Signal to noise ratio simulation of lake water color monitoring oriented satellite remote sensing system

2010 ◽  
Author(s):  
Jia Tian ◽  
Bin Peng ◽  
Jing Wang ◽  
Xiang Li
Keyword(s):  
Remote Sensing ◽  
Lake Water ◽  
Satellite Remote Sensing ◽  
Signal To Noise Ratio ◽  
Water Color ◽  
Signal To Noise ◽  
Sensing System ◽  
Noise Ratio

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.

Sign in / Sign up

Export Citation Format

Share Document