Multimodal Data Fusion via Entropy Minimization

Technical Report · Sun Mar 01 04:00:00 EST 2020

DOI:https://doi.org/10.2172/1614682· OSTI ID:1614682

Linville, Lisa M. ^[1]; Michalenko, Joshua James ^[1]; Anderson, Dylan Zachary ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

The use of gradient-based data-driven models to solve a range of real-world remote sensing problems can in practice be limited by the uniformity of available data. Use of data from disparate sensor types, resolutions, and qualities typically requires compromises based on assumptions that are made prior to model training and may not necessarily be optimal given over-arching objectives. For example, while deep neural networks (NNs) are state-of-the-art in a variety of target detection problems, training them typically requires either limiting the training data to a subset over which uniformity can be enforced or training independent models which subsequently require additional score fusion. The method we introduce here seeks to leverage the benefits of both approaches by allowing correlated inputs from different data sources to co-influence preferred model solutions, while maintaining flexibility over missing and mismatching data. In this work we propose a new data fusion technique for gradient updated models based on entropy minimization and experimentally validate it on a hyperspectral target detection dataset. We demonstrate superior performance compared to currently available techniques using a range of realistic data scenarios, where available data has limited spacial overlap and resolution.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1614682
• Report Number(s): SAND--2020-3703; 685236
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING