Extraction and change detection of urban impervious surface using multi-temporal remotely sensed data

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.