Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

<b>We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to...

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Bibliographic Details
Main Authors: Stefan Bachhofner, Ana-Maria Loghin, Johannes Otepka, Norbert Pfeifer, Michael Hornacek, Andrea Siposova, Niklas Schmidinger, Kurt Hornik, Nikolaus Schiller, Olaf Kähler, Ronald Hochreiter
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Remote Sensing
Subjects:
3D segmentation
deep learning
derived point clouds
tri-stereo
VHR Satellite Imagery
2.5D segmentation
Online Access:https://www.mdpi.com/2072-4292/12/8/1289

Internet

https://www.mdpi.com/2072-4292/12/8/1289

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