Evaluation of hyperspectral data for deep learning model performance

Samantha J. Butler; Stanton R. Price; Samantha S. Carley; Haley B. Land; Steven R. Price

doi:10.1117/12.3014030

7 June 2024 Evaluation of hyperspectral data for deep learning model performance

Samantha J. Butler, Stanton R. Price, Samantha S. Carley, Haley B. Land, Steven R. Price

Proceedings Volume 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX; 130310R (2024) https://doi.org/10.1117/12.3014030
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

The utilization of hyperspectral image data has contributed to improved performance of machine learning tasks by providing spectrally rich information that other more common sensor data lacks. An issue that can arise when using hyperspectral imagery is that it can often be computationally burdensome to collect and process. This study seeks to investigate the incorporation of hyperspectral image data collected on a co-aligned VNIR-SWIR sensor for the purpose of hyperspectral image classification. In which, the evaluation is focused on investigating the distinct effects pertaining to the VNIR data, to the SWIR data, and to the combination of the two data types with regards to hyperspectral image classification performance on vehicles. The experiments were run on data collected by the US Army Corps of Engineers Research and Development Center.

Citation Download Citation

Samantha J. Butler, Stanton R. Price, Samantha S. Carley, Haley B. Land, and Steven R. Price "Evaluation of hyperspectral data for deep learning model performance", Proc. SPIE 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, 130310R (7 June 2024); https://doi.org/10.1117/12.3014030

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