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Proceedings Paper

Extraction and change detection of urban impervious surface using multitemporal remotely sensed data
Author(s): Youjing Zhang; Xuemei Ma; Liang Chen
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Paper Abstract

An approach for extraction and detection urban impervious surface was proposed in this paper, in which a decision tree classifier based on data learning algorithm was employed using Landsat TM/ETM data in 1988, 1994 and 2002 at same season. The feature subset was constructed with spectral, spatial and change information related to the characters of urban impervious surface. The samples from the higher spatial resolution image were dealt with CART algorithm. The extraction and change detection were performance with the decision tree classifier, and change information of 1994-2002 and 1988-1992 was verified by overlay analysis from GIS for the reasonability. The result of extraction impervious surface for six urban types was shown that the overall accuracy was 88.1% compared with 69.3% of MLC (maximum-likelihood Classifier) in 2002, and the detection accuracy for the five change types was 89.1% and 91.4% between 1994 and 2002, 1988 and 1994 respectively. The research has been demonstrated that the proposed approach is of capability for the change detection and can be achieved better accuracy using medium spatial resolution remotely sensed data.

Paper Details

Date Published: 28 October 2006
PDF: 13 pages
Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64190Y (28 October 2006); doi: 10.1117/12.713005
Show Author Affiliations
Youjing Zhang, Hohai Univ. (China)
Xuemei Ma, Hohai Univ. (China)
Liang Chen, Hohai Univ. (China)


Published in SPIE Proceedings Vol. 6419:
Geoinformatics 2006: Remotely Sensed Data and Information
Liangpei Zhang; Xiaoling Chen, Editor(s)

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