Merging High Spectral Resolution Sounder Data with High Spatial Resolution Imager Data to infer Global Cloud Cover Properties

2011 ◽  
Author(s):  
W. Paul Menzel ◽  
Elisabeth Weisz ◽  
Richard Frey ◽  
Eva Borbas
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
Cloud Cover ◽  
High Spatial Resolution ◽  
High Spectral Resolution

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 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.

scholarly journals Investigation of circumstellar shells by mid-infrared heterodyne spectroscopy

1987 ◽  
Vol 122 ◽  
pp. 553-554
Author(s):  
U. Schrey ◽  
S. Drapatz ◽  
H.U. Käufl ◽  
H. Rothermel ◽  
S. K. Ghosh
Keyword(s):  
Penetration Depth ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
High Spatial Resolution ◽  
High Spectral Resolution ◽  
Mid Infrared ◽  
Heterodyne Spectroscopy ◽  
High Penetration ◽  
Circumstellar Shells

Heterodyne spectroscopy at 11 μm combines high spectral resolution (λ/Δ λ ~106), high spatial resolution (< 1 arcsec at 3 m telescopes) and high penetration depth. Therefore, it seems promising to use it also for the investigation of bright circumstellar atmospheres.

scholarly journals Fusion of Airborne Hyperspectral and WorldView2 Multispectral Images for Detailed Urban Land Cover Classification A case Study of Kuala Lumpur, Malaysia

2018 ◽  
Vol 7 (4.37) ◽  
pp. 202 ◽  
Author(s):  
Abbas Mohammed Noori ◽  
Sumaya Falih Hasan ◽  
Qayssar Mahmood Ajaj ◽  
Mustafa Ridha Mezaal ◽  
Helmi Z. M. Shafri ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
Urban Areas ◽  
High Spatial Resolution ◽  
High Spectral Resolution ◽  
Support Vector ◽  
Kuala Lumpur ◽  
High Quality ◽  
Urban Land Cover ◽  
Fused Image

Detecting the features of urban areas in detail requires very high spatial and spectral resolution in images. Hyperspectral sensors usually offer high spectral resolution images with a low spatial resolution. By contrast, multispectral sensors produce high spatial resolution images with a poor spectral resolution. Therefore, numerous fusion algorithms and techniques have been proposed in recent years to obtain high-quality images with improved spatial and spectral resolutions by sensibly combining the data acquired for the same scene. This work aims to exploit the extracted information from images in an effective way. To achieve this objective, a new algorithm based on transformation was developed. This algorithm primarily depends on the Gram–Schmidt process for fusing images, removing distortions, and improving the appearance of images. Images are first fused by using the Gram–Schmidt pansharpening method. The obtained fused image is utilized in the classification process in different areas by using support vector machine (SVM). The classification result is evaluated using a matrix of errors. The overall accuracy produced from the hyperspectral, multispectral and fused images was 72.33%, 82.83%, and 89.34%, respectively. Results showed that the developed algorithm improved the image enhancement and image fusion. Moreover, the developed algorithm has the ability to produce an imaging product with high spatial resolution and high-quality spectral data. 

scholarly journals Seasonal cycle of cloud cover analyzed using Meteosat images

1998 ◽  
Vol 16 (3) ◽  
pp. 331-341 ◽  
Author(s):  
J. Massons ◽  
D. Domingo ◽  
J. Lorente
Keyword(s):  
Spatial Resolution ◽  
Seasonal Cycle ◽  
Cloud Cover ◽  
High Spatial Resolution ◽  
Detection Method ◽  
Cloud Amount ◽  
Cloud Detection ◽  
Total Cloud Amount ◽  
Cloud Parameters ◽  
Cloud Properties

Abstract. A cloud-detection method was used to retrieve cloudy pixels from Meteosat images. High spatial resolution (one pixel), monthly averaged cloud-cover distribution was obtained for a 1-year period. The seasonal cycle of cloud amount was analyzed. Cloud parameters obtained include the total cloud amount and the percentage of occurrence of clouds at three altitudes. Hourly variations of cloud cover are also analyzed. Cloud properties determined are coherent with those obtained in previous studies.Key words. Cloud cover · Meteosat

scholarly journals Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

2020 ◽  
Vol 12 (6) ◽  
pp. 1009
Author(s):  
Xiaoxiao Feng ◽  
Luxiao He ◽  
Qimin Cheng ◽  
Xiaoyi Long ◽  
Yuxin Yuan
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Spatial Information ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Remote Sensing Image Fusion ◽  
Fusion Methods ◽  
Invariant Regions ◽  
The Difference

Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually.

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.

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