Land Use Classification of TM Imagery in Hilly Areas: Integration of Image Processing and Expert Knowledge

Improvement of the classification accuracy is one of the major concerns in the field of remote sensing application research in recent years. Previous research shows that the accuracy of the conventional classification methods based only on the original spectral information were usually unsatisfied and need to be refined by manual edit. This present paper describes a method of combining the image processing, ancillary data (such as digital elevation model) and expert knowledge (especially the knowledge of local professionals) to improve the efficiency and accuracy of the satellite image classification in hilly land. Firstly, the Landsat TM data were geo-referenced. Secondly, the individual bands of the image were intensitynormalized and the normalized difference vegetation index (NDVI) image was also generated. Thirdly, a set of sample pixels (collected from field survey) were utilized to discover their corresponding DN (digital number) ranges in the NDVI image, and to explore the relationships between land use type and its corresponding spectral features . Then, using the knowledge discovered from previous steps as well as knowledge from local professionals, with the support of GIS technology and the ancillary data, a set of conditional statements were applied to perform the TM imagery classification. The results showed that the integration of image processing and spatial analysis functions in GIS improved the overall classification result if compared with the conventional methods.