Signal-to-noise ratio reduction due to image smear concerning spaceborne imaging spectrometers for remote sensing of the Earth

1998 ◽  
Author(s):  
Jens Nieke ◽  
M. Solbrig ◽  
Andreas Neumann
Keyword(s):  
Remote Sensing ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
The Earth ◽  
Noise Ratio ◽  
Ratio Reduction

Mass Sensitivity or Gravimetric Satellites

2020 ◽  
Author(s):  
Robert Spero
Keyword(s):  
Signal To Noise Ratio ◽  
Satellite Tracking ◽  
Point Mass ◽  
Laser Ranging ◽  
Signal To Noise ◽  
Orbital Parameters ◽  
The Earth ◽  
Noise Ratio ◽  
Optimal Signal ◽  
Instrument Sensitivity

<p class="p1">A point mass on the surface of the Earth gives the highest frequency content for orbiting gravimetry, with<span class="Apple-converted-space">  </span>the maximum frequency for gradiometers or satellite-to-satellite tracking determined by orbital altitude.  Frequency-domain expressions are found for<span class="Apple-converted-space">  </span>measurements of a point-like source on the surface of the Earth.<span class="Apple-converted-space">  </span>The response of orbiting gradiometers such as GOCE and satellite-to-satellite tracking missions such as GRACE-FO are compared. The optimal signal-to-noise ratio as a function<span class="Apple-converted-space">  </span>of noise in the measurement apparatus is computed, and from that the minimum detectable mass is inferred. The point mass magnitude that gives signal-to-noise ratio = 3 is for GOCE<span class="Apple-converted-space">  </span>M_3=200 Gton and<span class="Apple-converted-space">  </span>for the laser ranging interferometer measurement on GRACE-FO<span class="Apple-converted-space">  </span>M_3= 0.5 Gton. For the laser ranging interferometer measurement, the optimal filter for detecting point-like masses has a passband of 1 to 20 mHz,<span class="Apple-converted-space">  </span>differing from the 0.3 to 20 mHz admittance filter of Ghobadi-Far et al. (2018), which is not specialized for detecting point-like masses. M_3 for<span class="Apple-converted-space">  </span>future GRACE-like missions with different orbital parameters and improved instrument sensitivity is explored, and the optimum spacecraft separation is found.</p>

scholarly journals Noise reduction with reflection supervirtual interferometry

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V249-V256
Author(s):  
Kai Lu ◽  
Zhaolun Liu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Noise Reduction ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Data Set ◽  
Reflection Data ◽  
The Poor ◽  
The Earth ◽  
The Common ◽  
Noise Ratio

To image deeper portions of the earth, geophysicists must record reflection data with much greater source-receiver offsets. The problem with these data is that the signal-to-noise ratio (S/N) significantly diminishes with greater offset. In many cases, the poor S/N makes the far-offset reflections imperceptible on the shot records. To mitigate this problem, we have developed supervirtual reflection interferometry (SVI), which can be applied to far-offset reflections to significantly increase their S/N. The key idea is to select the common pair gathers where the phases of the correlated reflection arrivals differ from one another by no more than a quarter of a period so that the traces can be coherently stacked. The traces are correlated and summed together to create traces with virtual reflections, which in turn are convolved with one another and stacked to give the reflection traces with much stronger S/Ns. This is similar to refraction SVI except far-offset reflections are used instead of refractions. The theory is validated with synthetic tests where SVI is applied to far-offset reflection arrivals to significantly improve their S/N. Reflection SVI is also applied to a field data set where the reflections are too noisy to be clearly visible in the traces. After the implementation of reflection SVI, the normal moveout velocity can be accurately picked from the SVI-improved data, leading to a successful poststack migration for this data set.

Error Analysis for Estimation of Trace Vapor Concentration Pathlength in Stack Plumes

2003 ◽  
Vol 57 (6) ◽  
pp. 614-621 ◽  
Author(s):  
Neal B. Gallagher ◽  
Barry M. Wise ◽  
David M. Sheen
Keyword(s):  
Remote Sensing ◽  
Error Analysis ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Chemical Vapor ◽  
Noise Model ◽  
Vapor Concentration ◽  
Signal To Noise ◽  
Sensing Applications ◽  
Noise Ratio

Near-infrared hyperspectral imaging is finding utility in remote sensing applications such as detection and quantification of chemical vapor effluents in stack plumes. Optimizing the sensing system or quantification algorithms is difficult because reference images are rarely well characterized. The present work uses a radiance model for a down-looking scene and a detailed noise model for dispersive and Fourier transform spectrometers to generate well-characterized synthetic data. These data were used with a classical least-squares-based estimator in an error analysis to obtain estimates of different sources of concentration-pathlength quantification error in the remote sensing problem. Contributions to the overall quantification error were the sum of individual error terms related to estimating the background, atmospheric corrections, plume temperature, and instrument signal-to-noise ratio. It was found that the quantification error depended strongly on errors in the background estimate and second-most on instrument signal-to-noise ratio. Decreases in net analyte signal (e.g., due to low analyte absorbance or increasing the number of analytes in the plume) led to increases in the quantification error as expected. These observations have implications on instrument design and strategies for quantification. The outlined approach could be used to estimate detection limits or perform variable selection for given sensing problems.

SCIMP—A scanning interferometric multiplex photometer

1978 ◽  
Vol 56 (6) ◽  
pp. 681-686 ◽  
Author(s):  
G. G. Shepherd ◽  
A. J. Deans ◽  
Y. P. Neo
Keyword(s):  
Remote Sensing ◽  
Comparative Analysis ◽  
Signal To Noise Ratio ◽  
Interference Filter ◽  
Wavelength Shift ◽  
Spectral Elements ◽  
Signal To Noise ◽  
Airborne Remote Sensing ◽  
Noise Ratio ◽  
Rocket Measurements

An interference filter photometer concept is described in which equally-spaced spectral elements of equal width are generated. The method takes advantage of the wavelength shift of off-axis radiation transmitted by the filter, and is accomplished by the use of masks in the location of the field stop. This technique lends itself to multiplexing, using Fourier or Hadamard coding, but a direct spectral configuration is also possible. The advantages of the concept and a comparative analysis of signal-to-noise ratio are described. The technique has been employed in ground based airglow studies, airborne remote sensing, and rocket measurements of airglow and aurora.

scholarly journals ANALYSIS OF DENOISING INTERPRETATION OF REMOTE SENSING IMAGE BASED ON ICA-WAVELET TRANSFORM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 411-414
Author(s):  
L. Sun ◽  
X. S. Gan
Keyword(s):  
Remote Sensing ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Control Point ◽  
Remote Sensing Image ◽  
Signal To Noise ◽  
Processing Step ◽  
Feature Information ◽  
Noise Ratio

Abstract. The noise will blur the key information of the remote sensing image, such as edge texture and important feature information, which will result in the loss of key information contained in the remote sensing image, resulting in the degradation of the overall quality of the image, which will bring difficulties to the interpretation work. Therefore, in order to obtain higher precision, signal-to-noise ratio and improve the quality of remote sensing image, denoising the remote sensing image containing noise is a crucial step and processing step for image remote sensing image application.In this paper, the ICA wavelet analysis algorithm is applied to the application of real-time remote sensing image denoising. A series of pre-processing procedures such as control point correction, image fusion and image mosaic are carried out on the Asian sub-level remote sensing image, and the signal-to-noise ratio of the remote sensing image is adopted. (SNR/dB) and mean square error (RMSE) verify the image quality after denoising.

scholarly journals Parameter Simulation and Design of an Airborne Hyperspectral Imaging LiDAR System

2021 ◽  
Vol 13 (24) ◽  
pp. 5123
Author(s):  
Liyong Qian ◽  
Decheng Wu ◽  
Dong Liu ◽  
Shalei Song ◽  
Shuo Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Density ◽  
Hyperspectral Imaging ◽  
Optical Fibers ◽  
Signal To Noise Ratio ◽  
Design Parameters ◽  
Signal To Noise ◽  
Lidar System ◽  
Power Spectral ◽  
Noise Ratio

With continuous technological development, the future development trend of LiDAR in the field of remote sensing and mapping is to obtain the elevation and spectral information of ground targets simultaneously. Airborne hyperspectral imaging LiDAR inherits the advantages of active and passive remote sensing detection. This paper presents a simulation method to determine the design parameters of an airborne hyperspectral imaging LiDAR system. In accordance with the hyperspectral imaging LiDAR equation and optical design principles, the atmospheric transmission model and the reflectance spectrum of specific ground targets are utilized. The design parameters and laser emission spectrum of the hyperspectral LiDAR system are considered, and the signal-to-noise ratio of the system is obtained through simulation. Without considering the effect of detector gain and electronic amplification on the signal-to-noise ratio, three optical fibers are coupled into a detection channel, and the power spectral density emitted by the supercontinuum laser is simulated by assuming that the signal-to-noise ratio is equal to 1. The power spectral density emitted by the laser must not be less than 15 mW/nm in the shortwave direction. During the simulation process, the design parameters of the hyperspectral LiDAR system are preliminarily demonstrated, and the feasibility of the hyperspectral imaging LiDAR system design is theoretically guaranteed in combination with the design requirements of the supercontinuum laser. The spectral resolution of a single optical fiber of the hyperspectral LiDAR system is set to 2.5 nm. In the actual prototype system, multiple optical fibers can be coupled into a detection channel in accordance with application needs to further improve the signal-to-noise ratio of hyperspectral LiDAR system detection.

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