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Landing zone identification for autonomous UAV applications using fused hyperspectral imagery and LIDAR point clouds
Author(s): Sarah Lane; Zsolt Kira; Ryan James; Domenic Carr; Grady Tuell
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Paper Abstract

Multi-modal data fusion for situational awareness is of interest because fusion of data can provide more information than the individual modalities alone. However, many questions remain, including what data is beneficial, what algorithms work the best or are fastest, and where in the processing pipeline should data be fused? In this paper, we explore some of these questions through a processing pipeline designed for multi-modal data fusion in an autonomous UAV landing scenario. In this paper, we assess landing zone identification methods using two data modalities: hyperspectral imagery and LIDAR point clouds. Using hyperspectral image and LIDAR data from two datasets of Maui and a university campus, we assess the accuracies of different landing zone identification methods, compare rule-based and machine learning based classifications, and show that depending on the dataset, fusion does not always increase performance. However, we show that machine learning methods can be used to ascertain the usefulness of individual modalities and their resulting attributes when used to perform classification.

Paper Details

Date Published: 8 May 2018
PDF: 12 pages
Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 106440D (8 May 2018); doi: 10.1117/12.2305136
Show Author Affiliations
Sarah Lane, Georgia Tech Research Institute (United States)
Zsolt Kira, Georgia Tech Research Institute (United States)
Ryan James, Georgia Tech Research Institute (United States)
Domenic Carr, Georgia Tech Research Institute (United States)
Grady Tuell, 3D Ideas, LLC (United States)


Published in SPIE Proceedings Vol. 10644:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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