期刊
REMOTE SENSING
卷 10, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/rs10010002
关键词
geospatial computer vision; multi-modal data; 3D point cloud; shape information; hyperspectral imagery; feature extraction; semantic classification; semantic information
类别
资金
- Deutsche Forschungsgemeinschaft
- Karlsruhe Institute of Technology
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.
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