Results using a neural network classifier for LWIR HSI target detection are presented. Detection performance using two different single layer networks with 10 and 20 nodes in the hidden layer are presented. Additionally, a two-layer network with 20 nodes in the first layer and 10 in the second is also tested. The larger networks generally performbetter, as expected, but this is not necessarily true for all targets. Neural networks significantly outperformACE across several detection metric for eight different targets. Even when ACE is applied to atmospherically-corrected emissivity datacubes, neural networks applied without atmospheric compensation still performbetter. Finally, it is found that incorporating atmospheric metadata into the network inputs can improve detection performance but is again target-dependent.
Jacob A. Martin, "Target detection using artificial neural networks on LWIR hyperspectral imagery," Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 1064402 (Presented at SPIE Defense + Security: April 17, 2018; Published: 8 May 2018); https://doi.org/10.1117/12.2303505.
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