Object-Oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications

By Thomas Blaschke, S. Lang, Eric. J. Lorup, J. Strobl, P. Zeil. 2000

444 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f35d94276ee54f720192304e2da403371689dbb1

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Citations in the corpus

A comparison of three image-object methods for the multiscale analysis of landscape structure (2003, background): “2) of the resulting segmented patches of forests, pastures, fields and built-up areas have motivated several European agencies to seriously evaluate this approach.”
Comparing object-based and pixel-based classifications for mapping savannas (2011, background): “…a per-pixel classification for the purposes of mapping vegetation cover at a community scale, the spectral heterogeneity within a particular land cover can lead to spurious (misclassified at that scale) pixels appearing within classes creating a ‘salt and pepper’ effect (Blaschke et al., 2000).”
QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: Implications for control and land use management (2008, methodology): “By dividing a remotely-sensed image of a landscape into meaningful objects, the discrimination of more intuitive features results, although defining the “meaningful objects” can be problematic (Blaschke et al., 2000).”
What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS (2001, methodology): “In many cases, image analysis leads to meaningful objects only when the image is segmented into ‘homogeneous’ areas (Gorte 1998, Molenaar 1998, Baatz & Schäpe 2000, Blaschke et al. 2000).”
Object based image analysis for remote sensing (2010, background): “Although published around the year 2000, before the use of the terms ‘object-oriented image analysis’ or ‘objectbased image analysis’ (Blaschke et al., 2000; Blaschke and Strobl, 2001; Blaschke and Hay, 2001) therewere some influential articles that can be mentioned specifically as having been impor”
Object-based classification of remote sensing data for change detection (2004, background): “A good overview of different approaches can be found in Blaschke et al. (2000).”
A review of deep learning methods for semantic segmentation of remote sensing imagery (2021, background): “Examples of semantic segmentation of remote sensing imagery include environmental monitoring (Blaschke, Lang, Lorup, Strobl, & Zeil, 2000; Yuan & Sarma, 2011), crop cover and type analysis (Yang, Chen, Yuan, & Liu, 2016; Kussul, Lavreniuk, Skakun, & Shelestov, 2017; Jadhav & Singh, 2018), tree…”
A multi-scale segmentation/object relationship modelling methodology for landscape analysis (2003, methodology): “This is crucial for the landscape used in this study and is one of the main reasons why FNEA was chosen, although many other segmentation algorithms have been tested (Blaschke et al., 2000).”
An automated object-based approach for the multiscale image segmentation of forest scenes (2005, methodology): “All rights reserved. doi:10.1016/j.jag.2005.06.005 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).”