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Title:

Deep neural networks for above-ground detection in very high spatial resolution digital elevation models

Document type:
Zeitschriftenaufsatz
Author(s):
Marmanis, Dimitrios; Adam, F; Datcu, Mihai; Esch, Thomas; Stilla, Uwe
Abstract:
Deep Learning techniques have lately received increased attention for achieving state-of-the-art results in many classification problems, including various vision tasks. In this work, we implement a Deep Learning technique for classifying above-ground objects within urban environments by using a Multilayer Perceptron model and VHSR DEM data. In this context, we propose a novel method called M-ramp which significantly improves the classifier’s estimations by neglecting artefacts, minimizing conve...     »
Keywords:
Deep Learning, Multilayer Perceptrons, Ground Filtering, DEM, Classification
Journal title:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Year:
2015
Journal volume:
2
Journal issue:
3
Pages contribution:
103
Fulltext / DOI:
doi:10.5194/isprsannals-II-3-W4-103-2015
WWW:
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W4/103/2015/isprsannals-II-3-W4-103-2015.pdf
Publisher:
Copernicus GmbH
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