scholarly journals Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

2019 ◽  
Vol 11 (22) ◽  
pp. 2603
Author(s):  
George Xian ◽  
Hua Shi ◽  
Cody Anderson ◽  
Zhuoting Wu
Keyword(s):  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Regression Tree ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Landsat 8 ◽  
Signal To Noise ◽  
Study Results ◽  
The Impact

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

scholarly journals MODIFIED OPTIMIZATION WATER INDEX (MOWI) FOR LANDSAT-8 OLI/TIRS

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 185-190 ◽  
Author(s):  
M. Moradi ◽  
M. Sahebi ◽  
M. Shokri
Keyword(s):  
Water Resources ◽  
Spatial Scales ◽  
Landsat Imagery ◽  
Human Life ◽  
Spectral Band ◽  
Remote Sensing Data ◽  
Particle Swarm Algorithm ◽  
Landsat 8 ◽  
Water Index ◽  
Water Detection

Water is one of the most important resources that essential need for human life. Due to population growth and increasing need of human to water, proper management of water resources will be one of the serious challenges of next decades. Remote sensing data is the best way to the management of water resources due time and cost effectiveness over a greater range of temporal and spatial scales. Between many kinds of satellite data, from SAR to optic or from high resolution to low resolution, Landsat imagery is more interesting data for water detection and management of earth surface water. Landsat8 OLI/TIRS is the newest version of Landsat satellite series. In this paper, we investigated the full spectral potential of Landsat8 for water detection. It is developed many kinds of methods for this purpose that index based methods have some advantages than other methods. Pervious indices just use a limited number of spectral band. In this paper, Modified Optimization Water Index (MOWI) defined by consideration of a linear combination of bands that each coefficient of bands calculated by particle swarm algorithm. The result shows that modified optimization water index (MOWI) has a proper performance on different condition like cloud, cloud shadow and mountain shadow.

A Study of Membrane Impact on Spatial Resolution of Liquid In Situ Transmission Electron Microscope

2020 ◽  
Vol 26 (1) ◽  
pp. 126-133
Author(s):  
Ming Li ◽  
Ruth Knibbe
Keyword(s):  
Electron Microscope ◽  
Spatial Resolution ◽  
Transmission Electron Microscope ◽  
Signal To Noise Ratio ◽  
Chromatic Aberration ◽  
Membrane Thickness ◽  
Additional Material ◽  
Transmission Electron ◽  
The Impact

AbstractMicrochip technology with electron transparent membranes is a key component for in situ liquid transmission electron microscope (TEM) characterization. The membranes can significantly influence the TEM imaging spatial resolution, not only due to introducing additional material layers but also due to the associated bulging. The membrane bulging is largely defined by the membrane materials, thickness, and short dimension. The impact of the membrane on the spatial resolution, especially the extent of its bulging, was systematically investigated through the impact on the signal-to-noise ratio, chromatic aberration, and beam broadening. The optimization of the membrane parameters is the key component when designing the in situ TEM liquid cell. The optimal membrane thickness of 50 nm was found which balances the impact of membrane bulging and membrane thickness. Beyond this, the short membrane window dimension and the chip nominal spacing should be minimized. However, these two parameters have practical limitations in regards to chip handling.

Spatial resolution and signal-to-noise ratio in x-ray imaging

2019 ◽  
Author(s):  
Timur Gureyev ◽  
David M. Paganin ◽  
Alex Kozlov ◽  
Harry Quiney
Keyword(s):  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
X Ray ◽  
X Ray Imaging ◽  
Noise Ratio

scholarly journals The Geometry of Noise in Color and Spectral Image Sensors

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4487
Author(s):  
Axel Clouet ◽  
Jérôme Vaillant ◽  
David Alleysson
Keyword(s):  
Signal To Noise Ratio ◽  
Spectral Estimation ◽  
Spectral Characteristics ◽  
Color Space ◽  
Color Correction ◽  
Signal To Noise ◽  
Noise Evaluation ◽  
Noise Ratio ◽  
Correction Step ◽  
The Impact

Digital images are always affected by noise and the reduction of its impact is an active field of research. Noise due to random photon fall onto the sensor is unavoidable but could be amplified by the camera image processing such as in the color correction step. Color correction is expressed as the combination of a spectral estimation and a computation of color coordinates in a display color space. Then we use geometry to depict raw, spectral and color signals and noise. Geometry is calibrated on the physics of image acquisition and spectral characteristics of the sensor to study the impact of the sensor space metric on noise amplification. Since spectral channels are non-orthogonal, we introduce the contravariant signal to noise ratio for noise evaluation at spectral reconstruction level. Having definitions of signal to noise ratio for each steps of spectral or color reconstruction, we compare performances of different types of sensors (RGB, RGBW, RGBWir, CMY, RYB, RGBC).

k-Space filtering in 2D gradient-echo breath-hold hyperpolarized3He MRI: Spatial resolution and signal-to-noise ratio considerations

2002 ◽  
Vol 47 (4) ◽  
pp. 687-695 ◽  
Author(s):  
Jim M. Wild ◽  
Martyn N.J. Paley ◽  
Magalie Viallon ◽  
Wolfgang G. Schreiber ◽  
Edwin J.R. van Beek ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Breath Hold ◽  
Gradient Echo ◽  
Signal To Noise ◽  
Noise Ratio

Performance Analysis of Hybrid Fusion in Cognitive Radio Networks

2013 ◽  
Vol 479-480 ◽  
pp. 1027-1031
Author(s):  
Man Man Guo ◽  
Yun Xue Liu ◽  
Wen Qiang Fan
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Signal To Noise Ratio ◽  
Radio Networks ◽  
Base Station ◽  
Good Choice ◽  
Signal To Noise ◽  
Noise Ratio ◽  
The Impact ◽  
Hybrid Fusion

Spectrum sensing is a crucial issue in cognitive radio networks for primary user detection. Cooperative sensing based on energy detection in the cognitive radio network with multiple antennas base-station is considered in this letter. To improve the sensing performance, we investigate hybrid fusion of the observed energies from the base-station and decisions (1bit, hard information) from different cognitive radio (CR) users around the base-station. Further, we present an optimized scheme where the global detection probability can be maximized according to the Neyman-Pearson criterion. Finally the impact of the change of parameters (Signal to Noise Ratio and number of CR users) in the optimized scheme is analyzed. Numerical simulations and extensive analysis confirm that hybrid fusion base on the optimized scheme is a good choice, also, Signal to Noise Ratio (SNR) and number of CR users does not have influence on the optimized scheme

scholarly journals A Methodology for Attributing the Role of Climate Change in Extreme Events: A Global Spectrally Nudged Storyline

2021 ◽  
Author(s):  
Linda van Garderen ◽  
Frauke Feser ◽  
Theodore G. Shepherd
Keyword(s):  
Climate Change ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Extreme Weather ◽  
Climate Impacts ◽  
Lower Atmosphere ◽  
Extreme Weather Events ◽  
Past Century ◽  
Signal To Noise ◽  
Noise Ratio

<p>Extreme weather events are generally associated with unusual dynamical conditions, yet the signal-to-noise ratio of the dynamical aspects of climate change that are relevant to extremes appears to be small, and the nature of the change can be highly uncertain. On the other hand, the thermodynamic aspects of climate change are already largely apparent from observations, and are far more certain since they are anchored in agreed-upon physical understanding. The storyline method of extreme event attribution, which has been gaining traction in recent years, quantitatively estimates the magnitude of thermodynamic aspects of climate change, given the dynamical conditions. There are different ways of imposing the dynamical conditions. Here we present and evaluate a method where the dynamical conditions are enforced through global spectral nudging towards reanalysis data of the large-scale vorticity and divergence in the free atmosphere, leaving the lower atmosphere free to respond. We simulate the historical extreme weather event twice: first in the world as we know it, with the events occurring on a background of a changing climate, and second in a ‘counterfactual’ world, where the background is held fixed over the past century. We describe the methodology in detail, and present results for the European 2003 heatwave and the Russian 2010 heatwave as a proof of concept. These show that the conditional attribution can be performed with a high signal-to-noise ratio on daily timescales and at local spatial scales. Our methodology is thus potentially highly useful for realistic stress testing of resilience strategies for climate impacts, when coupled to an impact model.</p>

scholarly journals Evaluation of Nonlinear Interference Effects in a Dispersion Managed (DM) Optical Fiber: Performance and Transmission Analysis of 16QAM Modulation using Split Step Fourier Method

2020 ◽  
Author(s):  
Reinhardt Rading
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Optical Fiber ◽  
Signal To Noise Ratio ◽  
Fourier Method ◽  
Interference Effects ◽  
Signal To Noise ◽  
Optical Links ◽  
Noise Ratio ◽  
The Impact

<div>This paper investigates the impact on the optical</div><div>signal-to-noise ratio (OSNR) of the residual per span (RDPS) in a N × 100km dispersion managed system with zero total accumulated dispersion from input to output using split step Fourier method (SSFM) -Monte Carlo simulation. </div><div><br></div><div>This paper shows that the nonlinear interference NLI does in-fact impact the performance yielding different best working power depending on the value of Nx100 km span and the type of dispersion managed link. The paper shows that dispersion uncompensated optical links are preferable to dispersion managed fibers in equalizing NLI effects in long haul optical links.</div>

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