MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies supervised and unsupervised learning pdf suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches.
Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications.
Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved. 2014 International Society for Photogrammetry and Remote Sensing, Inc. 2016, machine learning is at its peak of inflated expectations. When used interactively, these can be presented to the user for labeling. No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Here, it has learned to distinguish black and white circles. This is typically tackled in a supervised way.