Changes In Signal-to-noise Ratio Research Articles

The Output Signal-to-Noise Ratio and Speech Perception in Noise: The Effect of Multichannel and Free-Channel Hearing Aids

Background and Aim: The output Signal-to-Noise Ratio (SNR) is one of the essential factors in hearing aid benefits. There is limited evidence regarding SNR improvement by the Channel-Free (CFHA) and Multi-Channel Hearing Aid (MCHA) and the speech understanding in noise through them. This study aimed to investigate the extent to which output SNR was modified by CFHA and MCHA processing and the variation in aided speech recognition abilities with a change in output SNR. Methods: Thirty-six participants aged 50–65 years were included. A chosen CFHA and MCHA were used to obtain the output SNR and sentence recognition in noise in four different processing algorithms (linear, linear+noise reduction, WDRC, WDRC+noise reduction). Hagerman’s phase inversion technique was used to measure the attenuation of noise and, in turn, to obtain the output SNR of the hearing aid. Results: In all hearing aid processing algorithms among those with normal hearing and people with hearing loss, the output of CFHA revealed higher attenuation values than that of MCHA. There was a significant effect of the hearing aids and processing algorithms in both normal and individuals with hearing impairment on the mean SNR. Further, multiple linear regression analysis results showed that whether the hearing is channel-free or multi- channel significantly predicted speech recognition scores, while output SNR and processing algorithms did not. Conclusion: The signal processing algorithms in CFHA had greater noise attenuation values, better output SNR, and speech recognition scores, showing an advantage over the modern MCHA among individuals with hearing impairment. Keywords: Channel-free hearing aids; multi-channel hearing aids; sensorineural hearing loss; speech recognition

Factors Predicting Subjective Satisfaction for Successful Hearing Aid Adaptation.

(1) Background: For successful hearing aid (HA) use during daily life, an objective parameter reflecting the subjective satisfaction is required. We explored the aided hearing status, hearing in noise test (HINT) scores, and subjective outcomes to predict performance improvements in everyday living. (2) Methods: A total of 406 patients with hearing loss (HL) who were prescribed HAs were included and were divided into two groups according to the symmetricity of HL. The relationship between audiometric data and subjective questionnaires under unaided and aided (3 months) conditions were investigated. (3) Results: Patients with symmetric HL showed a significant HINT signal-to-noise ratio (SNR) change and significant increase in their subjective satisfaction questionnaire score under the bilateral HA condition. On the other hand, the HINT SNR change and subjective questionnaire score showed various significances according to the side of HA (better or worse hearing) in asymmetric HL HINT SNR and was significantly correlated with the subjective questionnaire score in symmetric HL patients and AHL patients with unilateral HA in their better ear. (4) Conclusions: The HINT SNR improvement after long-term HA use could be an effective tool for predicting the subjective satisfaction of HA use and HA validation.

Artifact noise suppression of particle-field computed tomography based on lightweight residual and enhanced convergence neural network

<sec>The realization of fast and high-quality three-dimensional particle-field image characterization is always highly desired in the areas, such as experimental fluid mechanics and biomedicine, etc., as the micro-particle distribution status in a flow-field can characterize the field properties well. In the particle-field image reconstruction and characterization, a popularly-used approach at present is the computed tomography. The great advantage of the computed tomography for particle-field image reconstruction lies in that the full particle spatial distribution can be obtained and presented due to multi-angle sampling.</sec><sec>Recently, with the development and application of deep learning techniques in the computed tomography, the image quality is greatly improved by means of the powerful learning ability of a deep learning network. In addition, the deep learning application also makes it possible to speed up the computed tomographic imaging process from sparse-sampling due to the strong image feature extraction ability of the network. However, sparse-sampling would lead to insufficient acquirement of the object information during sampling for the computed tomography. Therefore, a sort of artifact noise would emerge and accompany with the reconstructed images, and thus severely affect the image quality. As there is no universal network approach that can be applicable to all types of objects in the suppression of artifact noise, it is still a challenge in removing the sparse-sampling-induced artifact noise in the computed tomography by now.</sec><sec>Therefore, we propose and develop a specific lightweight residual and enhanced convergence neural network (LREC-net) approach for suppressing the artifact noise in the particle-field computed tomography here. In this method, the network input dataset is also optimized in signal-to-noise ratio (SNR) to reduce the input noise and ensure the effective particle image feature extraction of the network during the imaging process.</sec><sec>In the LREC-net architecture design, a five layers of lightweight and dual-residual down-sampling are constructed on the basis of typical U-net and Resnet50 to make the LREC-net to be more suitable for the particle-field image reconstruction. Moreover, a fast feature convergence module for rapid particle-field feature acquirement is added to up-sampling process of the network to further promote the network processing efficiency. Apart from the design of LREC-net network itself, the optimization of network input dataset in SNR of images is achieved by finding a fit image reconstruction algorithm that can produce higher-SNR particle images in the computed tomography. This achievement reduces the input noise as much as possible and ensure effective particle-field feature extraction by the network.</sec><sec>The simulation analysis and experimental test verify effectiveness of the proposed LREC-net method, which involve the evaluations of SNR changes of the input-output images through the network, the proportion of residual artifact noise as ghost-particles (GPP) in the reconstructed images, and the valid-particle loss proportion (PLP). In contrast to the performances of U-net and Resnet50 under the same imaging conditions, all the data in SNR, GPP and PLP show the great improvement of the image quality due to the application of LREC-net method. Meanwhile, the designed LREC-net method also enhances the network running efficiency to a large extent due to the remarkable reduction of training time. Therefore, this work provides a new and effective approach for developing sparse-sampling-based fast and high-quality particle-field computed tomography.</sec>

Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.

To develop a method for building MRI reconstruction neural networks robust to changes in signal-to-noise ratio (SNR) and trainable with a limited number of fully sampled scans. We propose Noise2Recon, a consistency training method for SNR-robust accelerated MRI reconstruction that can use both fully sampled (labeled) and undersampled (unlabeled) scans. Noise2Recon uses unlabeled data by enforcing consistency between model reconstructions of undersampled scans and their noise-augmented counterparts. Noise2Recon was compared to compressed sensing and both supervised and self-supervised deep learning baselines. Experiments were conducted using retrospectively accelerated data from the mridata three-dimensional fast-spin-echo knee and two-dimensional fastMRI brain datasets. All methods were evaluated in label-limited settings and among out-of-distribution (OOD) shifts, including changes in SNR, acceleration factors, and datasets. An extensive ablation study was conducted to characterize the sensitivity of Noise2Recon to hyperparameter choices. In label-limited settings, Noise2Recon achieved better structural similarity, peak signal-to-noise ratio, and normalized-RMS error than all baselines and matched performance of supervised models, which were trained with more fully sampled scans. Noise2Recon outperformed all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to OOD acceleration factors. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. Noise2Recon is a label-efficient reconstruction method that is robust to distribution shifts, such as changes in SNR, acceleration factors, and others, with limited or no fully sampled training data.

Alpha Oscillations During Effortful Continuous Speech: From Scalp EEG to Ear-EEG.

The purpose of this study was to investigate alpha power as an objective measure of effortful listening in continuous speech with scalp and ear-EEG. Scalp and ear-EEG were recorded simultaneously during presentation of a 33-s news clip in the presence of 16-talker babble noise. Four different signal-to-noise ratios (SNRs) were used to manipulate task demand. The effects of changes in SNR were investigated on alpha event-related synchronization (ERS) and desynchronization (ERD). Alpha activity was extracted from scalp EEG using different referencing methods (common average and symmetrical bi-polar) in different regions of the brain (parietal and temporal) and ear-EEG. Alpha ERS decreased with decreasing SNR (i.e., increasing task demand) in both scalp and ear-EEG. Alpha ERS was also positively correlated to behavioural performance which was based on the questions regarding the contents of the speech. Alpha ERS/ERD is better suited to track performance of a continuous speech than listening effort. EEG alpha power in continuous speech may indicate of how well the speech was perceived and it can be measured with both scalp and Ear-EEG.

Do Visual Cues Aid Comprehension of a Dialogue?

Do Visual Cues Aid Comprehension of a Dialogue?

Design of Low Probability Detection Signal with Application to Physical Layer Security

In this work, we mainly focus on low probability detection (LPD) and low probability interception (LPI) wireless communication in cyber-physical systems. An LPD signal waveform based on multi-carrier modulation and an under-sampling method for signal detection is introduced. The application of the proposed LPD signal for physical layer security is discussed in a typical wireless-tap channel model, which consists of a transmitter (Alice), an intended receiver (Bob), and an eavesdropper (Eve). Since the under-sampling method at Bob’s end depends very sensitively on accurate sampling clock and channel state information (CSI), which can hardly be obtained by Eve, the security transmission is initialized as Bob transmits a pilot for Alice to perform channel sounding and clock synchronization by invoking the channel reciprocal principle. Then, Alice sends a multi-carrier information-bearing signal constructed according to Bob’s actual sampling clock and the CSI between the two. Consequently, Bob can coherently combine the sub-band signals after sampling, while Eve can only obtain a destructive combination. Finally, we derived the closed-form expressions of detection probability at Bob’s and Eve’s ends when the energy detector is employed. Simulation results show that the bit error rate (BER) at Alice’s end is gradually decreased with the increase in the signal-to-noise ratio (SNR) in both the AWGN and fading channels. Meanwhile, the BER at Eve’s end is always unacceptably high no matter how the SNR changes.

Variations in signal-to-noise characteristics of tissue-equivalent attenuators for mammographic automatic exposure control system performance evaluation.

This work investigates the impact of tissue-equivalent attenuator choice on measured signal-to-noise ratio (SNR) for automatic exposure control (AEC) performance evaluation in digital mammography. It also investigates how the SNR changes for each material when used to evaluate AEC performance across different mammography systems. AEC performance was evaluated for four mammography systems using seven attenuator sets at two thicknesses (4 and 8 cm). All systems were evaluated in 2D imaging mode, and one system was evaluated in digital breast tomosynthesis (DBT) mode. The methodology followed the 2018 ACR digital mammography quality control (DMQC) manual. Each system-attenuator-thickness combination was evaluated using For Processing images in ImageJ with standard ROI size and location. The closest annual physicist testing results were used to explore the impact of varying measured AEC performance on image quality. The measured SNR varied by 44%-54% within each system across all attenuators at 4 cm thickness in 2D mode. The variation appeared to be largely due to changes in measured noise, with variations of 46%-67% within each system across all attenuators at 4 cm thickness in 2D mode. Two systems had failing SNR levels for two of the materials using the minimum SNR criterion specified in the ACR DMQC manual. Similar trends were seen in DBT mode and at 8 cm thickness. Within each material, there was 115%-131% variation at 4 cm and 82%-114% variation at 8 cm in the measured SNR across the four imaging systems. Variation in SNR did not correlate with system operating level based on visual image quality and average glandular dose (AGD). Choice of tissue-equivalent attenuator for AEC performance evaluation affects measured SNR values. Depending on the material, the difference may be enough to result in failure following the longitudinal and absolute thresholds specified in the ACR DMQC manual.

Measurements of suspended ashes concentration in turbulent flow with acoustic doppler velocimeter

Measuring and estimating dispersion coefficients for ash particles released in watercourses is required for a broad understanding of their transport and behaviour in aquatic ecosystems. Calibration of a high-frequency acoustic backscattering system and estimates of its sensitivity to particulates of known size distribution and concentration are presented. Laboratory experiments were conducted to derive the relationship between suspended sediment concentration (SSC) of biomass ashes and the signal-to-noise ratio (SNR) measured by a 10 MHz ADV. Small portions of ashes were added in steps (0.006–20 gl−1) into a stirred water tank while ADV was recording changes in SNR, with various combinations of ADV settings, which included power (LOW, LOW+), transmit length (TL = 0.3 mm, 0.6 mm) and frequencies of data acquisition (25 Hz, 100 Hz). Samples were collected for gravimetric and particle size distribution (PSD) analysis. As expected, the salinity and pH of the collected samples increased with SSC. Different backscatter responses were detected depending on the ADV settings and SSC. To maximize the calibration range, the ADV settings power LOW+, TL = 0.3 mm and sample frequency 25 Hz were combined, resulting in a calibrated SSC range of 10–500 mgl−1, where a linear relationship was obtained. The results suggest that it may be possible to apply a single-frequency acoustic technique to the detection of suspended ashes. The particle type and size distribution changed with a concentration above 0.5 gl−1, which may lead to errors in the acoustic estimates of SSC in ash-laden flows.

EFFECTS OF SMOOTH, MEDIUM SMOOTH AND MEDIUM RECONSTRUCTION KERNELS ON IMAGE QUALITY IN THREE-PHASE CT OF LIVER

Reconstruction kernel is one of the parameters that affects the computed tomography (CT) image quality. This study aimed to evaluate the effects of applying three different reconstruction kernels on image quality in 3-phased CT of the liver. A total of 63 CT liver images including normal liver (n = 43) and liver lesion (n = 20) were retrospectively reviewed. Smooth (B20f), medium smooth (B30f) and medium (B40f) reconstruction kernels were employed in the image reconstruction process. Mean attenuation, image noise, and signal-to-noise ratio (SNR) values from each kernel reconstruction were quantified and compared among those kernels using One Way Analysis of Variance (ANOVA) statistical analysis. Significant changes in image noise and SNR were observed in the normal liver (p < 0.001, respectively) following the application of those reconstruction kernels. However, no significant changes in mean attenuation, image noise, and SNR were demonstrated in the liver lesion (p > 0.05). Application of smooth (B20f), medium smooth (B30f), and medium (B40f) kernel reconstructions would significantly affect the image noise and SNR in the normal liver of CT images instead of liver lesions. Hence, proper selection of reconstruction kernel is important in CT images reconstruction to improve precision in diagnostic CT interpretation.

Measuring hearing aid compression algorithm preference with the Tympan

In this Tympan challenge, we aim to evaluate listeners’ preferences for different Wide Dynamic Range Compression (WDRC) settings in a real-world environment. While WDRC is used to provide comfort for high-level sounds and audibility for low-level sounds, the nonlinear algorithm distorts the stimulus envelope and can alter the input signal-to-noise ratio (SNR). To date, most measurements of speech understanding and preference under different WDRC parameters (e.g., compression speed and compression ratio) have used pre-processed stimuli presented in a sound-attenuating room. In this study, we plan to measure listeners’ preference for different algorithm settings by using the Tympan in real-world environments. The Tympan will be programmed based on the listeners’ hearing loss, but the user will be able to switch between algorithm settings immediately in their environment using the associated Smartphone app. Using ecological momentary assessment, we will briefly survey the users about their perceptions as they switch between settings. We also will use the Tympan to save acoustic samples and to measure the acoustic properties of the environment and the hearing output, in an effort to determine the drivers of the preference scores [i.e., amount of distortion, degree of audibility, SNR changes (if possible), etc.].

Analysis of Visible Light Communication Link’s Performance in Fire Smoke Environment

This paper mainly studies the communication performance of visible light communication link in fire smoke environment. Compared with the previous studies, this study no longer only considers a single wavelength and considers the influence of smoke and background light noise. Firstly, the variation of background light noise caused by sunlight during a day was analyzed, then signal-to-noise ratio (SNR) and bit error rate (BER) of the visible light communication link in fire smoke environment with different visibility were calculated, and finally, the variation of received power with the angle β between the LED optical axis and the communication link (from LED to receiver) was analyzed. The results show that, during a day from 07:00 to 18:30, background light noise first increases and then decreases, BER changes in the same trend and reaches the maximum at about 13:00, while SNR changes in the opposite trend and reaches the minimum at about 13:00. When visibility is 100 m, the maximum difference of SNR between 7:00 and 13:00 is about 19 dB, and the maximum difference of BER is about 5.3E-10. At 7:00, the maximum difference of SNR is about 13 dB between the visibility of 1 m and the visibility of 100 m, and the maximum difference of BER is about 1.44E − 11, and received power decreases with the increase of β. In addition, SNR increases with visibility of fire smoke environment, and BER changes in the opposite trend. Lastly, in order to enhance the performance of visible light communication in fire smoke environment, this paper gives the transmitting power scheme.

Phenomenon of stochastic resonance for an underdamped monostable system with multiplicative and additive noise

The stochastic resonance (SR) phenomenon for an underdamped monostable system with multiplicative and additive noise is investigated. The expression for the stationary probability density is obtained under the condition of the detailed balance and weak noise. The signal-to-noise ratio (SNR) for the monostable system is derived based on two-state theory. The result shows that the SR phenomenon can be observed when the SNR varies with the intensities of the multiplicative and additive white noise, as well as varies with the amplitude of the additive dichotomous noise. One resonance peak can be found when the SNR changes with the damping coefficient and with other system parameters.

Design of Entry Detection Method for Top-Bounded Spaces using GPS SNR and Spatial Characteristics for Seamless Positioning in Logistics Facilities.

With the widespread use of indoor positioning technology, various services based on this technology are beginning to be offered to consumers and industrial applications. In the case of logistics facilities, in addition to indoor and outdoor spaces, there are top-bounded spaces (TBSs): elongated areas that are covered with roofs or eaves on the upper parts of buildings. The sides of such spaces are open, and workers and forklifts work in these areas. Only a few studies have been conducted on positioning methods for this unusual environment, and the way by which Signal-to-Noise Ratio (SNR) of Global Positioning System (GPS) changes with the stay in TBSs is unclear. Therefore, we conducted preliminary experiments and confirmed that TBS dwellings are difficult to stably detect with existing methods due to the combination of satellites with variable and unchanged SNRs. In this study, we designed a simple processing flow for selecting satellites with high probabilities of changing SNRs by using the spatial characteristics of TBSs as parameters (height, depth, and side opening orientation). We propose a method to detect the stay in TBSs using the SNR change rates of the selected satellites. As a result of evaluation experiments with three TBSs, we successfully detected the stay in TBSs with about 30% higher probability than those of an existing method.

The very low-frequency transmitter radio wave anomalies related to the 2010 Ms 7.1 Yushu earthquake observed by the DEMETER satellite and the possible mechanism

Abstract. Earthquakes may disturb the lower ionosphere through various coupling mechanisms during the seismogenic and coseismic periods. The VLF (very low-frequency) signal radiated from ground-based transmitters will be affected when it penetrates the disturbed ionosphere above the epicenter area, and this anomaly can be recorded by low-Earth orbit satellites under certain conditions. In this paper, the temporal and spatial variation of the signal-to-noise ratio (SNR) of the VLF transmitter signal in the ionosphere over the epicenter of 2010 Yushu Ms 7.1 earthquake in China is analyzed using DEMETER (Detection of Electro-Magnetic Emission Transmitted from Earthquake Regions) satellite observation. The results show that SNR over the epicenter of the Yushu earthquake especially in the southwestern region decreased (or dropped) before the main shock, and a GPS–TEC (Global Positioning System; total electron content) anomaly accompanied, which implies that the decrease in SNR might be caused by the enhancement of TEC. A full-wave method is used to study the mechanism of the change in SNR before the earthquake. The simulated results show SNR does not always decrease before an earthquake. When the electron density in the lower ionosphere increases by 3 times, the electric field will decrease about 2 dB, indicating that the disturbed-electric-field decrease of 20 % compared with the original electric field and vice versa. It can be concluded that the variation of electron density before earthquakes may be one of the important factors influencing the variation of SNR.

An exploratory Study of EEG Alpha Oscillation and Pupil Dilation in Hearing-Aid Users During Effortful listening to Continuous Speech.

Individuals with hearing loss allocate cognitive resources to comprehend noisy speech in everyday life scenarios. Such a scenario could be when they are exposed to ongoing speech and need to sustain their attention for a rather long period of time, which requires listening effort. Two well-established physiological methods that have been found to be sensitive to identify changes in listening effort are pupillometry and electroencephalography (EEG). However, these measurements have been used mainly for momentary, evoked or episodic effort. The aim of this study was to investigate how sustained effort manifests in pupillometry and EEG, using continuous speech with varying signal-to-noise ratio (SNR). Eight hearing-aid users participated in this exploratory study and performed a continuous speech-in-noise task. The speech material consisted of 30-second continuous streams that were presented from loudspeakers to the right and left side of the listener (±30° azimuth) in the presence of 4-talker background noise (+180° azimuth). The participants were instructed to attend either to the right or left speaker and ignore the other in a randomized order with two different SNR conditions: 0 dB and -5 dB (the difference between the target and the competing talker). The effects of SNR on listening effort were explored objectively using pupillometry and EEG. The results showed larger mean pupil dilation and decreased EEG alpha power in the parietal lobe during the more effortful condition. This study demonstrates that both measures are sensitive to changes in SNR during continuous speech.

Listening Effort in Native and Nonnative English-Speaking Children Using Low Linguistic Single- and Dual-Task Paradigms.

Purpose It is not clear if behavioral indices of listening effort are sensitive to changes in signal-to-noise ratio (SNR) for young children (7-12 years old) from multilingual backgrounds. The purpose of this study was to explore the effects of SNR on listening effort in multilingual school-aged children (native English, nonnative English) as measured with a single- and a dual-task paradigm with low-linguistic speech stimuli (digits). The study also aimed to explore age effects on digit triplet recognition and response times (RTs). Method Sixty children with normal hearing participated, 30 per language group. Participants completed single and dual tasks in three SNRs (quiet, -10 dB, and -15 dB). Speech stimuli for both tasks were digit triplets. Verbal RTs were the listening effort measure during the single-task paradigm. A visual monitoring task was the secondary task during the dual-task paradigm. Results Significant effects of SNR on RTs were evident during both single- and dual-task paradigms. As expected, language background did not affect the pattern of RTs. The data also demonstrate a maturation effect for triplet recognition during both tasks and for RTs during the dual-task only. Conclusions Both single- and dual-task paradigms were sensitive to changes in SNR for school-aged children between 7 and 12 years of age. Language background (English as native language vs. English as nonnative language) had no significant effect on triplet recognition or RTs, demonstrating practical utility of low-linguistic stimuli for testing children from multilingual backgrounds.

Design of Galvanic Coupling Intra-Body Communication Transceiver Using Direct Sequence Spread Spectrum Technology

Intra-body communication (IBC) uses the human body as the transmission medium for electrical signals, and it features the following advantages: low power consumption, strong anti-interference ability, high data security, and broad application scenarios. However, some technical issues still need to be addresses, such as the choice of the best modulation and demodulation scheme in different application scenarios, influence of human activity on IBC performance, variable signal-to-noise ratio (SNR), and influence of transmission distance change on different modulation and demodulation methods. This paper adopts direct sequence spread spectrum (DSSS) communication and phase modulation to realize DSSS-differential phase shift keying (DPSK) and DPSK modulation transmission of baseband data. Moreover, the Costas loop method is employed to achieve reliable symbol recovery. Under the same conditions, in vivo experiments were conducted to compare the performance of DSSS-DPSK and DPSK galvanic coupling IBC transceivers. Notably, these transceivers are affected by the changes in SNR, transmission distance, and human activities. Results show that the bit error rate (BER) of the DPSK scheme is 40 times larger than the DSSS-DPSK scheme in a 30 cm channel length and different SNR experiments. When the BER performance changes from extremely poor (1.40 ×10 -1 ) to excellent (1.51×10 -6 ), the SNR of DSSS-DPSK scheme only needs to be improved by 16 dB. In contrast, when the BER performance changes from extremely poor (1.54 ×10 -1 ) to good (1.65 ×10 -5 ), the SNR of DPSK scheme needs to be improved by 25 dB. With a SNR of -5 dB, the BER ratios of the DPSK scheme is 7 times larger than the DSSS-DPSK scheme. Also, DSSS-DPSK scheme is more sensitive to changes in motion status than DPSK.

No prior recognition method of modulation mode by partition-fractal and SVM learning method

A modulation classification method in combination with partition-fractal and support-vector machine (SVM) learning methods is proposed to realize no prior recognition of the modulation mode in satellite laser communication systems. The effectiveness and accuracy of this method are verified under nine modulation modes and compared with other learning algorithms. The simulation results show when the signal-to-noise ratio (SNR) of the modulated signal is more than 8 dB, the classifier accuracy based on the proposed method can achieve more than 98%, especially when in binary phase shift keying and quadrature amplitude shift keying modes, and the classifier achieves 100% identification whatever the SNR changes to. In addition, the proposed method has strong scalability to achieve more modulation mode identification in the future.

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

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.