Multi-resolution, object-oriented fuzzy analysis of remote sensing data to GIS ready information

By U. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, Markus Heynen. Research on computing science, 2003

2,812 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/7d617f7b6b1b618c69c47d83bb86819be1e22944

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

A survey of image classification methods and techniques for improving classification performance (2007, neutral): “The eCognition method is so far the most commonly used object-oriented classification (Benz et al. 2004, Wang et al. 2004).”
Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends (2012, methodology): “Object based image analysis (OBIA) has been increasingly used in remote sensingapplicationsdue to theadventofhigh resolution satellite imagery and the emergence of commercial software (Benz et al., 2004; Wang et al., 2004).”
Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery (2006, background): “The merging criterion is that average heterogeneity of image objects weighted by their size in pixels should be minimized (Baatz and Schape, 2000; Benz et al., 2004).”
An automated object-based approach for the multiscale image segmentation of forest scenes (2005, positive): “In 2000, eCognition became the first (non-research focused) commercially available software for multiresolution segmentation and object-oriented fuzzy-rule classification, specially suited for high-resolution imagery (Burnett and Blaschke, 2003; Van der Sande et al., 2003; Benz et al., 2004).”
A Survey on Object Detection in Optical Remote Sensing Images (2016, neutral): “…2015b; Martha et al., 2010; Martha et al., 2011; Martha et al., 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…”
High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials (2015, methodology): “In this object-oriented classification, the features to be classified are not single pixels, but spatial pixel clusters defined through an aggregation algorithm, known as “multi-resolution segmentation” [39].”
Integration of forest mapping and inventory to support forest management (2010, methodology): “In the last decade alternative techniques have been developed based on objectoriented classification (Benz et al. 2004) of polygons produced by image segmentation.”
A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, background): “Many studies have proven that significant higher accuracy has been achieved with object-based approaches [Benz et al., 2004; Wang et al., 2004; Myint et al., 2011].”
Object based image analysis for remote sensing (2010, methodology): “, 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 levels are targeted for specific applications (Lang and Langanke, 2006; Lang, 2008; Weinke et al.”
Land-cover classification with high-resolution remote sensing images using transferable deep models (2018, background): “…RS images using the spectral features of individual pixels (Jensen and Lulla, 1986; Gong et al., 1992; Casals-Carrasco et al., 2000), but their performance is often heavily affected by intra-class spectral variations and noises (Blaschke, 2001; Burnett and Blaschke, 2003; Benz et al., 2004).”
Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China (2006, methodology): “( 2004 ), human interpretation and correction was listed as a method of validation of segmentation result.”
Identifying Santa Barbara's urban tree species from AVIRIS imagery using canonical discriminant analysis (2013, background): “Crown-level, as opposed to pixel-level, classification may further enhance our ability to make useful maps, with less ‘salt and pepper’ noise (Benz et al. 2004, Myint et al. 2011).”