Quality assessment of segmentation results devoted to object-based classification

Object-based image analysis often uses image segmentation as a preliminary step to enhance classification. Object-based classification therefore relies on the quality of the segmentation output. This study evaluates the relevance of quantitative segmentation quality indices to object-based classification. Image segmentation is expected to improve the thematic accuracy of classification but the counterpart is an increased chance of boundary artefacts. Goodness indices were used to assess the former while discrepancy indices evaluated boundary quality. Inter-class Bhattacharyya distance was used to test the relevance of the goodness indices. The results showed that the use of global goodness indices, which did not require a priori information about the study area, was relevant in the case of object-based classification. In this context, the goodness index based on intra-class standard deviation was more useful than the one based on mean object size. On the other hand, it was shown that object size improved class discrimination but this could deteriorate the boundary quality. The use of complementary discrepancy indices is therefore required in the case of frequent under-segmentation.