Status of land cover classification accuracy assessment

By Giles M. Foody. 2002

4,540 citations recorded by the source corpus.

Subfields: Ecology, Geography, Planning and Development, Media Technology

View Paper: https://www.semanticscholar.org/paper/765980abe01a7415267aee710faf946ef9a06d78

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Citations in the corpus

Land cover 2.0 (2018, background): “Approaches, protocols and sampling designs for the collection of calibration and validation data have matured over time (e.g. Janssen et al. 1994; Foody 2002, Stehman 2009; Stehman 2013) with increased emphasis on calibration data needs relative to speci ﬁ c data inputs (i.e. time series, Pelletier…”
Machine learning in geosciences and remote sensing (2016, methodology): “A common application of remote sensing is production of thematic maps using an image classi ﬁ cation (Foody, 2002).”
Forest Types Classification Based on Multi-Source Data Fusion (2017, background): “The user’s accuracy level refers to the ratio of pixels correctly classified under the class of interest to the number of total pixels classified under the class of interest by a classifier [50].”
Recent Advances in Techniques for Hyperspectral Image Processing (2009, methodology): “As representative classification metrics, we used the overall accuracy and the kappa coefficient (Foody, 2002).”
Measuring and modelling biodiversity from space (2008, background): “The validity of the maps derived from remote sensing is a critical issue but is fraught with diffi culty (Foody, 2002).”
Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques (2005, result): “This study builds on previous discussions of accuracy assessments for remote sensing classifications (Congalton, 1991; Foody, 2002; Parker- Williams & Hunt, 2004; Stehman & Czaplewski, 1998; Story & Congalton, 1986).”
Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, background): “Validating maps of pest damage derived from remote sensing There is a considerable body of literature concerning the assessment of accuracy for a product derived from remote sensing imagery (Foody 2002; Congalton and Green 2009; Stehman 2009; Olofsson et al. 2014).”
Exploiting synergies of global land cover products for carbon cycle modeling (2006, background): “, trees, shrubs, and grasses into discrete categories is problematic (Foody, 2002).”
A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI (2013, methodology): “overall accuracy (OA) and the kappa coefficient (Kappa) on the basis of the error matrix [45] were employed to evaluate the accuracy of the LSW map.”
A Comparison of Evaluation Techniques for Building Extraction From Airborne Laser Scanning (2009, background): “Completeness is also referred to as Detection Rate [10] or as Producer’s Accuracy [8], and is the percentage of entities in the reference that were detected.”
Comparative assessment of the measures of thematic classification accuracy (2007, methodology): “As a result, assessing thematic map accuracy has received considerable attention in the remote sensing literature (Foody, 2002).”
Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges (2010, background): “Nonetheless, a number of problems arise when trying to create abrupt thresholds (boundaries) for identifying explicit spatial units such as patches (Foody, 2002, 2008b), leading some researchers to ask explicitly if a landscape can actually be viewed as crisp, with abrupt boundaries (Southworth et…”