Unsupervised image segmentation evaluation and refinement using a multi-scale approach

作者：Johnson Brian^*; Xie Zhixiao

来源：ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(4): 473-483.

DOI：10.1016/j.isprsjprs.2011.02.006

• Object-based image analysis
• Multi-scale segmentation
• Image segmentation evaluation
• Under-segmentation
• Over-segmentation

In this study, a multi-scale approach is used to improve the segmentation of a high spatial resolution (30 cm) color infrared image of a residential area. First, a series of 25 image segmentations are performed in Definiens Professional 5 using different scale parameters. The optimal image segmentation is identified using an unsupervised evaluation method of segmentation quality that takes into account global intra-segment and inter-segment heterogeneity measures (weighted variance and Moran's I, respectively). Once the optimal segmentation is determined, under-segmented and over-segmented regions in this segmentation are identified using local heterogeneity measures (variance and Local Moran's I). The under- and over-segmented regions are refined by (1) further segmenting under-segmented regions at finer scales, and (2) merging over-segmented regions with spectrally similar neighbors. This process leads to the creation of several segmentations consisting of segments generated at three different segmentation scales. Comparison of single- and multi-scale segmentations shows that identifying and refining underand over-segmented regions using local statistics can improve global segmentation results. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

• 出版日期2011-7