This paper presents a"global-local"remote sensing information extract model

which extracts and integrates the spatial and spectral characteristics within the images’ local area. The model can optimize the accuracy of extraction on the pixels with spectral fuzzy. The model can be briefly described into two steps: "global"prior classifier and"local"posteriori classifier. The"global"priori classifier will only classify pixels which are above certain accuracy thresholds

and the "local"posteriori classifier will further explore the information of the already classified pixels from the partial-classified results. The local information will be used to classify the unclassified pixels at the global scale. When extraction of Impervious Surface Area（ ISA） experiment

we used Support Vector Machine（ SVM） as a priori classifier

which is controlled by an accuracy threshold to output the partial-classified results. We also used an Adjust Minimize Distance Classifier（ AMDC） as the posteriori classifier

which integrates the spatial information within local area around the unclassified pixels to classify the pixels with high degree of difficulty of classification by only spectral information. The experiment on the Landsat TM5 image and corresponding National Land Cover Database（ NLCD） pro-d ucts as reference indicates that"global-local"model enhanced the accuracy from 80. 31%

which is provided by SVM model

to 82. 73%. Meanwhile

the accuracy of posteriori classifier was enhanced from 54. 27%（ SVM） to 59. 94%. The results proved that combine with spatial and spectral information is an effective way for ISA extraction and the"globle-local"model can improve the accuracy of ISA extraction and can obtain more spatially explicit results.