Spectral resolution requirements for mapping urban areas

By Martin Herold, Margaret E. Gardner, Dar A. Roberts. IEEE Transactions on Geoscience and Remote Sensing, 2003

329 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/35deba09c83323b9330a9e2144480d0a45b05419

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Citations in the corpus

Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area (2012, background): “In contrast, in band 5, there is high reflectance when detecting built-up areas compared with vegetated areas [17].”
Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery (2006, methodology): “High spatial resolution imagery initially thrives on the application of urban-related feature extraction has been used (Jensen and Cowen, 1999; Benediktsson et al., 2003; Herold et al., 2003a), but there has not been as much work in detailed vegetation mapping using high spatial resolution imagery.”
Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography (2008, unknown): “As we did here, most researchers use standard error matrices (Congalton & Green, 1999; Foody, 2002) to evaluate the accuracy of object-based remotely sensed map products (e.g., Bauer & Steinnocher, 2001; Herold et al., 2003; Kettig & Landgrebe, 1976).”
Assessment of Very High Spatial Resolution Satellite Image Segmentations (2005, methodology): “Moreover, the spatial attributes, like texture, calculated with mobile windows, smooth the boundaries between discrete land cover region (Herold et al., 2003a; Carleer and Wolff, 2004) and create between-class texture, which is often more distinctive than within-class texture.”
Landsat continuity: Issues and opportunities for land cover monitoring (2008, background): “Besides monitoring vegetation cover, monitoring other land surface types such as urban areas may require different spectral information (Herold et al., 2003).”
Derivation of an urban materials spectral library through emittance and reflectance spectroscopy (2014, unknown): “…such as mapping of urbanisation (Deng et al., 2009), identiﬁcation of impervious surface cover for the quantiﬁca-tion of ﬂood risk (Schueler, 1994), assessment of concrete quality (Brook and Ben-Dor, 2011), and evaluation of road network conditions (Herold et al., 2003; Pascucci et al., 2008).”
The role of spatial metrics in the analysis and modeling of urban land use change (2005, methodology): “…cover classification (3 classes: buildings, vegetation and rest) using the eCognition software, which segments the image and allows for the incorporation of spatial and contextual information of object features in the image classification process (Baatz et al., 2001; Herold, Liu & Clarke, 2003).”
Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data (2010, background): “They enable discriminating between spectrally similar land cover classes that occur at highly frequent spatial patterns [3].”