Integrating Contextual Information with per-Pixel Classification for Improved Land Cover Classification

By J. Stuckens, Pol R. Coppin, Marvin E. Bauer. 2000

326 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/c9e0fa2e61f942d06cf06febbb878da4ef009ee4

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Citations in the corpus

Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement (2009, background): “), geology, soils, hydrology, transportation network, vegetation, enable us to achieve greater classification accuracy [9,14,33,34].”
A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, methodology): “…image classification approaches can be grouped into three categories, including 1) texture extraction, 2) Markov random fields (MRFs) modeling, and 3) image segmentation and object-based image analysis [Stuckens et al., 2000; Blaschke, 2010; Thoonen et al., 2012; Moser et al., 2013].”
Contextual classification of Landsat TM images to forest inventory cover types (2004, methodology): “…methods under realistic conditions in a forest inventory context remains scant, but published studies indicate an increase in accuracy after including context in the classification schemes (Palubinskas et al. 1995, Slaymaker et al. 1996, Flygare 1997, Stuckens et al. 2000, Hubert-Moy et al. 2001).”
Developments in Landsat Land Cover Classification Methods: A Review (2017, background): “Contextual ECHO, Majority filter TM Urban area 72–81 [136,137] ECHO, Majority filter TM Forest plantation 70–81 [136,137] ECHO, Majority filter TM, ETM+ Dense forest 72–82 [136,137] NN MSS Open forest 66–90 [57,136,137] ECHO, Majority filter TM, ETM+ Agricultural area 66–97 [136,137]”
Improved landsat-based forest mapping in steep mountainous terrain (2003, background): “At present, segmentation and object-based classification works very well for images with spatially discontinuous elements with abrupt borders such as large agricultural fields or metropolitan areas surrounded by forests (Stuckens et al., 2000).”
Modelling butterfly distribution based on remote sensing data (2002, result): “Introduction of object orientated segmentation methods could lead to more reliable classification results, particularly in small structured landscapes like ours (see, e.g. Kilpeläinen & Tokola, 1999; Stuckens et al., 2000; Mäkelä & Pekkarinen, 2001).”
Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability (2008, background): “…accuracy of land cover, although various contextual and hybrid classification procedures have been developed for the incorporation of contextual information over the last 20 years (Kittler and Föglein 1984, Ryherd and Woodcock 1996, Lobo 1997, Stuckens et al. 2000, Melgani and Serpico 2002).”
Monitoring and modelling of urban sprawl using remote sensing and GIS techniques (2008, background): “Remote sensing pattern recognition approaches, such as supervised, unsupervised and knowledge-based expert system approaches (Greenberg and Bradley, 1997; Vogelmann et al., 1998; Stuckens et al., 2000; Stefanov et al., 2001; Sugumaran et al., 2003; Lu and Weng, 2005; Mundia and Aniya, 2005) have bee”
Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China (2006, background): “2000 ), seed areas (Adams and Bischof 1994 ), or initial edge detection‐based segmentations (Egawa and Kusaka 1988 , Tilton 1996).”
A survey of image classification methods and techniques for improving classification performance (2007, unknown): “…in developing advanced classification approaches and techniques for improving classification accuracy (Gong and Howarth 1992, Kontoes et al. 1993, Foody 1996, San Miguel-Ayanz and Biging 1997, Aplin et al. 1999a, Stuckens et al. 2000, Franklin et al. 2002, Pal and Mather 2003, Gallego 2004).”
Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery (2011, background): “…system (GIS; Geneletti and Gorte 2003), (5) reduce occurrence of smaller mapping units, resulting in a more attractive classification map (Stuckens et al. 2000), (6) create objects from segmentation that are more visually recognizable than pixels, and (7) ecologically speaking, provide…”