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

Martin Weinmann und Michael Weinmann
In: Remote Sensing (2018), 10:1
 

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.

Bibtex

@ARTICLE{weinmann-2018-RemoteSensing,
    author = {Weinmann, Martin and Weinmann, Michael},
     title = {Geospatial Computer Vision Based on Multi-Modal Data - How Valuable Is Shape Information for the
              Extraction of Semantic Information?},
   journal = {Remote Sensing},
    volume = {10},
    number = {1},
      year = {2018},
  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.}
}