Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest

By C. Debes, A. Merentitis, R. Heremans, Jürgen T. Hahn, Nikolaos Frangiadakis, Tim van Kasteren, Wenzi Liao, Rik Bellens, Aleksandra Pizurica, S. Gautama and 4 more. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014

619 citations recorded by the source corpus.

The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.

Subfields: Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/79eb61c015c02fec633b4b5fd33fb3855ecc0b9f

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Citations in the corpus

Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, background): “optical and LiDAR data has become a hot research topic in recent years [83].”
Deep learning classifiers for hyperspectral imaging: A review (2019, background): “In this sense, the UH scene provides an interesting benchmark dataset, which was first presented at the IEEE Geoscience and Remote Sensing Society (GRSS) Image Analysis and Data Fusion Technical Committee during the 2013 Data Fusion Contest (DFC) (Debes et al., 2014).”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “Graph-based fusion methods couple data fusion and DR in a unified framework for classification [31], [32].”
Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017, methodology): “7) CASI University of Houston: This data set was used in the 2013 IEEE GRSS data fusion contest [69].”
Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest (2019, methodology): “It includes change detection [8], large-scale fusion of optical, SAR, and LiDAR data [9], classiﬁcation [4], [5], and large-scale classiﬁcation and domain adaptation [10].”
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs (2020, methodology): “It is worth noting that we use the standard sets of training and test samples which makes our results fully comparable with several works such as [7] and [8].”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, background): “Examples include multi-sensor [9], HMI [10] and remote sensing [42] applications.”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, methodology): “We next discuss [136], [138], [142], and [143] in more detail.”
Single-Source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification (2022, neutral): “1) Houston Dataset: The dataset includes Houston 2013 [44] and Houston 2018 [45] scenes, which were obtained by different sensors on the University of Houston campus and its vicinity in different years.”
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, methodology): “HOUSTON UNIVERSITY 2013 This data set was acquired on 23 June 2012 by the Compact Airborne Spectrographic Imager (CASI) over the campus of the University of Houston and the neighboring urban area [132].”
UAVid: A semantic segmentation dataset for UAV imagery (2018, background): “Houston dataset [13] provides hyperspectral images (HSIs) and Light Detection And Ranging (LiDAR) data, both of which have 2.”