Unsupervised image segmentation evaluation and refinement using a multi-scale approach |
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| Authors: | Brian Johnson Zhixiao Xie |
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| Institution: | Department of Geosciences, Florida Atlantic University, 777 Glades Rd., Boca Raton, FL 33431, United States |
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| Abstract: | 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 under- and over-segmented regions using local statistics can improve global segmentation results. |
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| Keywords: | Object-based image analysis Multi-scale segmentation Image segmentation evaluation Under-segmentation Over-segmentation |
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