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This paper presents a novel method for atmospheric transmittance-temperature-emissivity separation (AT2ES) using online midwave infrared hyperspectral images. Conventionally, temperature and emissivity separation (TES) is a well-known problem in the remote sensing domain. However, previous approaches use the atmospheric correction process before TE...
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Hyperspectral Imaging is an essential technique to deep explore surfaces, which provides more detailed information than the single point spectroscopy. Over the past decade there has been many devices which were invented for the Hyperspectral Image (HSI) acquisition .The complexity lies in the image dataset dimension, as the HSI data sets area diffi...
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Radiative cooling textiles hold promise for achieving personal thermal comfort under increasing global temperature. However, urban areas have heat island effects that largely diminish the effectiveness of cooling textiles as wearable fabrics because they absorb emitted radiation from the ground and nearby buildings. We developed a mid-infrared spectrally selective hierarchical fabric (SSHF) with emissivity greatly dominant in the atmospheric transmission window through molecular design, minimizing the net heat gain from the surroundings. The SSHF features a high solar spectrum reflectivity of 0.97 owing to strong Mie scattering from the nano-micro hybrid fibrous structure. The SSHF is 2.3°C cooler than a solar-reflecting broadband emitter when placed vertically in simulated outdoor urban scenarios during the day and also has excellent wearable properties.
The climatic fluctuations in northern China exhibit remarkable variability, making it imperative to harness the power of MODIS data for conducting comprehensive investigations into the influences of desertification, desert expansion, and snow and ice melting phenomena. Consequently, the rigorous evaluation of MODIS land surface temperature (LST) and land surface emissivity (LSE) products takes on a momentous role, as this provides an essential means to ensure data accuracy, thereby instilling confidence in the robustness of scientific analyses. In this study, a high-resolution hyperspectral imaging instrument was utilized to measure mid-wave hyperspectral images of grasslands and deserts in the northwest plateau region of Qinghai, China. The measured data were processed in order to remove the effects of sensor noise, atmospheric radiation, transmission attenuation, and scattering caused by sunlight and atmospheric radiation. Inversion of the temperature field and spectral emissivity was performed on the measured data. The inverted data were compared and validated against MODIS land surface temperature and emissivity products. The validation results showed that the absolute errors of emissivity of grassland backgrounds provided by MCD11C1 in the three mid-wave infrared bands (3.66–3.840 μm, 3.929–3.989 μm, and 4.010–4.080 μm) were 0.0376, 0.0191, and 0.0429, with relative errors of 3.9%, 2.1%, and 4.8%, respectively. For desert backgrounds, the absolute errors of emissivity were 0.0057, 0.0458, and 0.0412, with relative errors of 0.4%, 4.9%, and 3.9%, respectively. The relative errors for each channel were all within 5%. Regarding the temperature data products, compared to the inverted temperatures of the deserts and grasslands, the remote sensing temperatures provided by MOD11L2 had absolute errors of ±2.3 K and ±4.1 K, with relative errors of 1.4% and 0.7%, respectively. The relative errors for the temperature products were all within 2%.
Detecting remote man-made objects in mid-infrared band is an important problem in aerial surveillance. Spectral polarization in the midwave infrared (MWIR) hyperspectral band can be a feasible solution but it has been studied less than the visible light band. The conventional degree of linear polarization (DoLP) metric cannot provide physical spectral polarization information because it is defined as a energy-normalized ratio, which leads to incorrect polarization degree in low signal-to-noise ratio in MWIR band. This paper presents a novel physical metric called apparent spectral polarization radiant intensity (AS-polRI) for MWIR spectral polarization in man-made object detection. AS-polRI, measured in Watts per steradian (W/sr), is obtained by integrating the difference between parallel spectral radiance and perpendicular spectral radiance assuming a 1 m2 object area. The proposed AS-polRI is compared with the degree of linear polarization (DoLP) in terms of man-made object detection. Experimental results for various outdoor scenarios and materials validate the usefulness of the proposed AS-polRI.
Measuring accurate surface temperature using a long-wave infrared camera and a non-contact thermometer, is very difficult due to variables such as atmospheric transmittance, emissivity, and influences from the environment such as atmosphere, sun, and dust. Conventional approaches use geometric correction or atmospheric transmittance modeling for temperature correction. However, these approaches have limitations in finding an accurate temperature because it is difficult to fully model a physical phenomenon. In this paper, a new temperature estimation method using distance information of LiDAR and digital count of long-wave infrared camera is proposed. The proposed method estimates the temperature by redefining the mapping function between radiation and digital count by distance. Using the proposed method, if the digital count is measured at a specific distance, accurate temperature can be estimated through the redefined Radiation-Digital count mapping function at a specific distance. The most important property of proposed method is that complex physical modeling is complemented by mapping function of specific distances. In addition, digital counts that change according to the distance at the same temperature required for the mapping function are obtained through linear interpolation using digital count of specific distances. Experimental results using a blackbody, long-wave infrared camera and LiDAR verify that the proposed method estimates the precise temperature. In addition, through experiments on humans, it shows the possibility of accurate body temperature measurement through fusion of long-wave infrared cameras and LiDAR in the future. However, as a limitation, a new calibration is required when the temperature and humidity of the atmosphere change.
