, edition, pages
Taylor & Francis, 2013. Vol. 34, no 19, 6723-6737 p.
Keyword [en]
Classification scheme, Earth observations, Global environmental change, Kappa coefficient, Natural resource management, Overall accuracies, Remotely sensed data, Satellite remote sensing
National Category
Remote Sensing
URN: urn:nbn:se:kth:diva-125533DOI: 10.1080/01431161.2013.805282ISI: 000321609300016ScopusID: 2-s2.0-84880143624OAI: diva2:640213

QC 20150623

Available from: 2013-08-13 Created: 2013-08-09 Last updated: 2015-06-23Bibliographically approved

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Qin, YuchuBan, Yifang
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