A comparison of three image-object methods for the multiscale analysis of landscape structure

By Geoffrey J. Hay, Thomas Blaschke, Danielle J. Marceau, A. Bouchard. 2003

451 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Global and Planetary Change, Media Technology, Remote Sensing

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Citations in the corpus

Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms (2010, background): “…vector machines and maximum likelihood classification algorithms J.R. Otukei a,b,*, T. Blaschke b a Department of Surveying, Makerere University, P.O. Box 7062, Makerere Hill Road, Kampala, Uganda b Z_GIS Centre for GeoInformatics, University of Salzburg, Hellbrunner Str. 34, 5020 Salzburg, Austria”
Object-based contextual image classification built on image segmentation (2003, background): “Only very recently, several approaches in image analysis and pattern recognition are exploited to generate hypothesis for the segmentation rules as an alternative to knowledge-based segmentation [8, 32, 26].”
Unsupervised image segmentation evaluation and refinement using a multi-scale approach (2011, unknown): “Other studies have employed a multi-scale approach , creating a hierarchy of several single-scale segmentations (Hay et al., 2003; Blaschke et al., 2004; Trias-Sanz et al., 2008; Xie et al., 2008; Zhou and Troy, 2009).”
Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery (2009, methodology): “and entropy) statistics of the GLCM are calculated, and the fractal net evolution approach [9] is employed for the object-based analysis.”
Object based image analysis for remote sensing (2010, background): “It is well known that semantically significant regions are found in an image at different scales of analysis (Hay et al., 2001, 2003), and OBIA is inextricably linked to multiscale analysis concepts (Burnett and Blaschke, 2003; Benz et al., 2004; Lang, 2008; Hay and Castilla, 2008), even if single…”
Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning (2017, background): “OOA is mainly dealing with the measuring unit of ‘object’, which can be defined as ‘individually resolvable entities located within a digital image which are perceptually generated from high-resolution pixel groups’ (Hay et al., 2003).”
Morphological Building/Shadow Index for Building Extraction From High-Resolution Imagery Over Urban Areas (2012, methodology): “In this study, the bottom-up region merging algorithm [13] is used for segmentation.”
Analyzing High Resolution Topography for Advancing the Understanding of Mass and Energy Transfer Through Landscapes: A Review (2015, background): “Clearly, objects exist across a variety of scales in HRT, and so segmentation often requires a multi-scale analysis (Hay et al., 2001; Burnett and Blaschke, 2003; Hay et al., 2003; Schmidt and Andrew, 2005; Brodu and Lague, 2012).”
Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability (2008, background): “…that OBIA offers an efficient methodological framework for machine-based interpretation of complex classes of urban structure, defined by spectral, spatial, and structural as well as hierarchical properties (Burnett and Blaschke 2003, Hay et al. 2003, Benz et al. 2004, Schöpfer et al. in press).”
Vessel detection and classification from spaceborne optical images: A literature survey (2018, background): “However, only remote sensing technologies represent a source of large area satellite data while other domains provide useful concepts, methods, models, tools and insights for defining, visualising, querying and managing these data (Hay et al., 2003).”
An automated object-based approach for the multiscale image segmentation of forest scenes (2005, methodology): “While eCognition does provide a commercially available toolset that incorporates interesting segmentation, topological and semantic components, the setting of an important ‘scale parameter’ largely responsible for object segmentation that is not intuitively linked to a specific spatial scale or obje”
Geographic Object-Based Image Analysis – Towards a new paradigm (2014, unknown): “The subsequent area of research was referred to as object-based image analysis (OBIA) although terms like ‘‘object-oriented’’ and ‘‘ob-ject-speciﬁc’’ were often used (Hay et al., 1996, 2003; Blaschke et al., 2004).”
A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “, 2012; Stumpf and Kerle, 2011), land cover and land use mapping (Baker et al., 2013; Benz et al., 2004; Blaschke, 2003; Blaschke et al., 2011; Blaschke et al., 2008; Contreras et al., 2015; D'Oleire-Oltmanns et al., 2014; De Pinho et al., 2012; Doleire-Oltmanns et al., 2013; Drăguţ and Blaschke, 20”