Image Segmentation Methods for Object-based Analysis and Classification

By Thomas Blaschke, Charles Burnett, Anssi Pekkarinen. 2004

247 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “mogeneous spectral, color, or texture information. Due to the superiority compared to pixel-level approaches, object-level methods have dominated the task of remote sensing image analysis for decades [70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]. Although pixel- and object-level classification method”
An automated object-based approach for the multiscale image segmentation of forest scenes (2005, methodology): “005 towards the development and application of multiscale object-based methods (Blaschke et al., 2000; Hay et al., 2001, 2003; Castilla, 2003; Blaschke et al., 2004).”
Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery (2006, background): “The requirement of high-resolution imagery analysis and availability of commercial or non-commercial soft packages catalysed a boost of their application (Blaschke et al., 2004).”
A Survey on Object Detection in Optical Remote Sensing Images (2016, methodology): “…Blaschke et al., 2014). techniques have been 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…”
Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability (2008, methodology): “2.5 Segmentation While many different segmentation algorithms exist (Pal and Pal 1993, Freixenet et al. 2002, Blaschke et al. 2004), there are very few operational software environments for the processing of remotely sensed data (for a comparison, see Meinel and Neubert 2004).”
Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, methodology): “Besides these two groups, Blaschke et al. (2006) and Kohler (1981) considered pixel segmentation, which includes thresholdbased segmentation, as a third group.”
Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis (2011, background): “Therefore, a single-scale factor, the principal segment size controlling parameter, for the generation of optimum segments/objects (group of homogeneous pixels) in such situations does not exist [10].”