Partially supervised detection in hyperspectral imagery

Daniel C. Heinz; Thomas Bahr; David Streutker; Greg Terrie; Michael Ingram

doi:10.1117/12.2616301

31 May 2022 Partially supervised detection in hyperspectral imagery

Daniel C. Heinz, Thomas Bahr, David Streutker, Greg Terrie, Michael Ingram

Proceedings Volume 12094, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII; 120940F (2022) https://doi.org/10.1117/12.2616301
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

In this paper, performance of state-of-the-art partially supervised detection algorithms are compared. It can be difficult to characterize the performance of detection algorithms using field data, especially at the subpixel level, due to limited ground truth. Fortunately, the SpecTIR Hyperspectral Airborne Experiment (SHARE) 2012 contains multiple sets of targets for testing detection algorithms with excellent ground truth. In this paper we utilize field spectra acquired for six targets to evaluate the performance of multiple detection algorithms. Each method is initialized with a single field spectra target signature, and detection performance of each method is separately assessed for each of the six targets. Detailed evaluation of these detection methods on the SHARE 2012 hyperspectral data is provided.

Conference Presentation

Citation Download Citation

Daniel C. Heinz, Thomas Bahr, David Streutker, Greg Terrie, and Michael Ingram "Partially supervised detection in hyperspectral imagery", Proc. SPIE 12094, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII, 120940F (31 May 2022); https://doi.org/10.1117/12.2616301

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