Parameter selection for region‐growing image segmentation algorithms using spatial autocorrelation

By G. Espindola, G. Câmara, Ilka Afonso Reis, L. Bins, A. Monteiro. 2006

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Unsupervised image segmentation evaluation and refinement using a multi-scale approach (2011, unknown): “We chose to use Moran's I because it is a reliable indicator of statistical separation between spatial objects (Fotheringham et al., 2000), and was found to be a good indicator of segmentation quality in previous segmentation evaluation studies (Espindola et al., 2006; Kim et al., 2008).”
Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis (2011, background): “user-defined segmentation parameters [25], [26].”
Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine (2017, background): “GEE provides APIs related to image segmentation such as region grow [84].”
Impact of sudden oak death on tree mortality in the Big Sur ecoregion of California (2008, methodology): “0 ha), using a region-growing segmentation algorithm (Espindola et al. 2006).”