Assessing object-based classification: advantages and limitations

By Desheng Liu, Fan Xia. 2009

423 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/04351915342cf157125bf0d99ace5b5e18403e1d

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Citations in the corpus

Mapping vegetation morphology types in a dry savanna ecosystem: integrating hierarchical object-based image analysis with Random Forest (2014, background): “Visual inspection of segmentation results confirmed that beyond a scale value of 60, it was no longer possible to find representative homogeneous objects as the likelihood of class mixture within the object increased with object size (Liu and Xia 2010).”
Developments in Landsat Land Cover Classification Methods: A Review (2017, background): “However, OBIA land cover classification has limitations such as challenges in selecting the optimal segmentation scale, which can generate errors due to over or under segmentation, and misclassification of small land cover types due to the low or medium spatial resolution of Landsat images [94,105].”
Unsupervised image segmentation evaluation and refinement using a multi-scale approach (2011, positive): “In a recent study, Liu and Xia (2010) found that classification accuracy decreased considerably when over-or under-segmentation occurred(especially under-segmentation) because (a) the spatial, textual, and contextual information extracted from over-segmented objects was not very useful for…”
Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, background): “…creates bigger objects which cover more than one real-world feature and also cover the mixed classes, whereas over-segmentation creates smaller parts of real-world objects which then need to be merged to create a more realistic representation of actual features (Liu and Xia, 2010).”
Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification (2013, background): “The FNEA algorithm starts from individual pixels and grows regions by minimizing the average heterogeneity for a given number of image objects (Definiens 2007; Liu and Xia 2010).”
Relationship between X-band backscattering coefficients from high-resolution satellite SAR and biophysical variables in paddy rice (2013, methodology): “For example, the segmentation of high-resolution multispectral images allows us to obtain supplemental information on each field such as the shape, orientation and vegetation cover (e.g. Inoue et al. 2010, Liu and Xia 2010).”