Nowadays

investigations on land use / land cover change detections constitute a main objective for the global research.As a part of rapid development in technology

remote sensing has become an important tool to acquire the information of the land use/cover.Therefore

how best the extraction of timely and accurate information from these remotely sensed images is an impending problem.Recently

the knowledge-based interpretation of these images has become an effective and efficient approach to realize the automatic interpretation

which can integrate the spectral and other associated information based on experts’ knowledge and experience to improve the accuracy.However

it is a bottleneck problem to obtain the knowledge for its wide application.A case study on the land use/cover classification of Jiangning study area in Jiangsu Province is discussed in the present article.At first

the data are preprocessed

then the relevant sixteen variables including geographical coordinate

grey value of four bands

textural statistics

DEM

slope and aspect are selected and extracted.The defined training sample areas are picked up by stratified random sampling techniques based on geographical coordinates.Thirdly

classification rules are discovered from these samples through Classification and Regression Tree（CART） Analysis

which integrates spectral

textural and the spatial distribution characters.Fourthly

the interpretation was performed by a judgment based on these rules.Finally

the traditional supervised as well as logic channel classifications are also performed to check the classification accuracies.The results have suggested that the accuracy of classification based on the CART is higher than others’

which can obtain a lot of reasonable rules most quickly and effectively.So

it was felt that it is a good way to promote the wide application of knowledge-based interpretation of remote sensing images.