Automated parameterisation for multi-scale image segmentation on multiple layers
By Lucian Dragut, Ovidiu Csillik, Clemens Eisank, Dirk Tiede. Isprs Journal of Photogrammetry and Remote Sensing, 2014
631 citations recorded by the source corpus.
We introduce a new automated approach to parameterising multi-scale image segmentation of multiple layers, and we implemented it as a generic tool for the eCognition® software. This approach relies on the potential of the local variance (LV) to detect scale transitions in geospatial data. The tool detects the number of layers added to a project and segments them iteratively with a multiresolution segmentation algorithm in a bottom-up approach, where the scale factor in the segmentation, namely, the scale parameter (SP), increases with a constant increment. The average LV value of the objects in all of the layers is computed and serves as a condition for stopping the iterations: when a scale level records an LV value that is equal to or lower than the previous value, the iteration ends, and the objects segmented in the previous level are retained. Three orders of magnitude of SP lags produce a corresponding number of scale levels. Tests on very high resolution imagery provided satisfactory results for generic applicability. The tool has a significant potential for enabling objectivity and automation of GEOBIA analysis.
Subfields: Artificial Intelligence, Media Technology, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/060f5fd8101b51ae1e5a961b25c8e07fc1a33b47
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Citations in the corpus
- A Survey on Object Detection in Optical Remote Sensing Images (2016, neutral): “…techniques have been developed and applied in RSI analysis (Ardila et al., 2012; Baatz and Schäpe, 2000; Benz et al., 2004; Blaschke et al., 2004; Drăguţ et al., 2014; Drăguţ et al., 2010; Esch et al., 2008; Gao et al., 2011; Hay et al., 2005; Jungho et al., 2014; Kim et al., 2008; Kim et al.,…”
- Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features (2016, methodology): “To take full advantage of the multispectral information, an improved version of ESP is adopted to acquire the optimal scale [22].”