scholarly journals High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

2016 ◽  
Author(s):  
G. C. Hulley ◽  
R. M. Duren ◽  
F. M. Hopkins ◽  
S. J. Hook ◽  
N. Vance ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Thermal Emission ◽  
High Spatial Resolution ◽  
Matched Filter ◽  
Study Data ◽  
High Spectral Resolution ◽  
Emission Sources ◽  
Source Attribution ◽  
Economic Sectors ◽  
Strong Emission

Abstract. Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5–12 µm), wide swath (1–2 km), and high spatial resolution (~2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid Clutter Matched Filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH4, H2S, NH3, NO2, and SO2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions.

scholarly journals High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

2016 ◽  
Vol 9 (5) ◽  
pp. 2393-2408 ◽  
Author(s):  
Glynn C. Hulley ◽  
Riley M. Duren ◽  
Francesca M. Hopkins ◽  
Simon J. Hook ◽  
Nick Vance ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Thermal Emission ◽  
High Spatial Resolution ◽  
Matched Filter ◽  
Study Data ◽  
High Spectral Resolution ◽  
Emission Sources ◽  
Source Attribution ◽  
Economic Sectors ◽  
Strong Emission

Abstract. Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1–2 km), and high spatial resolution (∼ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH4, H2S, NH3, NO2, and SO2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions.

High-spatial-resolution detection of new single-photon emission sources

SPIE Newsroom ◽  
2016 ◽  
Author(s):  
Luiz H. G. Tizei ◽  
Sophie Meuret ◽  
Romain Bourrellier ◽  
Anna Tararan ◽  
Odile Stéphan ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Single Photon ◽  
Photon Emission ◽  
Emission Sources ◽  
Single Photon Emission

scholarly journals FUSION OF HYPERSPECTRAL AND PANCHROMATIC DATA BY SPECTRAL UNMIXING IN THE REFLECTIVE DOMAIN

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 567-574
Author(s):  
Y. Constans ◽  
S. Fabre ◽  
M. Seymour ◽  
V. Crombez ◽  
X. Briottet ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Quality Criteria ◽  
Local Scale ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Urban Landscapes ◽  
Urban Scenes ◽  
Mixed Pixels ◽  
Global And Local

Abstract. Earth observation at the local scale implies working on images with both high spatial and spectral resolutions. As the latter cannot be simultaneously provided by current sensors, hyperspectral pansharpening methods combine images jointly acquired by two different sensors, a panchromatic one providing high spatial resolution, and a hyperspectral one providing high spectral resolution, to generate an image with both high spatial and spectral resolutions. The main limitation in the fusion process is in presence of mixed pixels, which particularly affect urban scenes, and where large fusion errors may occur. Recently, the Spatially Organized Spectral Unmixing (SOSU) method was developed to overcome this limitation, delivering good results on agricultural and peri-urban landscapes, which contain a limited number of mixed pixels. This article presents a new version of SOSU, adapted to urban landscapes. It is validated on a Toulouse (France) urban dataset at a 1.6 m spatial resolution acquired by the HySpex instrument from the 2012 UMBRA campaign. A performance assessment is established, following Wald’s protocol and using complementary quality criteria. Visual and numerical (at the global and local scales) analyses of this performance are also proposed. Notably, in the VNIR domain, around 51 % of the mixed pixels are better processed by the presented version of SOSU than by the method used as a reference. This ratio is improved regarding shadowed areas in the reflective (52 %) and VNIR (57 %) domains.

scholarly journals Lunar Image Fusion using Wavelet Transform

2013 ◽  
pp. 211-215
Author(s):  
Dr.Vani. K ◽  
Anto. A. Micheal
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Lunar Surface ◽  
High Spatial Resolution ◽  
Multispectral Image ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Mineral Mapping

This paper is an attempt to combine high resolution panchromatic lunar image with low resolution multispectral lunar image to produce a composite image using wavelet approach. There are many sensors that provide us image data about the lunar surface. The spatial resolution and spectral resolution is unique for each sensor, thereby resulting in limitation in extraction of information about the lunar surface. The high resolution panchromatic lunar image has high spatial resolution but low spectral resolution; the low resolution multispectral image has low spatial resolution but high spectral resolution. Extracting features such as craters, crater morphology, rilles and regolith surfaces with a low spatial resolution in multispectral image may not yield satisfactory results. A sensor which has high spatial resolution can provide better information when fused with the high spectral resolution. These fused image results pertain to enhanced crater mapping and mineral mapping in lunar surface. Since fusion using wavelet preserve spectral content needed for mineral mapping, image fusion has been done using wavelet approach.

scholarly journals Wide Swath and High Resolution Airborne HyperSpectral Imaging System and Flight Validation

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1667 ◽  
Author(s):  
Dong Zhang ◽  
Liyin Yuan ◽  
Shengwei Wang ◽  
Hongxuan Yu ◽  
Changxing Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
High Efficiency ◽  
High Spatial Resolution ◽  
Imaging System ◽  
High Sensitivity ◽  
Field Of View ◽  
High Spectral Resolution ◽  
Wide Swath

Wide Swath and High Resolution Airborne Pushbroom Hyperspectral Imager (WiSHiRaPHI) is the new-generation airborne hyperspectral imager instrument of China, aimed at acquiring accurate spectral curve of target on the ground with both high spatial resolution and high spectral resolution. The spectral sampling interval of WiSHiRaPHI is 2.4 nm and the spectral resolution is 3.5 nm (FWHM), integrating 256 channels coving from 400 nm to 1000 nm. The instrument has a 40-degree field of view (FOV), 0.125 mrad instantaneous field of view (IFOV) and can work in high spectral resolution mode, high spatial resolution mode and high sensitivity mode for different applications, which can adapt to the Velocity to Height Ratio (VHR) lower than 0.04. The integration has been finished, and several airborne flight validation experiments have been conducted. The results showed the system’s excellent performance and high efficiency.

scholarly journals Spatially-resolved high-spectral resolution observations of the red supergiant Betelgeuse

2009 ◽  
Vol 5 (H15) ◽  
pp. 552-552
Author(s):  
K. Ohnaka
Keyword(s):  
Mass Loss ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
High Spectral Resolution ◽  
Stellar Disk ◽  
Loss Mechanism ◽  
Spatially Resolved ◽  
Outer Atmosphere ◽  
Limb Darkening ◽  
Co Gas

Red supergiants (RSGs) experience slow, intensive mass loss up to 10−4M⊙ yr−1. Despite its importance not only in stellar evolution but also in the chemical enrichment of the interstellar matter, the mass loss mechanism in RSGs is not well understood. A better understanding of the outer atmosphere of RSGs is a key to unraveling the mass-loss mechanism in these stars. High spatial resolution observations in IR molecular lines are very effective for probing the physical properties of the inhomogeneous outer atmosphere. We observed the prototypical RSG Betelgeuse (M1-2Ia–Ibe) in the CO first overtone lines with the spectro-interferometric instrument AMBER at the ESO's Very Large Telescope Interferometer (VLTI) using baselines of 16, 32, and 48 m. Details of the observations and the modeling are described in Ohnaka et al. (2009). The high-spectral (R = 4800–12000) and high-spatial resolution (9 mas) provided with AMBER allowed us to study inhomogeneities seen in the individual CO first overtone lines. Our AMBER observations represent the highest spatial resolution achieved for Betelgeuse, corresponding to five resolution elements over its stellar disk. The AMBER visibilities and closure phases in the K-band continuum can be reasonably fitted by a uniform disk with a diameter of 43.19 ± 0.03 mas or a limb-darkening disk with 43.56 ± 0.06 mas and a limb-darkening parameter of (1.2 ± 0.07) × 10−1. On the other hand, our AMBER data in the CO lines reveal salient inhomogeneous structures. The visibilities and phases (closure phases, as well as differential phases representing asymmetry in lines with respect to the continuum) measured within the CO lines show that the blue and red wings originate in spatially distinct regions over the stellar disk, indicating an inhomogeneous velocity field that makes the star appear different in the blue and red wings. Our AMBER data in the CO lines can be roughly explained by a simple model, in which a patch of CO gas is moving outward or inward with velocities of 10–15 km s−1, while the CO gas in the remaining region in the atmosphere is moving in the opposite direction at the same velocities. These velocities compare favorably with the macroturbulent velocities of 10–20 km s−1 derived by spectroscopic analyses. Also, the AMBER data are consistent with the presence of warm molecular layers (so-called MOLsphere) extending to ~1.4–1.5 R* with a CO column density of ~ 1 × 1020 cm−2. However, the present data are insufficient to constrain the surface pattern uniquely or to reconstruct an image. Our AMBER observations of Betelgeuse are the first spatially resolved study of the macroturbulence in a stellar atmosphere (photosphere and possibly MOLsphere as well) other than the Sun. The spatially resolved CO gas motion is likely to be related to convective motion in the upper atmosphere or intermittent mass ejections in clumps or arcs.

scholarly journals Self-Dictionary Regression for Hyperspectral Image Super-Resolution

2018 ◽  
Vol 10 (10) ◽  
pp. 1574 ◽  
Author(s):  
Dongsheng Gao ◽  
Zhentao Hu ◽  
Renzhen Ye
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Cross Correlation ◽  
Hyperspectral Image ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Image Super Resolution

Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution. It is difficult for sensors to acquire images with high-spatial-resolution and high-spectral-resolution simultaneously. Hyperspectral image super-resolution tries to enhance the spatial resolution of HSI by software techniques. In recent years, various methods have been proposed to fuse HSI and multispectral image (MSI) from an unmixing or a spectral dictionary perspective. However, these methods extract the spectral information from each image individually, and therefore ignore the cross-correlation between the observed HSI and MSI. It is difficult to achieve high-spatial-resolution while preserving the spatial-spectral consistency between low-resolution HSI and high-resolution HSI. In this paper, a self-dictionary regression based method is proposed to utilize cross-correlation between the observed HSI and MSI. Both the observed low-resolution HSI and MSI are simultaneously considered to estimate the endmember dictionary and the abundance code. To preserve the spectral consistency, the endmember dictionary is extracted by performing a common sparse basis selection on the concatenation of observed HSI and MSI. Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI. Extensive experiments on three datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

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