Abstract
Our first observations using the longwave infrared (LWIR) hyperspectral data subset of the Spectral and Polarimetric Imagery Collection Experiment (SPICE) database are summarized in this paper, focusing on the inherent challenges associated with using this sensing modality for the purpose of object pattern recognition. Emphases are also put on data quality, qualitative validation of expected atmospheric spectral features, and qualitative comparison against another dataset of the same site using a different LWIR hyperspectral sensor. SPICE is a collaborative effort between the Army Research Laboratory, U.S. Army Armament RDEC, and more recently the Air Force Institute of Technology. It focuses on the collection and exploitation of longwave and midwave infrared (LWIR and MWIR) hyperspectral and polarimetric imagery. We concluded from this work that the quality of SPICE hyperspectral LWIR data is categorically comparable to other datasets recorded by a different sensor of similar specs; and adequate for algorithm research, given the scope of SPICE. The scope was to conduct a long-term infrared data collection of the same site with targets, using both sensing modalities, under various weather and non-ideal conditions. Then use the vast dataset and associated ground truth information to assess performance of the state of the art algorithms, while determining performance degradation sources. The expectation is that results from these assessments will spur new algorithmic ideas with the potential to augment pattern recognition performance in remote sensing applications. Over time, we are confident the SPICE database will prove to be an asset to the wide open remote sensing community.
