Object‐based change detection using correlation image analysis and image segmentation

By Jungho Im, J. R. Jensen, J. Tullis. 2008

459 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/9d1ccc5e9fc128742de34c86b219580b864b49c8

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In Atmospheric Science

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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): “Image segmentation is a principal function that splits an image into separated regions or objects depending on parameters specified (Im et al., 2008; Lee & Warner, 2006; Myint et al., 2008; Stow et al., 2008).”
A review of multimodal image matching: Methods and applications (2021, methodology): “[498] performed change detection by using correlation image analysis and image segmentation.”
A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “The generalising properties of OBIA are however actively used as a 477 means of removing spurious, ‘salt and pepper’ features (Boldt et al., 2012; Im et al., 2008).”
A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “…et al., 2008; Chen and Hay, 2012; Contreras et al., 2016; Dissanska et al., 2009; Doxani et al., 2012; Doxani et al., 2008; Hussain et al., 2013; Im et al., 2008; Nebiker et al., 2014; Walter, 2004); land cover and land use mapping including vegetation, tree, water, residential, etc. (Baker et…”
Object based image analysis for remote sensing (2010, methodology): “Coincidentally, Im et al. (2008) compared three different change detection techniques, based on object/neighbourhood correlation, image analysis and image segmentation, with two different perpixel approaches, and found that object based change classifications were superior (KIA up to 90”
Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal (2010, background): “The segmentation process stops when the image objects exceeds a user-defined threshold [14].”
Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, background): “Getting detailed ‘‘from-to’’ change information is mostly required in many change analysis studies; however, a simple binary change vs. no change is often described as sufficient for many studies (Im et al., 2008a).”
Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine (2017, background): “Object-based analysis can improve salt and pepper effects and increase classification accuracies over pixel-based image classification [82,83].”