Automated parameterisation for multi-scale image segmentation on multiple layers

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 d...

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Published in	ISPRS journal of photogrammetry and remote sensing Vol. 88; no. 100; pp. 119 - 127
Main Authors	Drăguţ, L., Csillik, O., Eisank, C., Tiede, D.
Format	Journal Article
Language	English
Published	Amsterdam Elsevier B.V 01.02.2014 Elsevier
Subjects	algorithms Animal, plant and microbial ecology Applied geophysics Automated Automation Biological and medical sciences Computer programs computer software Earth sciences Earth, ocean, space Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques GEOBIA Image segmentation Imagery Internal geophysics Iterative methods MRS Object Representation Segmentation Segments Software spatial data Teledetection and vegetation maps variance GEOBIA Automation Object Representation Imagery MRS algorithms projects software scale factor Image Bottom up control automation imagery segmentation high resolution
Online Access	Get full text
ISSN	0924-2716 1872-8235
DOI	10.1016/j.isprsjprs.2013.11.018

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Abstract	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.
AbstractList	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. 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 registered 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. 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.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.
Author	Eisank, C. Csillik, O. Tiede, D. Drăguţ, L.
Author_xml	– sequence: 1 givenname: L. surname: Drăguţ fullname: Drăguţ, L. email: lucian.dragut@cbg.uvt.ro organization: Department of Geography, West University of Timişoara, V. Pârvan Blv. 4, 300223 Timişoara, Romania – sequence: 2 givenname: O. surname: Csillik fullname: Csillik, O. email: cskovi@yahoo.com organization: Department of Geography, West University of Timişoara, V. Pârvan Blv. 4, 300223 Timişoara, Romania – sequence: 3 givenname: C. surname: Eisank fullname: Eisank, C. email: clemens.eisank@sbg.ac.at organization: Interfaculty Department of Geoinformatics – Z_GIS, University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria – sequence: 4 givenname: D. surname: Tiede fullname: Tiede, D. email: dirk.tiede@sbg.ac.at organization: Interfaculty Department of Geoinformatics – Z_GIS, University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28212883$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/24748723$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1109/LGRS.2010.2101045 10.1016/0034-4257(87)90015-0 10.1080/01431161.2012.692829 10.1080/01431161.2012.747018 10.1109/TGRS.2011.2151866 10.14358/PERS.77.9.933 10.1080/13658810903174803 10.14358/PERS.76.2.193 10.1016/j.rse.2012.08.010 10.1016/j.geomorph.2009.10.004 10.1016/j.isprsjprs.2009.06.004 10.1080/01431161003777189 10.1080/01431160500057764 10.1016/j.rse.2011.11.020 10.1016/j.geomorph.2012.05.024 10.1080/01431160600617194 10.1016/j.cviu.2007.08.003 10.5194/nhess-11-2715-2011 10.1016/j.jag.2012.03.015 10.1016/j.jag.2011.06.005 10.1016/j.isprsjprs.2013.05.003 10.1016/j.isprsjprs.2003.10.002 10.3390/rs5020558 10.1016/j.isprsjprs.2011.09.012 10.1109/LGRS.2008.919622 10.1080/01431161003745608 10.2307/2389612 10.1016/j.rse.2011.05.013 10.1016/j.jag.2011.06.008 10.1080/01431169208904109 10.3390/rs4051310 10.1016/j.geomorph.2011.12.001 10.14358/PERS.76.3.289 10.3390/rs5094163 10.1016/j.geomorph.2011.07.003 10.1016/j.compmedimag.2005.12.001 10.1016/j.isprsjprs.2011.02.006
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References	Eisank, Drăguţ, Blaschke (b0065) 2011 Hölbling, Füreder, Antolini, Cigna, Casagli, Lang (b0100) 2012; 4 Lahousse, Chang, Lin (b0130) 2011; 11 Hellesen, Matikainen (b0095) 2013; 5 Zhan, Molenaar, Tempfli, Shi (b0220) 2005; 26 Kim, Madden, Warner (b0115) 2008 Duro, Franklin, Dubé (b0060) 2012; 118 Drăguţ, Eisank (b0050) 2012; 141–142 Whiteside, Boggs, Maier (b0200) 2011; 13 Espindola, Camara, Reis, Bins, Monteiro (b0075) 2006; 27 Laben, Brower (b0230) 2000 O’Neill (b0155) 1986 Shruthi, Kerle, Jetten (b0160) 2011; 134 Beger, Gedrange, Hecht, Neubert (b0025) 2011 Woodcock, Harward (b0210) 1992; 13 Sun, Sun, Chen, Gong (b0170) 2012; 33 Esch, Thiel, Bock, Roth, Dech (b0070) 2008; 5 Van Den Eeckhaut, Kerle, Poesen, Hervás (b0190) 2012; 173–174 Ardila, Bijker, Tolpekin, Stein (b0010) 2012; 15 Zhang, Fritts, Goldman (b0225) 2008; 110 Aguilar, Saldaña, Aguilar (b0005) 2012; 34 Kim, Warner, Madden, Atkinson (b0120) 2011; 32 Martha, Kerle, van Westen, Jetten, Kumar (b0145) 2011; 49 Blaschke (b0035) 2010; 65 Hagenlocher, Lang, Tiede (b0085) 2012; 126 Johnson, Xie (b0110) 2011; 66 Verbeeck, Hermy, Van Orshoven (b0195) 2012; 18 Martha, Kerle, Jetten, van Westen, Kumar (b0140) 2010; 116 Drăguţ, Tiede, Levick (b0055) 2010; 24 Tiede, Lang, Albrecht, Hölbling (b0175) 2010; 76 Baatz, Schäpe (b0020) 2000 Hay, Marceau, Bouchard (b0090) 2002; 34 Benz, Hofmann, Willhauck, Lingenfelder, Heynen (b0030) 2004; 58 Lu, Stumpf, Kerle, Casagli (b0135) 2011; 8 Gao, Mas, Kerle, Navarrete Pacheco (b0080) 2011; 32 Neubert, Herold, Meinel (b0150) 2008 Jakubowski, Li, Guo, Kelly (b0105) 2013; 5 Udupa, Leblanc, Zhuge, Imielinska, Schmidt, Currie, Hirsch, Woodburn (b0185) 2006; 30 Tiede, Lang, Füreder, Hölbling, Hoffmann, Zeil (b0180) 2011; 77 Woodcock, Strahler (b0215) 1987; 21 Arvor, Durieux, Andrés, Laporte (b0015) 2013; 82 Wiens (b0205) 1989; 3 Castilla, Hay (b0040) 2008 Stumpf, Kerle (b0165) 2011; 115 Clinton, Holt, Scarborough, Yan, Gong (b0045) 2010; 76 Ardila (10.1016/j.isprsjprs.2013.11.018_b0010) 2012; 15 Castilla (10.1016/j.isprsjprs.2013.11.018_b0040) 2008 Kim (10.1016/j.isprsjprs.2013.11.018_b0115) 2008 Baatz (10.1016/j.isprsjprs.2013.11.018_b0020) 2000 Drăguţ (10.1016/j.isprsjprs.2013.11.018_b0055) 2010; 24 Wiens (10.1016/j.isprsjprs.2013.11.018_b0205) 1989; 3 Hay (10.1016/j.isprsjprs.2013.11.018_b0090) 2002; 34 Woodcock (10.1016/j.isprsjprs.2013.11.018_b0210) 1992; 13 Lu (10.1016/j.isprsjprs.2013.11.018_b0135) 2011; 8 Woodcock (10.1016/j.isprsjprs.2013.11.018_b0215) 1987; 21 Aguilar (10.1016/j.isprsjprs.2013.11.018_b0005) 2012; 34 Blaschke (10.1016/j.isprsjprs.2013.11.018_b0035) 2010; 65 Stumpf (10.1016/j.isprsjprs.2013.11.018_b0165) 2011; 115 Martha (10.1016/j.isprsjprs.2013.11.018_b0145) 2011; 49 Arvor (10.1016/j.isprsjprs.2013.11.018_b0015) 2013; 82 Jakubowski (10.1016/j.isprsjprs.2013.11.018_b0105) 2013; 5 Udupa (10.1016/j.isprsjprs.2013.11.018_b0185) 2006; 30 Whiteside (10.1016/j.isprsjprs.2013.11.018_b0200) 2011; 13 Benz (10.1016/j.isprsjprs.2013.11.018_b0030) 2004; 58 Laben (10.1016/j.isprsjprs.2013.11.018_b0230) 2000 Lahousse (10.1016/j.isprsjprs.2013.11.018_b0130) 2011; 11 Shruthi (10.1016/j.isprsjprs.2013.11.018_b0160) 2011; 134 Tiede (10.1016/j.isprsjprs.2013.11.018_b0175) 2010; 76 Beger (10.1016/j.isprsjprs.2013.11.018_b0025) 2011 Espindola (10.1016/j.isprsjprs.2013.11.018_b0075) 2006; 27 Hellesen (10.1016/j.isprsjprs.2013.11.018_b0095) 2013; 5 Zhang (10.1016/j.isprsjprs.2013.11.018_b0225) 2008; 110 Johnson (10.1016/j.isprsjprs.2013.11.018_b0110) 2011; 66 Drăguţ (10.1016/j.isprsjprs.2013.11.018_b0050) 2012; 141–142 Duro (10.1016/j.isprsjprs.2013.11.018_b0060) 2012; 118 Gao (10.1016/j.isprsjprs.2013.11.018_b0080) 2011; 32 Hagenlocher (10.1016/j.isprsjprs.2013.11.018_b0085) 2012; 126 Martha (10.1016/j.isprsjprs.2013.11.018_b0140) 2010; 116 Neubert (10.1016/j.isprsjprs.2013.11.018_b0150) 2008 Van Den Eeckhaut (10.1016/j.isprsjprs.2013.11.018_b0190) 2012; 173–174 Esch (10.1016/j.isprsjprs.2013.11.018_b0070) 2008; 5 Zhan (10.1016/j.isprsjprs.2013.11.018_b0220) 2005; 26 O’Neill (10.1016/j.isprsjprs.2013.11.018_b0155) 1986 Tiede (10.1016/j.isprsjprs.2013.11.018_b0180) 2011; 77 Hölbling (10.1016/j.isprsjprs.2013.11.018_b0100) 2012; 4 Sun (10.1016/j.isprsjprs.2013.11.018_b0170) 2012; 33 Kim (10.1016/j.isprsjprs.2013.11.018_b0120) 2011; 32 Verbeeck (10.1016/j.isprsjprs.2013.11.018_b0195) 2012; 18 Clinton (10.1016/j.isprsjprs.2013.11.018_b0045) 2010; 76 Eisank (10.1016/j.isprsjprs.2013.11.018_b0065) 2011
References_xml	– volume: 27 start-page: 3035 year: 2006 end-page: 3040 ident: b0075 article-title: Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation publication-title: Int. J. Remote Sens. – volume: 30 start-page: 75 year: 2006 end-page: 87 ident: b0185 article-title: A framework for evaluating image segmentation algorithms publication-title: Comput. Med. Imaging Graph. – start-page: 12 year: 2000 end-page: 23 ident: b0020 article-title: Multiresolution segmentation-an optimization approach for high quality multi-scale image segmentation publication-title: Angew. Geogr. Info. verarbeitung – year: 2000 ident: b0230 article-title: Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening – volume: 118 start-page: 259 year: 2012 end-page: 272 ident: b0060 article-title: A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery publication-title: Remote Sens. Environ. – volume: 126 start-page: 27 year: 2012 end-page: 38 ident: b0085 article-title: Integrated assessment of the environmental impact of an IDP camp in Sudan based on very high resolution multi-temporal satellite imagery publication-title: Remote Sens. Environ. – start-page: 769 year: 2008 end-page: 784 ident: b0150 article-title: Assessing image segmentation quality-concepts, methods and applications publication-title: Object-Based Image Analysis. Spatial Concepts for Knowledge-Driven Remote Sens. Applications – start-page: 91 year: 2008 end-page: 110 ident: b0040 article-title: Image objects and geographic objects publication-title: Object-Based Image Analysis – volume: 76 start-page: 193 year: 2010 end-page: 202 ident: b0175 article-title: Object-based class modeling for cadastre-constrained delineation of Geo-objects publication-title: Photogramm. Eng. Remote Sens. – volume: 18 start-page: 428 year: 2012 end-page: 435 ident: b0195 article-title: External geo-information in the segmentation of VHR imagery improves the detection of imperviousness in urban neighborhoods publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 65 start-page: 2 year: 2010 end-page: 16 ident: b0035 article-title: Object based image analysis for remote sensing publication-title: ISPRS J. Photogramm. Remote Sen. – start-page: 291 year: 2008 end-page: 307 ident: b0115 article-title: Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery publication-title: Object-Based Image Analysis-Spatial concepts for knowledge driven remote Sensing applications – volume: 110 start-page: 260 year: 2008 end-page: 280 ident: b0225 article-title: Image segmentation evaluation: a survey of unsupervised methods publication-title: Comput. Vis. Image Underst. – volume: 5 start-page: 558 year: 2013 end-page: 583 ident: b0095 article-title: An object-based approach for mapping shrub and tree cover on grassland habitats by use of LiDAR and CIR orthoimages publication-title: Remote Sens. – year: 1986 ident: b0155 article-title: A Hierarchical Concept of Ecosystems – volume: 32 start-page: 2825 year: 2011 end-page: 2850 ident: b0120 article-title: Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects publication-title: Int. J. Remote Sens. – volume: 21 start-page: 311 year: 1987 end-page: 332 ident: b0215 article-title: The factor of scale in remote-Sens. publication-title: Remote Sens. Environ. – volume: 58 start-page: 239 year: 2004 end-page: 258 ident: b0030 article-title: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information publication-title: ISPRS J. Photogramm. Remote Sen. – volume: 32 start-page: 3747 year: 2011 end-page: 3763 ident: b0080 article-title: Optimal region growing segmentation and its effect on classification accuracy publication-title: Int. J. Remote Sens. – volume: 134 start-page: 260 year: 2011 end-page: 268 ident: b0160 article-title: Object-based gully feature extraction using high spatial resolution imagery publication-title: Geomorphology – volume: 82 start-page: 125 year: 2013 end-page: 137 ident: b0015 article-title: Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective publication-title: ISPRS J. Photogramm. Remote Sen. – volume: 76 start-page: 289 year: 2010 end-page: 299 ident: b0045 article-title: Accuracy assessment measures for object-based image segmentation goodness publication-title: Photogramm. Eng. Remote Sen. – volume: 34 start-page: 532 year: 2002 end-page: 535 ident: b0090 article-title: Modeling multi-scale landscape structure within a hierarchical scale-space framework publication-title: Int Arch. Photogramm. Remote Sens. Spatial Inf. Sci. – volume: 5 start-page: 4163 year: 2013 end-page: 4186 ident: b0105 article-title: Delineating Individual Trees from Lidar Data: A Comparison of Vector-and Raster-based Segmentation Approaches publication-title: Remote Sens. – volume: 15 start-page: 57 year: 2012 end-page: 69 ident: b0010 article-title: Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 24 start-page: 859 year: 2010 end-page: 871 ident: b0055 article-title: ESP: a tool to estimate scale parameters for multiresolution image segmentation of remotely sensed data publication-title: Int. J. Geogr. Inf. Sci. – volume: 8 start-page: 701 year: 2011 end-page: 705 ident: b0135 article-title: Object-oriented change detection for landslide rapid mapping publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 5 start-page: 463 year: 2008 end-page: 467 ident: b0070 article-title: Improvement of image segmentation accuracy based on multiscale optimization procedure publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 116 start-page: 24 year: 2010 end-page: 36 ident: b0140 article-title: Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods publication-title: Geomorphology – start-page: 125 year: 2011 end-page: 128 ident: b0065 article-title: A generic procedure for semantics-oriented landform classification using object-based image analysis publication-title: Geomorphometry 2011 – volume: 173–174 start-page: 30 year: 2012 end-page: 42 ident: b0190 article-title: Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data publication-title: Geomorphology – volume: 66 start-page: 473 year: 2011 end-page: 483 ident: b0110 article-title: Unsupervised image segmentation evaluation and refinement using a multi-scale approach publication-title: ISPRS J. Photogramm. Remote Sen. – volume: 49 start-page: 4928 year: 2011 end-page: 4943 ident: b0145 article-title: Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 3 start-page: 385 year: 1989 end-page: 397 ident: b0205 article-title: Spatial scaling in ecology publication-title: Funct. Ecol. – volume: 4 start-page: 1310 year: 2012 end-page: 1336 ident: b0100 article-title: A semi-automated object-based approach for landslide detection validated by persistent scatterer interferometry measures and landslide inventories publication-title: Remote Sens. – volume: 33 start-page: 6854 year: 2012 end-page: 6875 ident: b0170 article-title: Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery publication-title: Int. J. Remote Sens. – volume: 13 start-page: 884 year: 2011 end-page: 893 ident: b0200 article-title: Comparing object-based and pixel-based classifications for mapping Savannas publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 26 start-page: 2953 year: 2005 end-page: 2974 ident: b0220 article-title: Quality assessment for geo-spatial objects derived from remotely sensed data publication-title: Int. J. Remote Sens. – start-page: S40 year: 2011 end-page: S51 ident: b0025 article-title: Data fusion of extremely high resolution aerial imagery and LiDAR data for automated railroad centre line reconstruction publication-title: ISPRS J. Photogramm. Remote Sen. 66 – volume: 141–142 start-page: 21 year: 2012 end-page: 33 ident: b0050 article-title: Automated object-based classification of topography from SRTM data publication-title: Geomorphology – volume: 77 start-page: 933 year: 2011 end-page: 942 ident: b0180 article-title: Automated damage indication for rapid geospatial reporting. An operational object-based approach to damage density mapping following the 2010 Haiti earthquake publication-title: Photogramm. Eng. Remote Sens. – volume: 13 start-page: 3167 year: 1992 end-page: 3187 ident: b0210 article-title: Nested-hierarchical scene models and image segmentation publication-title: Int. J. Remote Sens. – volume: 115 start-page: 2564 year: 2011 end-page: 2577 ident: b0165 article-title: Object-oriented mapping of landslides using Random Forests publication-title: Remote Sens. Environ. – volume: 11 start-page: 2715 year: 2011 end-page: 2726 ident: b0130 article-title: Landslide mapping with multi-scale object-based image analysis – a case study in the Baichi watershed publication-title: Taiwan. Nat. Hazards Earth Sys. Sci. – volume: 34 start-page: 2583 year: 2012 end-page: 2606 ident: b0005 article-title: GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments publication-title: Int. J. Remote Sens. – volume: 8 start-page: 701 issue: 4 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0135 article-title: Object-oriented change detection for landslide rapid mapping publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2010.2101045 – volume: 21 start-page: 311 issue: 3 year: 1987 ident: 10.1016/j.isprsjprs.2013.11.018_b0215 article-title: The factor of scale in remote-Sens. publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(87)90015-0 – volume: 33 start-page: 6854 issue: 21 year: 2012 ident: 10.1016/j.isprsjprs.2013.11.018_b0170 article-title: Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2012.692829 – volume: 34 start-page: 2583 issue: 7 year: 2012 ident: 10.1016/j.isprsjprs.2013.11.018_b0005 article-title: GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2012.747018 – start-page: 769 year: 2008 ident: 10.1016/j.isprsjprs.2013.11.018_b0150 article-title: Assessing image segmentation quality-concepts, methods and applications – volume: 49 start-page: 4928 issue: 12 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0145 article-title: Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2011.2151866 – volume: 77 start-page: 933 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0180 article-title: Automated damage indication for rapid geospatial reporting. An operational object-based approach to damage density mapping following the 2010 Haiti earthquake publication-title: Photogramm. Eng. Remote Sens. doi: 10.14358/PERS.77.9.933 – volume: 24 start-page: 859 issue: 6 year: 2010 ident: 10.1016/j.isprsjprs.2013.11.018_b0055 article-title: ESP: a tool to estimate scale parameters for multiresolution image segmentation of remotely sensed data publication-title: Int. J. Geogr. Inf. Sci. doi: 10.1080/13658810903174803 – volume: 76 start-page: 193 issue: 2 year: 2010 ident: 10.1016/j.isprsjprs.2013.11.018_b0175 article-title: Object-based class modeling for cadastre-constrained delineation of Geo-objects publication-title: Photogramm. Eng. Remote Sens. doi: 10.14358/PERS.76.2.193 – volume: 126 start-page: 27 year: 2012 ident: 10.1016/j.isprsjprs.2013.11.018_b0085 article-title: Integrated assessment of the environmental impact of an IDP camp in Sudan based on very high resolution multi-temporal satellite imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2012.08.010 – year: 2000 ident: 10.1016/j.isprsjprs.2013.11.018_b0230 – volume: 116 start-page: 24 issue: 1–2 year: 2010 ident: 10.1016/j.isprsjprs.2013.11.018_b0140 article-title: Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods publication-title: Geomorphology doi: 10.1016/j.geomorph.2009.10.004 – volume: 65 start-page: 2 issue: 1 year: 2010 ident: 10.1016/j.isprsjprs.2013.11.018_b0035 article-title: Object based image analysis for remote sensing publication-title: ISPRS J. Photogramm. Remote Sen. doi: 10.1016/j.isprsjprs.2009.06.004 – volume: 32 start-page: 3747 issue: 13 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0080 article-title: Optimal region growing segmentation and its effect on classification accuracy publication-title: Int. J. Remote Sens. doi: 10.1080/01431161003777189 – volume: 26 start-page: 2953 issue: 14 year: 2005 ident: 10.1016/j.isprsjprs.2013.11.018_b0220 article-title: Quality assessment for geo-spatial objects derived from remotely sensed data publication-title: Int. J. Remote Sens. doi: 10.1080/01431160500057764 – volume: 118 start-page: 259 year: 2012 ident: 10.1016/j.isprsjprs.2013.11.018_b0060 article-title: A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.11.020 – volume: 173–174 start-page: 30 year: 2012 ident: 10.1016/j.isprsjprs.2013.11.018_b0190 article-title: Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data publication-title: Geomorphology doi: 10.1016/j.geomorph.2012.05.024 – volume: 27 start-page: 3035 issue: 14 year: 2006 ident: 10.1016/j.isprsjprs.2013.11.018_b0075 article-title: Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation publication-title: Int. J. Remote Sens. doi: 10.1080/01431160600617194 – volume: 110 start-page: 260 issue: 2 year: 2008 ident: 10.1016/j.isprsjprs.2013.11.018_b0225 article-title: Image segmentation evaluation: a survey of unsupervised methods publication-title: Comput. Vis. Image Underst. doi: 10.1016/j.cviu.2007.08.003 – volume: 11 start-page: 2715 issue: 10 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0130 article-title: Landslide mapping with multi-scale object-based image analysis – a case study in the Baichi watershed publication-title: Taiwan. Nat. Hazards Earth Sys. Sci. doi: 10.5194/nhess-11-2715-2011 – volume: 18 start-page: 428 year: 2012 ident: 10.1016/j.isprsjprs.2013.11.018_b0195 article-title: External geo-information in the segmentation of VHR imagery improves the detection of imperviousness in urban neighborhoods publication-title: Int. J. Appl. Earth Obs. Geoinf. doi: 10.1016/j.jag.2012.03.015 – volume: 15 start-page: 57 year: 2012 ident: 10.1016/j.isprsjprs.2013.11.018_b0010 article-title: Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images publication-title: Int. J. Appl. Earth Obs. Geoinf. doi: 10.1016/j.jag.2011.06.005 – volume: 82 start-page: 125 year: 2013 ident: 10.1016/j.isprsjprs.2013.11.018_b0015 article-title: Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective publication-title: ISPRS J. Photogramm. Remote Sen. doi: 10.1016/j.isprsjprs.2013.05.003 – volume: 34 start-page: 532 issue: 4 year: 2002 ident: 10.1016/j.isprsjprs.2013.11.018_b0090 article-title: Modeling multi-scale landscape structure within a hierarchical scale-space framework publication-title: Int Arch. Photogramm. Remote Sens. Spatial Inf. Sci. – volume: 58 start-page: 239 issue: 3–4 year: 2004 ident: 10.1016/j.isprsjprs.2013.11.018_b0030 article-title: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information publication-title: ISPRS J. Photogramm. Remote Sen. doi: 10.1016/j.isprsjprs.2003.10.002 – volume: 5 start-page: 558 issue: 2 year: 2013 ident: 10.1016/j.isprsjprs.2013.11.018_b0095 article-title: An object-based approach for mapping shrub and tree cover on grassland habitats by use of LiDAR and CIR orthoimages publication-title: Remote Sens. doi: 10.3390/rs5020558 – start-page: 291 year: 2008 ident: 10.1016/j.isprsjprs.2013.11.018_b0115 article-title: Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery – start-page: S40 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0025 article-title: Data fusion of extremely high resolution aerial imagery and LiDAR data for automated railroad centre line reconstruction publication-title: ISPRS J. Photogramm. Remote Sen. 66 doi: 10.1016/j.isprsjprs.2011.09.012 – volume: 5 start-page: 463 issue: 3 year: 2008 ident: 10.1016/j.isprsjprs.2013.11.018_b0070 article-title: Improvement of image segmentation accuracy based on multiscale optimization procedure publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2008.919622 – volume: 32 start-page: 2825 issue: 10 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0120 article-title: Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects publication-title: Int. J. Remote Sens. doi: 10.1080/01431161003745608 – volume: 3 start-page: 385 issue: 4 year: 1989 ident: 10.1016/j.isprsjprs.2013.11.018_b0205 article-title: Spatial scaling in ecology publication-title: Funct. Ecol. doi: 10.2307/2389612 – volume: 115 start-page: 2564 issue: 10 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0165 article-title: Object-oriented mapping of landslides using Random Forests publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.05.013 – volume: 13 start-page: 884 issue: 6 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0200 article-title: Comparing object-based and pixel-based classifications for mapping Savannas publication-title: Int. J. Appl. Earth Obs. Geoinf. doi: 10.1016/j.jag.2011.06.008 – volume: 13 start-page: 3167 issue: 16 year: 1992 ident: 10.1016/j.isprsjprs.2013.11.018_b0210 article-title: Nested-hierarchical scene models and image segmentation publication-title: Int. J. Remote Sens. doi: 10.1080/01431169208904109 – start-page: 125 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0065 article-title: A generic procedure for semantics-oriented landform classification using object-based image analysis – volume: 4 start-page: 1310 issue: 5 year: 2012 ident: 10.1016/j.isprsjprs.2013.11.018_b0100 article-title: A semi-automated object-based approach for landslide detection validated by persistent scatterer interferometry measures and landslide inventories publication-title: Remote Sens. doi: 10.3390/rs4051310 – start-page: 91 year: 2008 ident: 10.1016/j.isprsjprs.2013.11.018_b0040 article-title: Image objects and geographic objects – start-page: 12 year: 2000 ident: 10.1016/j.isprsjprs.2013.11.018_b0020 article-title: Multiresolution segmentation-an optimization approach for high quality multi-scale image segmentation – volume: 141–142 start-page: 21 year: 2012 ident: 10.1016/j.isprsjprs.2013.11.018_b0050 article-title: Automated object-based classification of topography from SRTM data publication-title: Geomorphology doi: 10.1016/j.geomorph.2011.12.001 – volume: 76 start-page: 289 issue: 3 year: 2010 ident: 10.1016/j.isprsjprs.2013.11.018_b0045 article-title: Accuracy assessment measures for object-based image segmentation goodness publication-title: Photogramm. Eng. Remote Sen. doi: 10.14358/PERS.76.3.289 – volume: 5 start-page: 4163 issue: 9 year: 2013 ident: 10.1016/j.isprsjprs.2013.11.018_b0105 article-title: Delineating Individual Trees from Lidar Data: A Comparison of Vector-and Raster-based Segmentation Approaches publication-title: Remote Sens. doi: 10.3390/rs5094163 – volume: 134 start-page: 260 issue: 3–4 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0160 article-title: Object-based gully feature extraction using high spatial resolution imagery publication-title: Geomorphology doi: 10.1016/j.geomorph.2011.07.003 – volume: 30 start-page: 75 issue: 2 year: 2006 ident: 10.1016/j.isprsjprs.2013.11.018_b0185 article-title: A framework for evaluating image segmentation algorithms publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2005.12.001 – volume: 66 start-page: 473 issue: 4 year: 2011 ident: 10.1016/j.isprsjprs.2013.11.018_b0110 article-title: Unsupervised image segmentation evaluation and refinement using a multi-scale approach publication-title: ISPRS J. Photogramm. Remote Sen. doi: 10.1016/j.isprsjprs.2011.02.006 – year: 1986 ident: 10.1016/j.isprsjprs.2013.11.018_b0155
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Snippet	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...
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SubjectTerms	algorithms Animal, plant and microbial ecology Applied geophysics Automated Automation Biological and medical sciences Computer programs computer software Earth sciences Earth, ocean, space Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques GEOBIA Image segmentation Imagery Internal geophysics Iterative methods MRS Object Representation Segmentation Segments Software spatial data Teledetection and vegetation maps variance
Title	Automated parameterisation for multi-scale image segmentation on multiple layers
URI	https://dx.doi.org/10.1016/j.isprsjprs.2013.11.018 https://www.ncbi.nlm.nih.gov/pubmed/24748723 https://www.proquest.com/docview/1531003380 https://www.proquest.com/docview/1826591871 https://www.proquest.com/docview/2101363972 https://pubmed.ncbi.nlm.nih.gov/PMC3990455
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