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By Nicolas Lusin

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2007 8:56pm Compositor Name: VBalamugundan Remote Sensing of Impervious Surfaces rapid urbanization is the concurrent increase (Xian and Crane, 2005) in impervious surfaces, which include roads, sidewalks, parking lots, and various rooftops, through which water cannot infiltrate into soil (Arnold and Gibbons, 1996). , 2004) partly funded by NASA’s land-cover land-use change program found that the aggregate impervious surface area of continental United States was slightly less than the total area of the state of Ohio.

2005) and require fewer training samples (Pal and Mather, 2003). , 2004). ANNs are also capable of estimating subpixel imperviousness from medium-resolution satellite imagery such as Landsat TM and ETMþ (Civco and Hurd, 1997; Flanagan and Civco, 2001; Lee and Lathrop, 2006). Moreover, a comparison of spectral unmixing models states that neural network models outperform the traditional linear unmixing models (Liu and Wu, 2005). Although ANN has the ability to generate detailed land-use and land-cover information, its potential in extracting subpixel impervious surface information from highresolution remote sensing imagery is still in question.

The methodology including a general description of neural network and activation-level maps is described in Section 3. Section 4 reports the results and verification of the results. Finally, in Section 5, the chapter ends with conclusions and future scope. 1) covers Grafton village and township in Ozaukee County, Wisconsin, United States. This area was chosen because of the availability of the IKONOS image and its diversified urban and rural land uses. The total land area of Grafton is roughly 24 mi2 (Census, 2000).

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Leçons sur les ensembles analytiques et leurs applications by Nicolas Lusin


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