Статті в журналах: "Spatial correction"

1

Schultz, Craig A., Stephen C. Myers, James Hipp, and Christopher J. Young. "Nonstationary Bayesian kriging: A predictive technique to generate spatial corrections for seismic detection, location, and identification." Bulletin of the Seismological Society of America 88, no. 5 (October 1, 1998): 1275–88. http://dx.doi.org/10.1785/bssa0880051275.

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Abstract Seismic characterization works to improve the detection, location, and identification of seismic events by correcting for inaccuracies in geophysical models. These inaccuracies are caused by inherent averaging in the model, and, as a result, exact data values cannot be directly recovered at a point in the model. Seismic characterization involves cataloging reference events so that inaccuracies in the model can be mapped at these points and true data values can be retained through a correction. Application of these corrections to a new event requires the accurate prediction of the correction value at a point that is near but not necessarily coincident with the reference events. Given that these reference events can be sparsely distributed geographically, both interpolation and extrapolation of corrections to the new point are required. In this study, we develop a closed-form representation of Bayesian kriging (linear prediction) that incorporates variable spatial damping. The result is a robust nonstationary algorithm for spatially interpolating geophysical corrections. This algorithm extends local trends when data coverage is good and allows for damping (blending) to an a priori background mean when data coverage is poor. Benchmark tests show that the technique gives reliable predictions of the correction value along with an appropriate uncertainty estimate. Tests with travel-time residual data demonstrate that combining variable damping with an azimuthal coverage criterion reduces the large errors that occur with more classical linear prediction techniques, especially when values are extrapolated in poor coverage regions. In the travel-time correction case, this technique generates both seismic corrections along with uncertainties and can properly incorporate model error in the final location estimate. Results favor the applicability of this nonstationary algorithm to other types of seismic corrections such as amplitude and attenuation measures.

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Kang, Kwangmin, and Venkatesh Merwade. "The effect of spatially uniform and non-uniform precipitation bias correction methods on improving NEXRAD rainfall accuracy for distributed hydrologic modeling." Hydrology Research 45, no. 1 (June 18, 2013): 23–42. http://dx.doi.org/10.2166/nh.2013.194.

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In order to improve the accuracy of rainfall estimates from next generation radar (NEXRAD) uncertainties, data assimilation technique is performed by considering NEXRAD and available rain gauges which can be used to assimilate spatially uniform Multisensor Precipitation Estimator (MPE) scheme and non-uniform (based on rainfall interpolation and bias interpolation) NEXRAD bias estimations during a storm event by Kalman filtering. Even though NEXRAD provides a better spatial representation of rainfall variability than rain gauge information, it suffers from uncertainties and biases due to Z–R (reflectivity–rainfall) conversion method and limitation of available rain gauge information for NEXRAD bias correction in a particular river basin. Analysis of correcting NEXRAD bias rainfall with three different bias correction schemes is described in this study. The prediction accuracy of the STORE DHM (Storage Released based Distributed Hydrologic Model) simulations is also evaluated by using three different NEXRAD bias corrected rainfall inputs. The Upper Wabash River (17,907 km2) and the Upper Cumberland River (38,160 km2) basins are selected as the study areas to evaluate rainfall input sensitivity on different spatial characteristics. Use of spatially non-uniform NEXRAD bias correction schemes results has better rainfall and prediction accuracy compared to that of spatially uniform NEXRAD bias correction rainfall.

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Jiao, Zhaoqiang, Yiwen Li, Ge Chen, Yao Li, Shijie Chai, and Puyousen Zhang. "Correction of Spatial Nonuniformity in Spectroradiometer Field-of-View Using a Concentric-Circles Method." Photonics 9, no. 2 (January 21, 2022): 56. http://dx.doi.org/10.3390/photonics9020056.

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Spectroradiometers exhibit the smallest aberration and the optimum response at the field-of-view (FOV) center. The aberration increases and the response deteriorates at positions further away from the FOV center, which leads to nonuniformity in the spectroradiometer FOV. In this study, a concentric-circles method for correcting the spectroradiometer FOV nonuniformity was developed. The calibration experiment for FOV nonuniformity was conducted by establishing the experimental platform. The nonuniformity correction coefficients were obtained and then used to fit the correction function curve within the whole FOV, allowing for correction of measurement targets with an arbitrary shape. The radiation intensity of the blackbody at different temperatures was obtained by measurement, and the nonuniformity coefficient was used to correct it. After correction, the error was within 1.84% for the spectrally integrated radiant intensity in the non-absorption band. Using this correction method, efficient calibration of spectroradiometer nonuniformity can be achieved, thereby enhancing the measurement accuracy of the spectroradiometer.

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Gou, Yabin, and Haonan Chen. "Combining Radar Attenuation and Partial Beam Blockage Corrections for Improved Quantitative Application." Journal of Hydrometeorology 22, no. 1 (January 2021): 139–53. http://dx.doi.org/10.1175/jhm-d-20-0121.1.

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AbstractPartial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (KDP)–reflectivity (ZH) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived KDP and ZH relationships, as well as rainfall estimates based on R(ZH) and R(KDP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of ZH measurements can effectively be enhanced; (ii) the distribution of radar-measured KDP versus ZH is more consistent with the DSD-derived KDP versus ZH; (iii) the measured ZH from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where ZH is larger than 30 dBZ; and (iv) rainfall estimates based on R(ZH) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(KDP).

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5

Hoefler, Raegan, Pablo González-Barrios, Madhav Bhatta, Jose A. R. Nunes, Ines Berro, Rafael S. Nalin, Alejandra Borges, et al. "Do Spatial Designs Outperform Classic Experimental Designs?" Journal of Agricultural, Biological and Environmental Statistics 25, no. 4 (August 29, 2020): 523–52. http://dx.doi.org/10.1007/s13253-020-00406-2.

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Abstract Controlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a two-dimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments. However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1 $$\times $$ × AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments. Supplementary materials accompanying this paper appear online.

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6

Serhal, Philippe, and Sébastien Lemieux. "Correction of Spatial Bias in Oligonucleotide Array Data." Advances in Bioinformatics 2013 (March 13, 2013): 1–9. http://dx.doi.org/10.1155/2013/167915.

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Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target’s true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users’ current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias.

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Kim, Mingyu, and Jeongrae Kim. "SBAS-Aided GPS Positioning with an Extended Ionosphere Map at the Boundaries of WAAS Service Area." Remote Sensing 13, no. 1 (January 5, 2021): 151. http://dx.doi.org/10.3390/rs13010151.

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Space-based augmentation system (SBAS) provides correction information for improving the global navigation satellite system (GNSS) positioning accuracy in real-time, which includes satellite orbit/clock and ionospheric delay corrections. At SBAS service area boundaries, the correction is not fully available to GNSS users and only a partial correction is available, mostly satellite orbit/clock information. By using the geospatial correlation property of the ionosphere delay information, the ionosphere correction coverage can be extended by a spatial extrapolation algorithm. This paper proposes extending SBAS ionosphere correction coverage by using a biharmonic spline extrapolation algorithm. The wide area augmentation system (WAAS) ionosphere map is extended and its ionospheric delay error is compared with the GPS Klobuchar model. The mean ionosphere error reduction at low latitude is 52.3%. The positioning accuracy of the extended ionosphere correction method is compared with the accuracy of the conventional SBAS positioning method when only a partial set of SBAS corrections are available. The mean positioning error reduction is 44.8%, and the positioning accuracy improvement is significant at low latitude.

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8

Wu, Charley M., Eric Schulz, Mona M. Garvert, Björn Meder, and Nicolas W. Schuck. "Correction: Similarities and differences in spatial and non-spatial cognitive maps." PLOS Computational Biology 16, no. 10 (October 21, 2020): e1008384. http://dx.doi.org/10.1371/journal.pcbi.1008384.

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9

Koren, Lior, Yaniv Keren, and Mark Eidelman. "Multiplanar Deformities Correction Using Taylor Spatial Frame in Skeletally Immature Patients." Open Orthopaedics Journal 10, no. 1 (April 6, 2016): 71–79. http://dx.doi.org/10.2174/1874325001610010603.

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Background: Taylor Spatial Frame (TSF) is a modern circular external fixator that, using a virtual hinge, is able to correct six axis deformities simultaneously. Despite the growing popularity of this method, few reports exist about its use in children and adolescents. To evaluate the effectiveness of TSF in correcting multiplanar deformities in patients with open physis, we reviewed the results of treatment in children who had at least two planes deformities of lower limbs. Methods: Over a period of 8 years, we treated 51 patients, 40 boys, 11 girls, with a mean age of 12.4 years (range, 2-16 years). All patients had open physis at the time of the TSF application. All patients had at least two deformities (angular and/or rotational). Fifty-five osteotomies (11 femoral, 44 tibial) were performed. Patients were divided into four groups: 13 with post-traumatic malunions, 18 with tibia vara, six with rickets, and 14 with miscellaneous deformities. Correction goal was determined as correction of deformities to population-average parameters of the lower limbs in frontal and sagittal views and normal mechanical axis deviation. Results: Correction goal was achieved in all except one patient; four patients had recurrence of deformities post-operatively and were re-operated. Most common complications were pin tract infection (20 patients), delayed union (2), regenerate translation (1), post-removal femoral fractures (2), knee subluxation (1), nonunion (1), and one patient developed chronic osteomyelitis secondary to deep pin tract infection. Conclusion: TSF allowed accurate correction of complex limb deformities in children and adolescents with relatively few serious complications. Level of Evidence: Level IV. Case series.

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Vrac, Mathieu, and Petra Friederichs. "Multivariate—Intervariable, Spatial, and Temporal—Bias Correction*." Journal of Climate 28, no. 1 (December 31, 2014): 218–37. http://dx.doi.org/10.1175/jcli-d-14-00059.1.

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Abstract Statistical methods to bias correct global or regional climate model output are now common to get data closer to observations in distribution. However, most bias correction (BC) methods work for one variable and one location at a time and basically reproduce the temporal structure of the models. The intervariable, spatial, and temporal dependencies of the corrected data are usually poor compared to observations. Here, the authors propose a novel method for multivariate BC. The empirical copula–bias correction (EC–BC) combines a one-dimensional BC with a shuffling technique that restores an empirical multidimensional copula. Several BC methods are investigated and compared to high-resolution reference data over the French Mediterranean basin: notably, (i) a 1D BC method applied independently to precipitation and temperature fields, (ii) a recent conditional correction approach developed for producing correct two-dimensional intervariable structures, and (iii) the EC–BC method. Assessments are realized in terms of intervariable, spatial, and temporal dependencies, and an objective evaluation using the integrated quadratic distance (IQD) is presented. As expected, the 1D methods cannot produce correct multidimensional properties. The conditional technique appears efficient for intervariable properties but not for spatial and temporal dependencies. EC–BC provides realistic dependencies in all respects: intervariable, spatial, and temporal. The IQD results are clearly in favor of EC–BC. As many BC methods, EC–BC relies on a stationarity assumption and is only able to reproduce patterns inherited from historical data. However, because of its ease of coding, its speed of application, and the quality of its results, the EC–BC method is a very good candidate for all needs in multivariate bias correction.

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11

Shi Hailiang, 施海亮, 熊伟 Xiong Wei, 李志伟 Li Zhiwei, and 罗海燕 Luo Haiyan. "Phase Error Correction of Spatial Heterodyne Spectrometer." Acta Optica Sinica 33, no. 3 (2013): 0330003. http://dx.doi.org/10.3788/aos201333.0330003.

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12

Schad, Lothar, Sam Lott, Franz Schmitt, Volker Sturm, and Walter J. Lorenz. "Correction of Spatial Distortion in MR Imaging." Journal of Computer Assisted Tomography 11, no. 3 (May 1987): 499–505. http://dx.doi.org/10.1097/00004728-198705000-00025.

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13

Brodsky, Emily E. "Correction to “The spatial density of foreshocks”." Geophysical Research Letters 38, no. 13 (July 2011): n/a. http://dx.doi.org/10.1029/2011gl048369.

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Hinch, Scott G., Keith M. Somers, and Nicholas C. Coliins. "Spatial Autocorrelation and Assessment of Habitat–Abundance Relationships in Littoral Zone Fish." Canadian Journal of Fisheries and Aquatic Sciences 51, no. 3 (March 1, 1994): 701–12. http://dx.doi.org/10.1139/f94-070.

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Spatial autocorrelation, wherein intersite similarity is correlated with distance between sites, is a characteristic of most ecological studies spanning a large environmental range. If data are spatially autocorrelated, classical statistical techniques provide biased estimates of relationships between species attributes and environmental variables. We examined abundances of seven littoral fishes in 25 lakes that varied substantially in morphometry, chemistry, and elevation across central Ontario. Weak correlations were observed between abundances of particular species and environmental variables before correcting for spatial autocorrelation, and we hypothesized that correlations reflected species' habitat preferences. However, spatial autocorrelation existed in the abiotic and fish abundance datasets. Once large-scale geographic patterns (spatial autocorrelation) were removed using partial Mantel tests, correlations changed within and between datasets. A strong relationship emerged between abundances and lake elevation. By comparing patterns within geographically corrected data with those without correction, we identified particular species that exhibited spatially autocorrelated abundances. The geographic direction of spatial autocorrelation provided additional insights into environmental factors also correlating with species abundance. We recommend that ecologists examine both geographically corrected and noncorrected data when developing hypotheses to explain regional variation in species abundance.

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Sharma, D., A. Das Gupta, and M. S. Babel. "Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand." Hydrology and Earth System Sciences Discussions 4, no. 1 (January 17, 2007): 35–74. http://dx.doi.org/10.5194/hessd-4-35-2007.

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Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

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Sharma, D., A. Das Gupta, and M. S. Babel. "Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand." Hydrology and Earth System Sciences 11, no. 4 (June 20, 2007): 1373–90. http://dx.doi.org/10.5194/hess-11-1373-2007.

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Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

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Marujo, R. F. B., J. G. Fronza, A. R. Soares, G. R. Queiroz, and K. R. Ferreira. "EVALUATING THE IMPACT OF LASRC AND SEN2COR ATMOSPHERIC CORRECTION ALGORITHMS ON LANDSAT-8/OLI AND SENTINEL-2/MSI DATA OVER AERONET STATIONS IN BRAZILIAN TERRITORY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 271–77. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-271-2021.

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Abstract. Accurate and consistent Surface Reflectance estimation from optical remote sensor observations is directly dependant on the used atmospheric correction processor and the differences caused by it may have implications on further processes, e.g. classification. Brazil is a continental scale country with different biomes. Recently, new initiatives, as the Brazil Data Cube Project, are emerging and using free and open data policy data, more specifically medium spatial resolution sensor images, to create image data cubes and classify the Brazilian territory crops. For this reason, the purpose of this study is to verify, on Landsat-8 and Sentinel-2 images for the Brazilian territory, the suitability of the atmospheric correction processors maintained by their image providers, LaSRC from USGS and Sen2cor from ESA, respectively. To achieve this, we tested the surface reflectance products from Landsat-8 processed through LaSRC and Sentinel-2 processed through LaSRC and Sen2cor comparing to a reference dataset computed by ARCSI and AERONET. The obtained results point that Landsat-8/OLI images atmospherically corrected using the LaSRC corrector are consistent to the surface reflectance reference and other atmospheric correction processors studies, while for Sentinel-2/MSI images, Sen2cor performed best. Although corrections over Sentinel-2/MSI data weren’t as consistent as in Landsat-8/OLI corrections, in comparison to the surface reflectance references, most of the spectral bands achieved acceptable APU results.

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Andarab, Mehdi Solhi. "The Effect of Spatial Intelligence-based Metalinguistic Written Corrective Feedback on EFL Learners’ Development in Writing." Journal of Curriculum and Teaching 8, no. 1 (February 22, 2019): 40. http://dx.doi.org/10.5430/jct.v8n1p40.

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Correcting and providing feedback to the written work of the learners has always been one of the hotly-debatedissues over the last decades. While a group of scholars argue in favor of the effectiveness of the written corrective(CF) feedback, others question the utility and usefulness of the CF on writing of the learners. Even there seem to befewer consensuses on the typology of the CF. Metalinguistic written corrective feedback (CF) (e.g., briefgrammatical descriptions and error codes) is a type of written feedback, through which teacher gives metalinguisticclue to the nature of the errors (Ellis, 2009). In this study, a different type of metalinguistic feedback, conceptualizedas spatial intelligence-based (SIB) metalinguistic written CF_ using the colorful stationery to write, highlight, locate,or underline the linguistic errors of the learners while giving feedback_ was used while providing feedback to thelearner’s work. In order to investigate the effectiveness of SIB metalinguistic written CF on English as a foreignlanguage (EFL) learners’ development in writing, 47 intermediate learners were randomly assigned into two groups.The learners in the first group received SIB metalinguistic written CF for their errors in writing, while the ones in thesecond group only obtained metalinguistic written CF for their errors. An independent samples t-test applied on thescores achieved from a posttest showed a significant difference in scores of the first group and that of theexperimental group. Results indicated that the accuracy (mechanics) and style of the writing of the first group ofstudents who received SIB correction for their linguistic errors exceled that of the second group students whosereceived written correction was only metalinguistic. However, there was no significant difference between thegroups in the content, and organization of their writing.

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Ding Yi, 丁毅, 罗海燕 Luo Haiyan, 李志伟 Li Zhiwei, 施海亮 Shi Hailiang, 李思亮 Li Siliang та 熊伟 Xiong Wei. "时空联合调制型空间外差干涉成像仪运动误差评估与校正". Acta Optica Sinica 42, № 5 (2022): 0512007. http://dx.doi.org/10.3788/aos202242.0512007.

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Kim, Sangchoon. "Comments and Corrections Correction to “Transmit Antenna Selection for Precoding-Aided Spatial Modulation”." IEEE Access 8 (2020): 71302–3. http://dx.doi.org/10.1109/access.2020.2987477.

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Tang, Youbing, Shaofeng Xie, Liangke Huang, Lilong Liu, Pengzhi Wei, Yabo Zhang, and Chunyang Meng. "Spatial Estimation of Regional PM2.5 Concentrations with GWR Models Using PCA and RBF Interpolation Optimization." Remote Sensing 14, no. 21 (November 7, 2022): 5626. http://dx.doi.org/10.3390/rs14215626.

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In recent years, geographically weighted regression (GWR) models have been widely used to address the spatial heterogeneity and spatial autocorrelation of PM2.5, but these studies have not fully considered the effects of all potential variables on PM2.5 variation and have rarely optimized the models for residuals. Therefore, we first propose a modified GWR model based on principal component analysis (PCA-GWR), then introduce five different spatial interpolation methods of radial basis functions to correct the residuals of the PCA-GWR model, and finally construct five combinations of residual correction models to estimate regional PM2.5 concentrations. The results show that (1) the PCA-GWR model can fully consider the contributions of all potential explanatory variables to estimate PM2.5 concentrations and minimize the multicollinearity among explanatory variables, and the PM2.5 estimation accuracy and the fitting effect of the PCA-GWR model are better than the original GWR model. (2) All five residual correction combination models can better achieve the residual correction optimization of the PCA-GWR model, among which the PCA-GWR model corrected by Multiquadric Spline (MS) residual interpolation (PCA-GWRMS) has the most obvious accuracy improvement and more stable generalizability at different time scales. Therefore, the residual correction of PCA-GWR models using spatial interpolation methods is effective and feasible, and the results can provide references for regional PM2.5 spatial estimation and spatiotemporal mapping. (3) The PM2.5 concentrations in the study area are high in winter months (January, February, December) and low in summer months (June, July, August), and spatially, PM2.5 concentrations show a distribution of high north and low south.

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Zhao, Yuehua, Jiguang Zhang, Jie Ma, and Shibiao Xu. "Large-Scale Semantic Scene Understanding with Cross-Correction Representation." Remote Sensing 14, no. 23 (November 28, 2022): 6022. http://dx.doi.org/10.3390/rs14236022.

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Real-time large-scale point cloud segmentation is an important but challenging task for practical applications such as remote sensing and robotics. Existing real-time methods have achieved acceptable performance by aggregating local information. However, most of them only exploit local spatial geometric or semantic information dependently, few considering the complementarity of both. In this paper, we propose a model named Spatial–Semantic Incorporation Network (SSI-Net) for real-time large-scale point cloud segmentation. A Spatial-Semantic Cross-correction (SSC) module is introduced in SSI-Net as a basic unit. High-quality contextual features can be learned through SSC by correcting and updating high-level semantic information using spatial geometric cues and vice versa. Adopting the plug-and-play SSC module, we design SSI-Net as an encoder–decoder architecture. To ensure efficiency, it also adopts a random sample-based hierarchical network structure. Extensive experiments on several prevalent indoor and outdoor datasets for point cloud semantic segmentation demonstrate that the proposed approach can achieve state-of-the-art performance.

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Xu, Lingling, Wei Xiong, Weining Yi, Zhenwei Qiu, Xiao Liu, Dongying Zhang, Wei Fang, et al. "Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite." Remote Sensing 14, no. 17 (September 5, 2022): 4427. http://dx.doi.org/10.3390/rs14174427.

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Atmospheric conditions vary significantly in terms of the temporal and spatial scales. Therefore, it is critical to obtain atmospheric parameters synchronized with an image for atmospheric correction based on radiative transfer calculation methods. On 3 July 2020, the high resolution and multimode imaging satellite, Gao Fen Duo Mo (GFDM), which was the first civilian high-resolution remote sensing satellite equipped with the Synchronization Monitoring Atmospheric Corrector (SMAC), was launched. The SMAC is a multispectral and polarization detection device that is used to retrieve atmospheric parameters that are time-synchronized with the image sensor of GFDM in the same field-of-view. On the basis of the atmospheric parameters obtained from the SMAC, a synchronization atmospheric correction (Syn-AC) method is proposed to remove the influence of the atmosphere and the adjacency effects to retrieve the surface reflectance. The Syn-AC method was applied in the experiments of synchronous atmospheric correction for GFDM images, where the surface reflectance retrieved via the Syn-AC method was compared with the field-measured values. In addition, the classical correction method, the FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method. The results indicated that the image possessed better clarity and contrast with the blurring effect removed, and the multispectral reflectance was in agreement with the field-measured spectral reflectance. The deviations between the reflectance retrievals of Syn-AC and the field-measured values of the selected targets were within 0.0625, representing a higher precision than that of the FLAASH method (the max deviation was 0.2063). For the three sites, the mean relative error of Syn-AC was 19.3%, and the mean relative error of FLAASH was 76.6%. Atmospheric correction based on synchronous atmospheric parameters can improve the quantitative accuracy of remote sensing images, and it is meaningful for remote sensing applications.

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Wang Runhao, 王润昊, 孙影茹 Sun Yingru, 甘茵露 Gan Yinlu, 吴兴江 Wu Xingjiang, 柯俊杰 Ke Junjie, 王新强 Wang Xinqiang та 甘永莹 Gan Yongying. "空间外差连续光的非均匀误差校正". Laser & Optoelectronics Progress 59, № 19 (2022): 1930002. http://dx.doi.org/10.3788/lop202259.1930002.

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25

Lillo, Peter M., Lyle M. Pickett, Helena Persson, Oivind Andersson, and Sanghoon Kook. "Diesel Spray Ignition Detection and Spatial/Temporal Correction." SAE International Journal of Engines 5, no. 3 (April 16, 2012): 1330–46. http://dx.doi.org/10.4271/2012-01-1239.

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26

Wukich, Dane K., and Dekarlos Dial. "Equinovarus Deformity Correction With the Taylor Spatial Frame." Operative Techniques in Orthopaedics 16, no. 1 (January 2006): 18–22. http://dx.doi.org/10.1053/j.oto.2006.02.002.

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Iobst, Christopher. "Taylor Spatial Frame for Deformity Correction in Children." Operative Techniques in Orthopaedics 21, no. 2 (June 2011): 144–55. http://dx.doi.org/10.1053/j.oto.2011.01.002.

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Sontich, John K. "Posttraumatic Taylor Spatial Frame Deformity Correction in Adults." Operative Techniques in Orthopaedics 21, no. 2 (June 2011): 129–43. http://dx.doi.org/10.1053/j.oto.2011.01.012.

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29

Hart, Simon P., Jacob Usinowicz, and Jonathan M. Levine. "Publisher Correction: The spatial scales of species coexistence." Nature Ecology & Evolution 1, no. 9 (August 2, 2017): 1411. http://dx.doi.org/10.1038/s41559-017-0289-1.

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30

Sugar, Joshua D., Aron W. Cummings, Benjamin W. Jacobs, and David B. Robinson. "A Free Matlab Script for Spatial Drift Correction." Microscopy Today 22, no. 5 (August 29, 2014): 40–47. http://dx.doi.org/10.1017/s1551929514000790.

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31

Soysal, Ömer M., Kazim Şekeroglu, and Jeff Dickey. "An automated geo-spatial correction framework for transportation." Journal of Traffic and Transportation Engineering (English Edition) 6, no. 2 (April 2019): 147–61. http://dx.doi.org/10.1016/j.jtte.2018.01.007.

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32

Schaffer, Bernhard, Werner Grogger, and Gerald Kothleitner. "Automated spatial drift correction for EFTEM image series." Ultramicroscopy 102, no. 1 (December 2004): 27–36. http://dx.doi.org/10.1016/j.ultramic.2004.08.003.

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33

Liu, Zhongqiang, Åse Marit Wist Amdal, Jean-Sébastien L’Heureux, Suzanne Lacasse, Farrokh Nadim, and Xin Xie. "Correction: Spatial variability of medium dense sand deposit." AIMS Geosciences 6, no. 2 (2020): 149–50. http://dx.doi.org/10.3934/geosci.2020010.

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34

Englert, Christoph R., John M. Harlander, Joel G. Cardon, and Fred L. Roesler. "Correction of phase distortion in spatial heterodyne spectroscopy." Applied Optics 43, no. 36 (December 20, 2004): 6680. http://dx.doi.org/10.1364/ao.43.006680.

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35

Hairer, Martin, and Jan Maas. "A spatial version of the Itô–Stratonovich correction." Annals of Probability 40, no. 4 (July 2012): 1675–714. http://dx.doi.org/10.1214/11-aop662.

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36

Pickens, David R., Ronald R. Price, Jon J. Erickson, and A. Everette James. "Digital image motion correction by spatial warp methods." Medical Physics 14, no. 1 (January 1987): 56–61. http://dx.doi.org/10.1118/1.596095.

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37

SMITS, A. J., J. MONTY, M. HULTMARK, S. C. C. BAILEY, N. HUTCHINS, and I. MARUSIC. "Spatial resolution correction for wall-bounded turbulence measurements." Journal of Fluid Mechanics 676 (April 6, 2011): 41–53. http://dx.doi.org/10.1017/jfm.2011.19.

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A correction for streamwise Reynolds stress data acquired with insufficient spatial resolution is proposed for wall-bounded flows. The method is based on the attached eddy hypothesis to account for spatial filtering effects at all wall-normal positions. This analysis reveals that outside the near-wall region the spatial filtering effect scales inversely with the distance from the wall, in contrast to the commonly assumed scaling with the viscous length scale. The new formulation is shown to work very well for data taken over a wide range of Reynolds numbers and wire lengths.

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38

Li, Jia, Dan Zeng, Junjie Zhang, Jungong Han, and Tao Mei. "Column-Spatial Correction Network for Remote Sensing Image Destriping." Remote Sensing 14, no. 14 (July 13, 2022): 3376. http://dx.doi.org/10.3390/rs14143376.

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The stripe noise in the multispectral remote sensing images, possibly resulting from the instrument instability, slit contamination, and light interference, significantly degrades the imaging quality and impairs high-level visual tasks. The local consistency of homogeneous region in striped images is damaged because of the different gains and offsets of adjacent sensors regarding the same ground object, which leads to the structural characteristics of stripe noise. This can be characterized by the increased differences between columns in the remote sensing image. Therefore, the destriping can be viewed as a process of improving the local consistency of homogeneous region and the global uniformity of whole image. In recent years, convolutional neural network (CNN)-based models have been introduced to destriping tasks, and have achieved advanced results, relying on their powerful representation ability. Therefore, to effectively leverage both CNNs and the structural characteristics of stripe noise, we propose a multi-scaled column-spatial correction network (CSCNet) for remote sensing image destriping, in which the local structural characteristic of stripe noise and the global contextual information of the image are both explored at multiple feature scales. More specifically, the column-based correction module (CCM) and spatial-based correction module (SCM) were designed to improve the local consistency and global uniformity from the perspectives of column correction and full image correction, respectively. Moreover, a feature fusion module based on the channel attention mechanism was created to obtain discriminative features derived from different modules and scales. We compared the proposed model against both traditional and deep learning methods on simulated and real remote sensing images. The promising results indicate that CSCNet effectively removes image stripes and outperforms state-of-the-art methods in terms of qualitative and quantitative assessments.

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39

Tippett, Michael K., Anthony G. Barnston, David G. DeWitt, and Rong-Hua Zhang. "Statistical Correction of Tropical Pacific Sea Surface Temperature Forecasts." Journal of Climate 18, no. 23 (December 1, 2005): 5141–62. http://dx.doi.org/10.1175/jcli3581.1.

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Abstract This paper is about the statistical correction of systematic errors in dynamical sea surface temperature (SST) prediction systems using linear regression approaches. The typically short histories of model forecasts create difficulties in developing regression-based corrections. The roles of sample size, predictive skill, and systematic error are examined in evaluating the benefit of a linear correction. It is found that with the typical 20 yr of available model SST forecast data, corrections are worth performing when there are substantial deviations in forecast amplitude from that determined by correlation with observations. The closer the amplitude of the uncorrected forecasts is to the optimum squared error-minimizing amplitude, the less likely is a correction to improve skill. In addition to there being less “room for improvement,” this rule is related to the expected degradation in out-of-sample skill caused by sampling error in the estimate of the regression coefficient underlying the correction. Application of multivariate [canonical correlation analysis (CCA)] correction to three dynamical SST prediction models having 20 yr of data demonstrates improvement in the cross-validated skills of tropical Pacific SST forecasts through reduction of systematic errors in pattern structure. Additional beneficial correction of errors orthogonal to the CCA modes is achieved on a per-gridpoint basis for features having smaller spatial scale. Until such time that dynamical models become freer of systematic errors, statistical corrections such as those shown here can make dynamical SST predictions more skillful, retaining their nonlinear physics while also calibrating their outputs to more closely match observations.

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40

Wu, Jiansheng, Jingtian Liang, Liguo Zhou, Fei Yao, and Jian Peng. "Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China." Remote Sensing 11, no. 19 (September 24, 2019): 2223. http://dx.doi.org/10.3390/rs11192223.

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Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., ≥1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., <1 km) AOD and PM2.5 concentrations. Taking Shenzhen City, China as the study area, we derived AOD at the 16-m spatial resolution for the period 2015–2017 based on Gaofen-1 (GF-1) satellite images and the Dark Target (DT) algorithm. Then, we extracted AOD at spatial scales ranging from 40 m to 5000 m and applied vertical and humidity corrections. We analyzed the correlation between AOD and PM2.5 concentrations, and the impacts of AOD correction and spatial scale on the correlation. It was found that the DT-derived GF-1 AOD at different spatial scales had statistically significant correlations with surface PM2.5 concentrations, and the AOD corrections strengthened the correlations. The correlation coefficients (R) between AOD at different spatial scales and PM2.5 concentrations were 0.234–0.329 and 0.340–0.423 before and after AOD corrections, respectively. In spring, summer, autumn, and winter, PM2.5 concentrations had the best correlations with humidity-corrected AOD, uncorrected AOD, vertical and humidity-corrected AOD, and uncorrected AOD, respectively, indicating a distinct seasonal variation of the aerosol characteristics. At spatial scales of 1–5 km, AOD at finer spatial scales generally had higher correlations with PM2.5 concentrations. However, at spatial scales <1 km, the correlations fluctuated irregularly, which could be attributed to scale mismatches between AOD and PM2.5 measurements. Thus, 1 km appears to be the optimum spatial scale for DT-derived AOD to maximize the correlation with PM2.5 concentrations. It is also recommended to aggregate very high-resolution DT-derived AOD to an appropriate medium resolution (e.g., 1 km) before matching them with in situ PM2.5 measurements in regional air pollution studies.

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41

Li, Bin, Tianzhong Zhao, Xiaohui Su, Guangpeng Fan, Wenjie Zhang, Zhuo Deng, and Yonghui Yu. "Correction of Terrain Effects on Forest Canopy Height Estimation Using ICESat-2 and High Spatial Resolution Images." Remote Sensing 14, no. 18 (September 7, 2022): 4453. http://dx.doi.org/10.3390/rs14184453.

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The Ice, Cloud, and Land Elevation Satellite–2 (ICESat–2) carries the Advanced Topographic Laser Altimeter System (ATLAS), enabling global canopy height measurements from forest canopy height models (CHMs). Topographic slope is a crucial factor affecting the accuracy of canopy height estimates from ICESat–2 CHMs, but it has not been sufficiently studied. This paper aims to eliminate the influence of slope on canopy height estimates from ICESat–2 data and establishes a method for correcting forest canopy heights based on high spatial resolution digital orthophoto maps (DOM). The cross-track photons are corrected horizontally to eliminate the estimation error. Multi-resolution segmentation is used to segment tree crowns in the DOM, and the distance and relative position between the top of canopy (TOC) photons and the center point of the crown are calculated. TOC photon correction rules are established for different terrains, and the vertical error of the TOC photons is corrected. The results indicate that the vertical error increases exponentially with the slope. The cross-track photon correction and the TOC photon correction method eliminate the effect of slope on canopy height estimates. The cross-track photon correction method reduces the mean absolute error (MAE) and root mean square error (RMSE) of the canopy height estimates by 35.71% and 35.98%, respectively. The TOC photon correction approach further reduces the MAE and RMSE by 23% and 19.23%, respectively. The proposed method has significantly higher accuracy for forest canopy height estimation using ICESat–2 data than the traditional method.

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42

Lakew, Haileyesus Belay, and Semu Ayalew Moges. "Dynamical bias correction procedure to improve global gridded daily streamflow data for local application in the Upper Blue Nile basin." Journal of Hydrology and Hydromechanics 69, no. 1 (January 26, 2021): 41–48. http://dx.doi.org/10.2478/johh-2020-0040.

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Abstract Recently water resources reanalysis (WRR) global streamflow products are emerging from high- resolution global models as a means to provide long and consistent global streamflow products for assessment of global challenge such as climate change. Like any other products, the newly developed global streamflow products have limitations accurately represent the dynamics of local streamflow hydrographs. There is a need to locally evaluate and apply correction factors for better representation and make use of the data. This research focuses on the evaluation and correction of the bias embedded in the global streamflow product (WRR, 0.25°) developed by WaterGAP3 hydrological model in the upper Blue Nile basin part of Ethiopia. Three spatiotemporal dynamical bias correction schemes (temporal-spatial variable, temporal-spatial constant and spatial variable) tested in twelve watersheds of the basin. The temporal-spatial variable dynamical bias correction scheme significantly improves the streamflow estimation. The Nash-Sutcliffe coefficient (NSCE) improves by 30% and bias decreases by 19% for the twelve streamflow gauging stations applying leave one out cross-validation approach in turn. Therefore, the temporal-spatial variable scheme is applicable and can use as one method for the bias correction to use the global data for local applications in the upper Blue Nile basin.

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43

Fatima, Zainab, Maher A. Quraan, Natasa Kovacevic, and Anthony Randal McIntosh. "ICA-based artifact correction improves spatial localization of adaptive spatial filters in MEG." NeuroImage 78 (September 2013): 284–94. http://dx.doi.org/10.1016/j.neuroimage.2013.04.033.

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44

Hu, Denghui, and Yongsheng Xu. "A New Method of De-Aliasing Large-Scale High-Frequency Barotropic Signals in the Mediterranean Sea." Remote Sensing 12, no. 13 (July 6, 2020): 2157. http://dx.doi.org/10.3390/rs12132157.

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With the development of satellite observation technology, higher resolution and shorter return cycle have also placed higher demands on satellite data processing. The non-tide high-frequency barotropic oscillation in the marginal sea produces large aliasing errors in satellite altimeter observations. In previous studies, the satellite altimeter aliasing correction generally relied on a few bottom pressure data or the model data. Here, we employed the high-frequency tide gauge data to extract the altimeter non-tide aliasing correction in the west Mediterranean Sea. The spatial average method and EOF analysis method were adopted to track the high-frequency oscillation signals from 15 tide gauge records (TGs), and then were used to correct the aliasing errors in the Jason-1 and Envisat observations. The results showed that the EOF analysis method is better than the spatial average method in the altimeter data correction. After EOF correction, 90% of correlation (COR) between TG and sea level of Jason-1 has increased ~5%, and ~3% increase for the Envisat sea level; for the spatial average correction method, only ~70% of Jason-1 and Envisat data at the TGs location has about 2% increase in correlation. The EOF correction reduced the average percentage of error variance (PEL) by ~30%, while the spatial average correction increased the average percentage of PEL by ~20%. After correction by the EOF method, the altimeter observations are more consistent with the distribution of strong currents and eddies in the west Mediterranean Sea. The results prove that the proposed EOF method is more effective and accurate for the non-tide aliasing correction.

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45

Zhang, Hong Xin, and Xiao Xi Xu. "High-Resolution Correction of Arbitrary Wavefront Aberration Using Liquid Crystal Spatial Light Modulator." Applied Mechanics and Materials 121-126 (October 2011): 877–81. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.877.

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Wavefront correction plays significant role in some fields like astronomical observation, laser processing and medical imaging, etc. Liquid crystal spatial light modulator ( LC SLM) is an ideal device for high-resolution wavefront correction because of its low cost, low consumption, large number of pixels and independent programming control of each unit. It is researched experimentally that LC SLM is used as a wavefront correction device and corrects arbitrary wavefront aberration. Wavefront correction is performed based on phase conjugation and periodic phase modulation with modulo-2π. The experimental results show that the PV value of the irregular wavefront aberration is 1.56λ, RMS value is 0.25 and Strehl ratio is 0.08 before correction, but the PV value of the residual aberration is reduced to 0.26λ, RMS value is 0.02 and Strehl ratio is increased to 0.97 which is approximated diffraction limit after correction. It is proved to be feasible and effective that LC SLM is used to the high-precision and high-resolution wavefront correction.

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46

NOH, HYERIM. "WEAKLY NONLINEAR PERTURBATIONS OF COSMOLOGICAL FLUIDS: PURE GENERAL RELATIVISTIC EFFECTS." Modern Physics Letters A 23, no. 17n20 (June 28, 2008): 1606–13. http://dx.doi.org/10.1142/s0217732308028004.

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Recently we have shown that to the second-order perturbations, the density and velocity perturbation equations of general relativistic zero-pressure, irrotational, single-component fluid in a spatially flat background coincide exactly with the ones known in Newton's theory without using the gravitational potential. Here, we present results relaxing all the assumptions made in the previous works and present the general relativistic correction terms arising due to pressure, multi-component, background spatial curvature, and rotation. We show that even in the multi-component no general relativistic correction terms appear in the zero-pressure, irrotational fluids in a flat background. Thus, the relativistic/Newtonian correspondence of the density and velocity perturbations equations continues even in the multi-component situation. However, the pressure, background curvature, rotation lead to pure general relativistic correction terms to the second order. For the fluid including the pressure, the relativistic corrections appear even in the level of background and linear perturbation equations. In the small-scale limit, to the second order, relativistic equations of density and velocity perturbations including the rotation coincide with the ones in Newton's gravity. We include the cosmological constant in the background world model which is consistent with the currently favored cosmology.

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47

Xie, Kun, Wenguang Liu, Qiong Zhou, Zongfu Jiang, Fengjie Xi, and Xiaojun Xu. "Real-time phase measurement and correction of dynamic multimode beam using a single spatial light modulator." Chinese Optics Letters 18, no. 1 (2020): 011404. http://dx.doi.org/10.3788/col202018.011404.

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48

Pommerening, Arne, and Dietrich Stoyan. "Edge-correction needs in estimating indices of spatial forest structure." Canadian Journal of Forest Research 36, no. 7 (July 1, 2006): 1723–39. http://dx.doi.org/10.1139/x06-060.

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Indices quantifying spatial forest structure are frequently used to monitor spatial aspects of tree attributes including biodiversity in research plots of limited size. The treatment of edge trees, which are close to the plot boundaries, can affect the estimation of such indices that include neighbour effects, since some of their neighbours are likely to fall outside the plot. This paper investigates whether and under what circumstances edge-correction methods are necessary and evaluates the performance of six different approaches: no edge correction, translation, reflection, buffer zone, and two new nearest-neighbour methods. The performance of edge-correction methods depends strongly on the algorithmic structure of the indices and the spatial pattern of tree positions involved. Some edge-correction methods introduce more error than ignoring edge bias altogether. For indices accounting for the diversity of tree positions and especially for those computing angles, translation or buffer zone methods reduce the estimation error regardless of the sample size. The use of the reflection method is associated with large bias values. One of the new nearest-neighbour edge-correction methods proves to be capable of reducing the bias considerably. The results confirm the need for sufficiently large monitoring plots to avoid bias from edge effects. Where this is impossible, neighbours beyond the plot boundary need to be included in the survey, thus providing unbiased estimates but at the cost of extra measurements. Sensitivity analysis is required for newly introduced indices prior to their first application.

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49

Ding, Yuan Ming, Gang Xiang Guo, and Jin Wei Chen. "Research on Method of Serial Robot Relative Position Correction." Applied Mechanics and Materials 719-720 (January 2015): 405–10. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.405.

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For the problem of serial robot structure error, method of relative position correction was researched. An adaptive filtering correction method based on robot joint position feedback was proposed. The method built a robot joint adaptive filtering corrector (AFC) to correct robot joint feedback. The real spatial position of robot end-effector can be got through the forward kinematics computation with the corrected joint feedback. Thus, the robot structure error is corrected. Weights matrices of AFC were trained with relative positions between calibration points on the standard calibration module. The proposed method of relative positions correction provides a new way for serial robot structure error correction, which does not directly modify the robot kinematics design parameters, dose not need measuring equipment, and can be applied in field. The simulation results got with LabVIEW robot module show that the corrected trajectory of robot end-effector is more approaching to its true trajectory than before, which means the robot structure error is effectively corrected.Keywords: Correction; Relative position; Serial robot; Kinematics

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50

Hilton, James, and Nikhil Garg. "Rapid wind–terrain correction for wildfire simulations." International Journal of Wildland Fire 30, no. 6 (2021): 410. http://dx.doi.org/10.1071/wf20062.

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Modelling the propagation of wildfires requires an accurate wind field to correctly predict the behaviour of the fire. Although numerical weather prediction models produce reliable and accurate mesoscale forecasts, these are typically either available at a spatial resolution many times greater than the typical resolution of a wildfire model or a spot forecast that must be spatially interpolated to the area of the modelled wildfire. Due to this, these forecasts may not account for fine-scale terrain interactions with the wind and must be downscaled to a higher spatial resolution before use in a wildfire model. These downscaling methods are typically computationally intensive, limiting their use for situations where rapid predictions are required. Despite this, a three-dimensional mass balancing method is commonly used in wildfire prediction as a preprocessing step. In this study we show that this mass balancing method can be reduced to a two-dimensional approach, greatly reducing the complexity and computational time required for the model. The two-dimensional method is compared with the existing three-dimensional method and experimentally measured datasets. Furthermore, a combination of rapid numerical solution techniques and modern computational processors allow these wind–terrain correction methods to be directly incorporated into wildfire propagation models.

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