Applying evidential reasoning methods to agricultural land cover classification

Land cover classification remains an important and complex problem when land observational satellites are involved. Conventional methods of image classification have been shown to provide adequate results; however, when complex surface arrangements are encountered the reliability of conventional approaches can be called into question. This situation is particularly apparent when land cover classes cannot be well discriminated, or when land cover categories are sufficiently broad in their definition that a single spectral response pattern cannot adequately capture the inherent within variation associated with that class. In this paper the problem of agricultural land mapping was examined and the potential for applying soft-classification procedures based on the Dempster-Shafer Theory of Evidence was demonstrated. Results of this study show that applying Dempster-Shafer Theory in image classification can yield thematic maps with accuracies that can support their operational use. In addition, through the application of this technique, a framework can be developed to support and guide the use of subjective judgement during the classification process and permit greater flexibility in the formulation of informational classes.