It is a broadly established belief that the segmentation result significantly affects subsequent image classification accuracy. However, the actual correlation between the two has never been evaluated. Such an evaluation would be of considerable importance for any attempts to automate the object-based classification process, as it would reduce the amount of user intervention required to fine-tune the segmentation parameters. We conducted an assessment of segmentation and classification by analyzing 100 different segmentation parameter combinations, 3 classifiers, 5 land cover classes, 20 segmentation evaluation metrics, and 7 classification accuracy measures. The reliability definition of segmentation evaluation metrics as indicators of land cover classification accuracy was based on the linear correlation between the two. All unsupervised metrics that are not based on number of segments have a very strong correlation with all classification measures and are therefore reliable as indicators of land cover classification accuracy. On the other hand, correlation at supervised metrics is dependent on so many factors that it cannot be trusted as a reliable classification quality indicator. Algorithms for land cover classification studied in this paper are widely used; therefore, presented results are applicable to a wider area.
Slovenia is one of the most forested countries in Europe. Its forest management authorities need information about the forest extent and state, as their responsibility lies in forest observation and preservation. Together with appropriate geographic information system mapping methods the remotely sensed data represent essential tool for an effective and sustainable forest management. Despite the large data availability, suitable mapping methods still present big challenge in terms of their speed which is often affected by the huge amount of data. The speed of the classification method could be maximised, if each of the steps in object-based classification was automated. However, automation is hard to achieve, since segmentation requires choosing optimum parameter values for optimal classification results. <p> </p>This paper focuses on the analysis of segmentation and classification performance and their correlation in a range of segmentation parameter values applied in the segmentation step. In order to find out which spatial resolution is still suitable for forest classification, forest classification accuracies obtained by using four images with different spatial resolutions were compared.<p> </p> Results of this study indicate that all high or very high spatial resolutions are suitable for optimal forest segmentation and classification, as long as appropriate scale and merge parameters combinations are used in the object-based classification. If computation interval includes all segmentation parameter combinations, all segmentation-classification correlations are spatial resolution independent and are generally high. If computation interval includes over- or optimal-segmentation parameter combinations, most segmentation-classification correlations are spatial resolution dependent.