Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction

By D. Flanders, Mryka Hall-Beyer, Joan Pereverzoff. 2003

317 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/c20382a8eb6670017681bc64296fd641b6f36ec2

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In Ecology

In Environmental Engineering

In Media Technology

Citations in the corpus

Developments in Landsat Land Cover Classification Methods: A Review (2017, methodology): “For example, Flanders, Hall-Beyer and Pereverzoff [82] used Landsat ETM+ to evaluate the performance of eCognition by comparing the accuracy of OBIA with pixel-based classification on different forest types.”
Object based image analysis for remote sensing (2010, methodology): “…generally agreed (Blaschke et al., 2000; Blaschke and Strobl, 2001; Schiewe, 2002; Hay et al., 2003; Burnett and Blaschke, 2003; Koch et al., 2003; Flanders et al., 2003; Benz et al., 2004; Blaschke et al., 2004; Zhang et al., 2005a; Liu et al., 2006; Navulur, 2007; Lang, 2008; Hay and Castilla,…”
Forest change detection by statistical object-based method (2006, background): “Operational systems for monitoring and updating forest maps are thus needed for many applications such as forest management, carbon budgeting and habitat monitoring (de Wasseige & Defourny, 2004; Foody, 2003; Sader et al., 2001).”
A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “, a larger value of the scale allows a higher internal heterogeneity, which increases the number of pixels per object and vice versa (Baatz and Schäpe, 2000; Benz et al., 2004; Flanders et al., 2003).”
Object-based contextual image classification built on image segmentation (2003, methodology): “While this was claimed earlier as hypotheses [14, 7] it could recently empirically verified by [16].”
Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms (2020, methodology): “After the segmentation, GEOBIA generally combines spectral and spatial information with the texture and context information of the image to perform the final object classification [13].”
Optimizing landscape selection for estimating relative effects of landscape variables on ecological responses (2013, background): “…involving segmentation (Definiens Professional 5.0; Definiens 2006) of higher resolution SPOT panchromatic imagery (10 9 10 m pixels) into individual objects, based on maximizing between-object variance and minimizing within-object variance (Flanders et al. 2003; Dingle Robertson and King 2011).”
Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability (2008, background): “For a number of years, classification trees (Hansen et al. 1996) are being developed in an attempt to add spatial information to pixel-based classifiers (Flanders et al. 2003).”
Automated classification of landform elements using object-based image analysis (2006, methodology): “Early models of object-based image classification faced obstacles in fusing information from multilevel analysis, validating classifications, reconciling conflicting results, attaining reasonable efficiency in processing (time and effort), and automating the analysis (Flanders et al., 2003).”