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Remote Sens. 2018, 10(1), 2; https://doi.org/10.3390/rs10010002

Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information?

1
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, D-76131 Karlsruhe, Germany
2
Institute of Computer Science II, University of Bonn, Friedrich-Ebert-Allee 144, D-53113 Bonn, Germany
*
Author to whom correspondence should be addressed.
Received: 12 October 2017 / Revised: 11 December 2017 / Accepted: 17 December 2017 / Published: 21 December 2017
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Abstract

In this paper, we investigate the value of different modalities and their combination for the analysis of geospatial data of low spatial resolution. For this purpose, we present a framework that allows for the enrichment of geospatial data with additional semantics based on given color information, hyperspectral information, and shape information. While the different types of information are used to define a variety of features, classification based on these features is performed using a random forest classifier. To draw conclusions about the relevance of different modalities and their combination for scene analysis, we present and discuss results which have been achieved with our framework on the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set. View Full-Text
Keywords: geospatial computer vision; multi-modal data; 3D point cloud; shape information; hyperspectral imagery; feature extraction; semantic classification; semantic information geospatial computer vision; multi-modal data; 3D point cloud; shape information; hyperspectral imagery; feature extraction; semantic classification; semantic information
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Weinmann, M.; Weinmann, M. Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information? Remote Sens. 2018, 10, 2.

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