Following the public release of the Spectral and Polarimetric Imagery Collection Experiment (SPICE) dataset, a persistent imaging experiment dataset collected by the Army Research Laboratory (ARL), the data were analyzed and materials in the scene characterized temporally and spatially using radiance data. The noise equivalent spectral radiance provided by the sensor manufacturer was compared with instrument noise calculated from in-scene information, and found to be comparable given differences in laboratory setting and real-life conditions. The processed dataset have regular "inconsistent cubes," specifically for data collected immediately after blackbody measurements, which were automatically executed approximately at each hour mark. Omitting these erroneous data, three target detection algorithms (adaptive coherent/cosine estimator, spectral angle mapper, and spectral matched filter) were tested on the temporal data using two target spectra (noon and midnight). The spectral matched filter produced the best detection rate for both noon and midnight target spectra for a 24-hrs period.
Nirmalan Jeganathan, John Kerekes, and Dalton Rosario, "Characterizing the temporal and spatial variability of longwave infrared spectral images of targets and backgrounds," Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980K (Presented at SPIE Defense + Security: April 11, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2262506.
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