Object-based classification of remote sensing data for change detection

By V. Walter. 2004

630 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/33219ffeb3c4578d0c7467b322b1b5cd6db90202

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Citations in the corpus

Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery (2011, background): “…classes in such scenes (Campbell, 2007), because they use spectral information (pixel values) alone as a basis to analyze and classify remote sensing images, and ignore spatial information and a group of pixels that need to be considered together as an object (Bentz et al., 2004; Walter, 2004).”
Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability (2008, methodology): “We employed a so-called object-based classification methodology (Benz et al. 2004, Blaschke et al. 2004, Walter 2004) for several reasons.”
Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, methodology): “Walter (2004) integrated GIS and OBIA for OBCD and used maximum likelihood classification (MLC).”
A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “In addition to these one could also consider 62 mapping objects, typically vector polygons derived from field survey, or stereo or mono photogrammetry 63 (Comber et al., 2004b; Sofina et al., 2012; Walter, 2004).”
Forest change detection by statistical object-based method (2006, methodology): “Initially, object boundaries specified by forest stand delineation vectors were derived from a Geographic Information System (GIS) (Coppin & Bauer, 1995; Heikkonen & Varjo, 2004; Kayitakire et al., 2002; Varjo, 1996; Walter, 2004; Wulder et al., 2004).”
Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China (2006, neutral): “The classification can be very simple, for example by comparing the mean grey values of the objects with those of the training samples, those objects are classified to the classes to which they are most close; but also can be very complex, combining ancillary data, using shape characteristics and ne”
Geographic Object-Based Image Analysis – Towards a new paradigm (2014, methodology): “Interestingly, GEOBIA methods are only one of the 63 speciﬁed by Weng, although its number of literature references per category (from international journals between 2003 and 2004) is the highest overall.”
Unmanned Aerial Vehicle for Remote Sensing Applications - A Review (2019, background): “However, if semantic meanings of the changed objects are needed, the classiﬁcation still needs to be performed either on the di ﬀ erence maps or independently on each temporal dataset [126].”
Remote Sensing of Irrigated Agriculture: Opportunities and Challenges (2010, methodology): “has been widely used for agricultural applications [94,95].”
A survey of image classification methods and techniques for improving classification performance (2007, neutral): “2004 , Walter 2004 ), which does not require the use of GIS vector data.”
Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network (2017, methodology): “…following the idea of Liang-Chieh Chen et al. [30], we use “atrous” convolution (also known as “dilation” convolution in other studies) instead of the ordinary convolution to increase the feature density, and build the multi-scale classification model by adding the skip-layer network architecture.”
Object based image analysis for remote sensing (2010, neutral): “…and Defourny, 2008; Neubert et al., 2008; Trias-Sanz et al., 2008; Weidner, 2008), OBIA change detection (Civco et al., 2002; Hall and Hay, 2003; Walter, 2004; Blaschke, 2005; Desclée et al., 2006; Niemeyer et al., 2008; Stow et al., 2008; Conchedda et al., 2008; Schöpfer et al., 2008; Bontemps…”