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.

在过去的二十年中，遥感传感器的进步导致产生了非常高的空间分辨率多光谱图像。为了适应这种快速发展并处理这些数据，出现了基于对象的分析。这种分析的关键部分是图像分割。最佳分割参数值的选择将生成定性分割输出，并直接影响特征提取和随后的整体分类精度。尽管在文献中已经开发并提出了几种图像分割方法，但是每种方法都有其优点和缺点。本文介绍了图像分割方法的概念特征，特别关注语义分割。此外，通过对近期图像分割案例研究的全面审查，进行了荟萃分析。它包括有关所应用的分割算法，所使用的软件和数据源等方面的统计数据和定量数据。由于没有奇迹般的分割算法，因此统计结果仅描绘了近期趋势。最后，讨论了一些有趣的主题，包括当前问题的识别，非传统数据的图像分割以及未来研究的热点。