Correlation between images in the long-wave infrared and short-wave infrared of natural ground terrain

1991 ◽  
Author(s):  
Eyal Agassi ◽  
Kalman Wilner ◽  
Nissim Ben-Yosef
Keyword(s):  
Short Wave ◽  
Long Wave ◽  
Short Wave Infrared ◽  
Long Wave Infrared

scholarly journals EFFECTS OF GLOBAL WARMING

2015 ◽  
Vol 3 (9SE) ◽  
pp. 1-2
Author(s):  
Basanti Jain
Keyword(s):  
Global Warming ◽  
Short Wave ◽  
Average Temperature ◽  
The Past ◽  
The Earth ◽  
Long Wave ◽  
The World ◽  
Abnormal Increase ◽  
Ionizing Radiations ◽  
Long Wave Infrared

The abnormal increase in the concentration of the greenhouse gases is resulting in higher temperatures. We call this effect is global warming. The average temperature around the world has increased about 1'c over 140 years, 75% of this has risen just over the past 30 years. The solar radiation, as it reaches the earth, produces "greenhouse effect" in the atmosphere. The thick atmospheric layers over the earth behaves as a glass surface, as it permits short wave radiations from coming in, but checks the outgoing long wave ones. As a result, gradually the atmosphere gets heated up during the day as well as night. If such an effect were not there in the atmosphere the ultraviolet, infrared and other ionizing radiations would have also entered our atmosphere and the very existence of life would have been endangered. The ozone layer shields the earth from the sun's harmful ultraviolet radiations. The warm earth emits long wave (infrared)   radiations, which is partly absorbed by the green house gaseous blanket. This atmospheric blanket raises the earth’s temperature.

Design and Fabrication of Short-Wave /Long-Wave Infrared Dichroic Beam Splitter

2010 ◽  
Vol 30 (2) ◽  
pp. 597-601
Author(s):  
章岳光 Zhang Yueguang ◽  
王颖 Wang Ying ◽  
孙雪铮 Sun Xuezheng ◽  
沈伟东 Shen Weidong ◽  
刘旭 Liu Xu ◽  
...  
Keyword(s):  
Beam Splitter ◽  
Short Wave ◽  
Long Wave ◽  
Long Wave Infrared

scholarly journals Geometric- and Optimization-Based Registration Methods for Long-Wave Infrared Hyperspectral Images

2021 ◽  
Vol 13 (13) ◽  
pp. 2465
Author(s):  
Alper Koz ◽  
Ufuk Efe
Keyword(s):  
Hyperspectral Image ◽  
Three Dimensional ◽  
Average Energy ◽  
Short Wave ◽  
Hyperspectral Images ◽  
Spectral Radiance ◽  
Global Registration ◽  
Long Wave ◽  
Image Pixels ◽  
Long Wave Infrared

Registration of long-wave infrared (LWIR) hyperspectral images with their thermal and emissivity components has until now received comparatively less attention with respect to the visible near and short wave infrared hyperspectral images. In this paper, the registration of LWIR hyperspectral images is investigated to enhance applications of LWIR images such as change detection, temperature and emissivity separation, and target detection. The proposed approach first searches for the best features of hyperspectral image pixels for extraction and matching in the LWIR range and then performs a global registration over two-dimensional maps of three-dimensional hyperspectral cubes. The performances of temperature and emissivity features in the thermal domain along with the average energy and principal components of spectral radiance are investigated. The global registration performed over whole 2D maps is further improved by blockwise local refinements. Among the two proposed approaches, the geometric refinement seeks the best keypoint combination in the neighborhood of each block to estimate the transformation for that block. The alternative optimization-based refinement iteratively finds the best transformation by maximizing the similarity of the reference and transformed blocks. The possible blocking artifacts due to blockwise mapping are finally eliminated by pixelwise refinement. The experiments are evaluated with respect to the (i) utilized similarity metrics in the LWIR range between transformed and reference blocks, (ii) proposed geometric- and optimization-based methods, and (iii) image pairs captured on the same and different days. The better performance of the proposed approach compared to manual, GPU-IMU-based, and state-of-the-art image registration methods is verified.

scholarly journals On the possibility of using thermal vision cameras for observations of meteors and other atmospheric formations in long wave infrared

2019 ◽  
pp. 33-38
Author(s):  
P. Kozak ◽  
L. Kozak
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Ultra Violet ◽  
Short Wave ◽  
Infrared Observations ◽  
Long Wave ◽  
Thermal Vision ◽  
And Inversion ◽  
The Given ◽  
Long Wave Infrared

The characteristics of the modern low-cost thermal vision cameras for possible observations of meteors and other atmospheric formations in long wave infrared spectrum range of 8-14 μm are investigated. An overview of meteor observations in non-traditional spectrum ranges: ultra-violet, near infrared, short wave, mid wave, and long wave infrared is done. A short description of the modern instruments for infrared observations is presented. By the example of a modern inexpensive model of thermal vision camera of the lower price segment there are presented results of test observations of clouds, possible atmospheric bolide tails and inversion t tracks of airplanes, meteors, and thunderstorm discharges. A short analysis of technical characteristics of the selected model, and corresponding software is given, the merits and demerits of the given type of observational instruments are analyzed as well. The conclusion for outlook of using in the future the thermal vision cameras in meteor astronomy and geophysics is done.

p-on-n HgCdTe Infrared Focal-Plane Arrays: From Short-Wave to Very-Long-Wave Infrared

2013 ◽  
Vol 43 (3) ◽  
pp. 802-807 ◽  
Author(s):  
L. Mollard ◽  
G. Bourgeois ◽  
C. Lobre ◽  
S. Gout ◽  
S. Viollet-Bosson ◽  
...  
Keyword(s):  
Focal Plane ◽  
Short Wave ◽  
Focal Plane Arrays ◽  
Infrared Focal Plane Arrays ◽  
Long Wave ◽  
Long Wave Infrared

scholarly journals Quantitative Mineral Mapping of Drill Core Surfaces II: Long-Wave Infrared Mineral Characterization Using μXRF and Machine Learning

2020 ◽  
Author(s):  
Rocky D. Barker ◽  
Shaun L.L. Barker ◽  
Matthew J. Cracknell ◽  
Elizabeth D. Stock ◽  
Geoffrey Holmes
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Short Wave ◽  
Spectral Unmixing ◽  
Training Data ◽  
Data Set ◽  
X Ray ◽  
Long Wave ◽  
Mean Square Errors ◽  
Long Wave Infrared

Abstract Long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine learning approach to predict mineral species and abundances. In this study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analyzed by LWIR and micro-X-ray fluorescence (μXRF). Linear programming-derived mineral abundances from quantified μXRF data were used as training data to construct a series of Random Forest regression models. The LWIR Random Forest models produced mineral proportion estimates with root mean square errors of 1.17 to 6.75% (model predictions) and 1.06 to 6.19% (compared to quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. These results are comparable to the error of proportion estimates from linear spectral deconvolution (±7–15%), a commonly used spectral unmixing technique. Having a mineralogical and chemical training data set makes it possible to identify and quantify mineralogy and provides a more robust and meaningful LWIR spectral interpretation than current methods of utilizing a spectral library or spectral end-member extraction. Using the method presented here, LWIR spectroscopy can be used to overcome the limitations inherent with the use of short-wave infrared (SWIR) in fine-grained, low reflectance rocks. This new approach can be applied to any deposit type, improving the accuracy and speed of infrared data interpretation.

Sign in / Sign up

Export Citation Format

Share Document