A contextual classification method for recognizing land use patterns in high resolution remotely sensed data

摘要

This paper describes a CONtextual ANalysis procedure (CONAN) which is designed to recognize land use patterns in high resolution remotely sensed data by analysis of the local frequency distribution of scene components (i.e. ground cover type classes). The procedure was tested with randomly generated synthetic data developed to simulate the frequency distribution of cover type components for four land use classes. It was found that the accuracy in discriminating between the four test classes depends upon the size of the pixel neighborhood used to compute the component frequency distribution.