ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data

By Lucian Dragut, Dirk Tiede, Shaun R. Levick. International Journal of Geographical Information Science, 2010

849 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/88cc264a41188e850102ebe0e1ad1499f834f5d4

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Citations in the corpus

Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis (2011, neutral): “[28] have developed an estimation-of-scale-”
Developments in Landsat Land Cover Classification Methods: A Review (2017, methodology): “Although different formal approaches, such as using an estimation of scale parameter (ESP) tool [110] and a segmentation index [108], were developed to address the challenges of establishing the optimal segmentation scale, using trial-and-error remains the most common approach of selecting the optim”
Object-oriented mapping of landslides using Random Forests (2011, background): “Proposed statistical optimizationmethods (e.g. Drăguţ et al., 2010) may allow an objectification of the choice of the scale parameter if the targeted objects or processes exhibit a single operational scale.”
A Survey on Object Detection in Optical Remote Sensing Images (2016, neutral): “…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., 2011; Ming et al.,…”
Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis (2018, methodology): “To avoid time-consuming trial-and-error and subjective selection of the SP (Drǎguţ et al., 2010), we used an automated tool for assisting the segmentation procedure, namely Estimation of Scale Parameter 2 (ESP2) (Drăguţ et al.”
Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features (2016, background): “Assuming there is a certain amount of spectral contrast between the object and background, the object boundaries will be preserved in segmentation at a number of higher levels, where the SD of this object remains the same [21].”