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Biblioteca Object-based land cover change detection for cross-sensor images

Object-based land cover change detection for cross-sensor images

Object-based land cover change detection for cross-sensor images

Resource information

Date of publication
Diciembre 2013
Resource Language
ISBN / Resource ID
AGRIS:US201400169051
Pages
6723-6737

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.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Qin, Y.
Niu, Z.
Chen, F.
Li, B.
Ban, Y.

Publisher(s)
Data Provider
Geographical focus