Land cover classification with multi-source data using evidential reasoning approach

Abstract

Land cover classification is the core of converting satellite imagery to available geographic data. However, spectral signatures do not always provide enough information in classification decisions. Thus, the application of multi-source data becomes necessary. This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery, altitude and slope data. Results show that multi-source data contribute to the classification accuracy achieved by the ER method, whereas play a negative role to that derived by maximum likelihood classifier (MLC). In comparison to the results derived based on TM imagery alone, the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible. The ER method is regarded as a better approach for multi-source image classification. In addition, the method produces not only an accurate classification result, but also the uncertainty which presents the inherent difficulty in classification decisions. The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.