<p>The use of airborne remote sensing techniques for geological mapping offers many benefits as it allows coverage of large areas in a very efficient way. &#160;While hyperspectral imaging from airborne/spaceborne platforms is now a well-established method applied to resolve many geological problems, it has mostly been developed only in the Visible-Near Infrared (VNIR, 0.4&#8211;1.0 mm) and Shortwave Infrared (SWIR, 1.0&#8211;2.5 mm) regions of the electromagnetic spectrum. However, the reflectance spectral features measured in the VNIR and SWIR spectral ranges are generally overtones and combination bands from fundamental absorption bands at longer wavelengths, such as in the Longwave Infrared (LWIR, 8&#8211;12 mm). The single absorption bands in the VNIR and SWIR spectral ranges are often very closely spaced so that the reflectance features measured by common spectrometers in this spectral region are typically broad and/or suffer from strong overlapping, which raises selectivity issues for mineral identification in some cases.</p><p>The inherent self-emission associated with LWIR under ambient conditions allows airborne mineral mapping in various weather (cloudy, partly cloudy or clear sky) and illumination (day or night) conditions. For this reason, LWIR often refers to the thermal infrared (TIR) spectral range. Solid targets such as minerals not only emit but also reflect TIR radiation. Since the two phenomena occur simultaneously, they end-up mixed in the radiance measured at the sensor level. The spectral features observed in a TIR spectrum of the sky and the atmosphere mostly correspond to ozone, water&#160; vapor, carbon dioxide, methane and nitrous oxide with pretty sharp and narrow features compared with the infrared signature of solid materials such as minerals. The sharp spectral features of atmospheric gases are mixed up with broad minerals features in the collected geological mapping data, to unveil the spectral features associated with minerals from TIR measurements, the respective contributions of self-emission and reflection in the measurement must be &#171;unmixed&#187; and the atmospheric contributions must be compensated. This procedure refers to temperature-emissivity separation (TES). Therefore, to achieve an efficient TES and atmospheric compensation, the collection time and conditions of LWIR airborne hyperspectral data is of importance. Data of a flight mission in Southern Spain collected systematically at different times of the day (morning, mid-day and night) and in different altitudes using the Telops Hyper-Cam airborne system, a passive TIR hyperspectral sensor based on Fourier transform spectroscopy, were analyzed. TES was carried out on the hyperspectral data using<strong> two</strong> different approaches: a) Telops Reveal FLAASH IR software and b) DIMAP In-scene atmospheric compensation algorithm in order to retrieve thermodynamic temperature map and spectral emissivity data. Spectral analysis of the emissivity data with different mineral mapping methods based on commercial spectral libraries was used to compare results obtained during the different flight times and altitudes using the two post-processing methodologies. The results are discussed in the light of optimizing LWIR-based airborne operations in time and altitude to achieve best results for routine field mineral mapping applications such as in mining, soil science or archaeology, where the spatial analysis of mineral and chemical distribution is essential</p>
Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach
