Remote sensing image segmentation advances: A meta-analysis

The advances in remote sensing sensors during the last two decades have led to the production of very high spatial resolution multispectral images. In order to adapt to this rapid development and handle these data, object-based analysis has emerged. A critical part of such an analysis is image segmentation. The selection of optimal segmentation parameters' values generates a qualitative segmentation output and has a direct impact on feature extraction and subsequent overall classification accuracy. Even though several image segmentation methods have been developed and suggested in the literature, each of them has advantages and disadvantages. This article presents the conceptual characteristics of image segmentation methods with a special focus on semantic segmentation. In addition, a meta-analysis was conducted through a comprehensive review of recent image segmentation case studies. It includes statistics and quantitative data regarding the applied segmentation algorithm, the software utilized and the data source among others. Since there is no miraculous segmentation algorithm, the statistical results depict only the recent trend. Finally, a few interesting subjects are addressed, including identification of current problems, image segmentation on non-traditional data and hot topics for future research.