Download Remote Sensing of Impervious Surfaces (Taylor & Francis by Qihao Weng PDF

By Qihao Weng

Distant sensing of impervious surfaces has matured utilizing advances in geospatial expertise so fresh that its purposes have bought purely sporadic assurance in distant sensing literature. distant Sensing of Impervious Surfaces is the 1st to concentration solely in this constructing box. It presents specific insurance of mapping, facts extraction, and modeling strategies particular to interpreting impervious surfaces, similar to roads and structures. Written via well known specialists within the box, this publication experiences the most important techniques that practice to this rising box in addition to present demanding situations, advancements, and tendencies. The authors introduce distant sensing electronic photo processing innovations for estimating and mapping impervious surfaces in city and rural components. offering the most recent modeling instruments and algorithms for facts extraction and research, the e-book explains easy methods to differentiate roads, roofs, and different artifical buildings from remotely sensed pictures for person research. the ultimate chapters study the way to use impervious floor info for predicting the circulation of hurricane- or floodwater and learning traits in inhabitants, land use, source distribution, and different real-world purposes in environmental, city, and local making plans. each one bankruptcy bargains a constant structure together with a concise overview of simple strategies and methodologies, well timed case stories, and counsel for fixing difficulties and examining facts utilizing the innovations provided.

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Extra info for Remote Sensing of Impervious Surfaces (Taylor & Francis Series in Remote Sensing Applications)

Sample text

A feed-forward back-propagation neural network calculates a pixel’s activation values for all land-cover classes under consideration. In a hard ANN classification, the highest activation value is selected and the pixel is assigned to the corresponding class. However, the same activation values can be used to represent land-cover class memberships (Lee and Lathrop, 2006) and this is known as soft ANN classification approach. In this study, a soft ANN classification approach was followed to estimate the proportion of impervious surface for each individual pixel of the IKONOS image.

Although tree-covered areas are included in the calibration models, they are still a likely source of error. However, the error was <6% in a preliminary evaluation of this effect in an urban area with varying amounts of tree cover. (4) Differences in image acquisition dates and vegetation condition and phenology within images. We used mostly August images, but several are early September, which likely had somewhat less greenness for grass that was not irrigated than irrigated lawns. Similarly, early senescing trees would have less greenness.

4 Training and Testing Sample Selection....................................... 4 Results........................................................................................................... 1 Output Transformation.................................................................. 2 Result Verification .......................................................................... 5 Conclusion ................................................................................................... 34 References .............................................................................................................

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