Forest change detection by statistical object-based method

By Baudouin Desclée, Patrick Bogaert, Pierre Defourny. 2006

571 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/879d770013f68a58ce4805c35317b0c986f1e407

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Citations in the corpus

Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis (2018, methodology): “, 2006), robustness against shadowing and registration errors (Desclée et al., 2006; Mäkelä and Pekkarinen, 2001), and reduced salt-and-pepper effect apparent in per-pixel classifications (Matton et al.”
Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference (2021, methodology): “In general, change detection plays an important role in urban planning [1], forest monitoring [2], disaster assessment [3]–[5], and the dynamic detection of land use [6].”
A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “For 557 example, kernel based texture (He et al., 2011), multi-temporal image-object texture (Desclée et al., 2006), 558 image-object shape comparison (Boldt et al., 2012), local image correlation from kernel (Im & Jensen, 2005) 559 and multi-temporal image-objects (Im et al., 2008) and lastly,…”
Pan-tropical monitoring of deforestation (2007, methodology): “Interpretation accuracies of 80–95% are achievable for monitoring changes in forest cover with mid-resolution imagery when using only two classes, forest and non-forest (Desclée et al 2006).”
Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery (2011, background): “The object-centered classification prototype generally starts with the generation of segmented objects at multiple level of scales as fundamental units for image analysis, instead of considering a perpixel basis at a single scale for classification (Desclée et al., 2006; Im et al., 2008; Myint et al”
Object based image analysis for remote sensing (2010, background): “Spatial information in imagery includes aspects such as image texture, contextual information, pixel proximity, and geometric attributes of features (Narumalani et al., 1998; Gitas et al., 2003; Laliberte et al., 2004; Blaschke et al., 2004; Ivits et al., 2005; Chubey et al., 2006; Desclée et al., 2”