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

Martin Weinmann,Michael Weinmann

doi:10.3390/rs10010002

Martin Weinmann, Michael Weinmann

Open Access

https://doi.org/10.3390/rs10010002

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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.

Highlights

Geospatial computer vision deals with the acquisition, exploration, and analysis of our natural and/or man-made environments
We evaluate the performance of our framework using the MUUFL Gulfport Hyperspectral and
We have addressed scene analysis based on multi-modal data

Summary

Introduction

Geospatial computer vision deals with the acquisition, exploration, and analysis of our natural and/or man-made environments. The captured data may be represented in various forms such as imagery or point clouds, and acquired spatial (i.e., geometric), spectral, and radiometric data might be given at different resolutions The use of these individual types of geospatial data as well as different combinations is of particular interest for the acquisition and analysis of urban scenes which provide a rich diversity of both natural and man-made objects. A ground-based acquisition of urban scenes nowadays typically relies on the use of mobile laser scanning (MLS) systems [1,2,3,4] or terrestrial laser scanning (TLS) systems [5,6] While this delivers a dense sampling of object surfaces, achieving a full coverage of the considered scene is challenging, as the acquisition system has to be moved through the scene either continuously (in the case of an MLS system) or with relatively small displacements of a few meters (in the case of a TLS system) to handle otherwise occluded parts of the scene. The sampling of object surfaces is not that dense, Remote Sens. 2018, 10, 2; doi:10.3390/rs10010002 www.mdpi.com/journal/remotesensing

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