Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends
Qihao Weng

Type: Paper
Tags:Artificial neural networks

title = "Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends ",
journal = "Remote Sensing of Environment ",
volume = "117",
number = "",
pages = "34 - 49",
year = "2012",
note = "Remote Sensing of Urban Environments ",
issn = "0034-4257",
doi = "",
url = "",
author = "Qihao Weng",
keywords = "Urban remote sensing",
keywords = "Impervious surfaces",
keywords = "Remotely sensed data characteristics",
keywords = "Urban mapping requirements",
keywords = "Pixel-based algorithms",
keywords = "Sub-pixel based algorithms",
keywords = "Object-oriented method",
keywords = "Artificial neural networks ",
abstract = "The knowledge of impervious surfaces, especially the magnitude, location, geometry, spatial pattern of impervious surfaces and the perviousness–imperviousness ratio, is significant to a range of issues and themes in environmental science central to global environmental change and human–environment interactions. Impervious surface data is important for urban planning and environmental and resources management. Therefore, remote sensing of impervious surfaces in the urban areas has recently attracted unprecedented attention. In this paper, various digital remote sensing approaches to extract and estimate impervious surfaces will be examined. Discussions will focus on the mapping requirements of urban impervious surfaces. In particular, the impacts of spatial, geometric, spectral, and temporal resolutions on the estimation and mapping will be addressed, so will be the selection of an appropriate estimation method based on remotely sensed data characteristics. This literature review suggests that major approaches over the past decade include pixel-based (image classification, regression, etc.), sub-pixel based (linear spectral unmixing, imperviousness as the complement of vegetation fraction etc.), object-oriented algorithms, and artificial neural networks. Techniques, such as data/image fusion, expert systems, and contextual classification methods, have also been explored. The majority of research efforts have been made for mapping urban landscapes at various scales and on the spatial resolution requirements of such mapping. In contrast, there is less interest in spectral and geometric properties of impervious surfaces. More researches are also needed to better understand temporal resolution, change and evolution of impervious surfaces over time, and temporal requirements for urban mapping. It is suggested that the models, methods, and image analysis algorithms in urban remote sensing have been largely developed for the imagery of medium resolution (10–100 m). The advent of high spatial resolution satellite images, spaceborne hyperspectral images, and LiDAR data is stimulating new research idea, and is driving the future research trends with new models and algorithms. "

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