Cohen, Y., Institute of Agricultural Engineering, Agricultural Research Organization (A.R.O), Volcani Center Bet Dagan, Israel
Shoshany, M., Department of Transportation and Geo-Information Engineering, Faculty of Civil and Environmental Engineering, Technion, Haifa, Israel

The use of KBSs based on evidential reasoning, for land-cover mapping based on remotely-sensed images is spreading widely. In recent years, KBS utilizing Dempster-Shafer Theory of Evidence (D-S ToE) were found most successful in wide range of remote sensing applications. One important feature of the D-S ToE is that it provides a measure for the evidential support (belief) accumulated for each object class at each pixel. Although cumulative belief values (CBVs) play a major role in classification decisions, their analysis has received little attention in the literature. The objective of the present study was to investigate and to characterize the added value of the KBS by the analysis of the CBV. For that purpose we applied a KBS based on D-S ToE to crop recognition in a wide heterogeneous region and compared its results with those of the application of ISODATA classification. We investigated the relationships between the distribution of the CBV of the different classes and their corresponding classification accuracy/reliability. The CBVs were found to be good indicators of levels of classification complexity in both the pixel and the class scales. In addition to that, levels of two class properties could be analyzed according to the distribution of CBVs of each class: heterogeneity and uniqueness. Moderate and high correlations (r2=0.69 and r2=0.94) were found between these two properties and classification efficiency of an unsupervised classification (US). Lower correlations were found between these properties and the KBS classification efficiency (r2=0.59 and r2=0.75). Moreover, US classification was highly affected by heterogeneity and uniqueness as referred from much higher slope coefficients (5 times higher): US classification efficiency decreased with increasing heterogeneity levels and decreasing uniqueness levels. These findings are suggesting that in contrast to the US classification the KBS facilitates identification of a class with little affect of its internal variability (heterogeneity) and its similarity with other classes (lack of uniqueness).