Numerical Simulation of Super-Resolution Structured Illumination Microscopy (SIM) Using Heintzmann-Cremer Algorithm with Non-Continuous Spatial Frequency Support

We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact of the diffraction limit of the underlying imaging system on the optimal SIM grating frequency that can be used to obtain the highest SR enhancement with non-continuous spatial frequency support. Besides confirming the previous theoretical and experimental work that SR-SIM can achieve an enhancement close to 3 times the diffraction limit with grating pattern illuminations, we also observe and report a series of more subtle effects of SR-SIM with non-continuous spatial frequency support. Our simulations show that when the SIM grating frequency exceeds twice that of the diffraction limit, the higher SIM grating frequency can help achieve a higher SR enhancement for the underlying imaging systems whose diffraction limit is low, though this enhancement is obtained at the cost of losing resolution at some lower resolution targets. Our simulations also show that, for underlying imaging systems with high diffraction limits, however, SR-SIM grating frequencies above twice the diffraction limits tend to bring no significant extra enhancement. Furthermore, we observed that there exists a limit grating frequency above which the SR enhancement effect is lost, and the reconstructed images essentially have the same resolution as the one obtained directly from the underlying imaging system without using the SIM process.

Effects of Different Spatial Resolutions on Prediction Accuracy of Thunnus alalunga Fishing Ground in Waters Near the Cook Islands Based on Long Short-Term Memory(LSTM)Neural Network Model

Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with different spatial resolutions,which leads to different results in tuna fishery prediction.Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources.The nominal catch per unit effort(CPUE)of albacore tuna is calculated according to vessel monitor system(VMS)data collected from Chinese distantwater fishery enterprises from January 1,2017 to May 31,2021.A total of 26 spatiotemporal and environmental factors,including temperature,salinity,dissolved oxygen of 0–300 m water layer,chlorophyll-a concentration in the sea surface,sea surface height,month,longitude,and latitude,were selected as variables.The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5°×0.5°,1°×1°,2°×2°,and 5°×5°.The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE,together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions.The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory(LSTM)neural network model.The mean absolute error(MAE)and root mean square error(RMSE)were used to analyze the fitness and accuracy of the models,and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground.The results show the resolution of 1°×1°can lead to the best prediction accuracy,with the MAE and RMSE being 0.0268 and 0.0452 respectively,followed by 0.5°×0.5°,2°×2°and 5°×5°with declining prediction accuracy.The results suggested that 1)albacore tuna fishing ground can be predicted by LSTM;2)the VMS records the data in detail and can be used scientifically to calculate the CPUE;3)correlation analysis,and multicollinearity diagnosis are necessary to improve the prediction accuracy of the model;4)the spatial resolution should be 1°×1°in the forecast of albacore tuna fishing ground in waters near the Cook Islands.

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.

A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries

Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.

Improved spatial resolution in soil moisture retrieval at arid mining area using apparent thermal inertia

A surface soil moisture model with improved spatial resolution was developed using remotely sensed apparent thermal inertia（ATI）.The model integrates the surface temperature derived from TM/ETM＋ image and the mean surface temperature from MODIS images to improve the spatial resolution of soil temperature difference based on the heat conduction equation,which is necessary to calculate the ATI.Consequently,the spatial resolution of ATI and SMC can be enhanced from 1 km to 120 m（TM） or 60m（ETM＋）.Moreover,the enhanced ATI has a much stronger correlation coefficient（R^2） with SMC（0.789） than the surface reflectance（0.108） or the ATI derived only from MODIS images（0.264）.Based on the regression statistics of the field SMC measurement and enhanced ATI,a linear regression model with an RMS error of 1.90%was found.

Resolution analysis of thermal neutron radiography based on accelerator-driven compact neutron source

Owing to the immobility of traditional reactors and spallation neutron sources,the demand for compact thermal neutron radiography(CTNR)based on accelerator neutron sources has rapidly increased in industrial applications.Recently,thermal neutron radiography experiments based on a D-T neutron generator performed by Hefei Institutes of Physical Science indicated a significant resolution deviation between the experimental results and the values calculated using the traditional resolution model.The experimental result was up to 23%lower than the calculated result,which hinders the achievement of the design goal of a compact neutron radiography system.A GEANT4 Monte Carlo code was developed to simulate the CTNR process,aiming to identify the key factors leading to resolution deviation.The effects of a low collimation ratio and high-energy neutrons were analyzed based on the neutron beam environment of the CTNR system.The results showed that the deviation was primarily caused by geometric distortion at low collimation ratios and radiation noise induced by highenergy neutrons.Additionally,the theoretical model was modified by considering the imaging position and radiation noise factors.The modified theoretical model was in good agreement with the experimental results,and the maximum deviation was reduced to 4.22%.This can be useful for the high-precision design of CTNR systems.

Areal density and spatial resolution of high energy electron radiography

Ultrafast imaging tools are of great importance for determining the dynamic density distribution in high energy density(HED)matter.In this work,we designed a high energy electron radiography(HEER)system based on a linear electron accelerator to evaluate its capability for imaging HED matter.40 MeV electron beams were used to image an aluminum target to study the density resolution and spatial resolution of HEER.The results demonstrate a spatial resolution of tens of micrometers.The interaction of the beams with the target and the beam transport of the transmitted electrons are further simulated with EGS5 and PARMELA codes,with the results showing good agreement with the experimental resolution.Furthermore,the experiment can be improved by adding an aperture at the Fourier plane.

Improving Simulations of Vegetation Dynamics over the Tibetan Plateau:Role of Atmospheric Forcing Data and Spatial Resolution

The efficacy of vegetation dynamics simulations in offline land surface models(LSMs)largely depends on the quality and spatial resolution of meteorological forcing data.In this study,the Princeton Global Meteorological Forcing Data(PMFD)and the high spatial resolution and upscaled China Meteorological Forcing Data(CMFD)were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics(SSiB4/TRIFFID)and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau(TP),a region with complex topography and sparse observations.By comparing the monthly Leaf Area Index(LAI)and Gross Primary Production(GPP)against observations,we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP,reducing RMSEs by 24.3%and 20.5%,respectively.The multi-year averaged GPP decreased from 364.68 gC m^(-2)yr^(-1)to 241.21 gC m^(-2)yr^(-1)with the percentage bias dropping from 50.2%to-1.7%.When using the high spatial resolution CMFD,the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5%and 9.5%,respectively.This study highlights the importance of more realistic and high-resolution forcing data in simulating vegetation growth and carbon exchange between the atmosphere and biosphere over the TP.

Study of spatial resolution of the associated alpha particle imaging–time-of-flight method

Associated alpha particle imaging based on the time-of-flight(API-TOF) technique is an advanced neutron analysis method, which is capable of discriminating material nuclides and three-dimensional imaging of the spatial distribution of material nuclei. In this paper, the spatial resolution of API-TOF and its effects are studied using mathematical analysis and Monte Carlo numerical simulation. The results can provide guidance and assist in designing of API-TOF detection devices. First, a mathematical analysis of the imaging principles of the API-TOF was carried out, and the calculation formulas of the spatial resolution of API-TOF were deduced. Next, the relationship between the device layout and the spatial resolution of the API-TOF detection device was studied. The concept of a typical API-TOF detection device with an optimized structure was proposed. Then, the spatial distribution of the spatial resolution of the typical API-TOF detection device was analyzed, and the effects of the time resolution and the neutron emission angle resolution on the spatial resolution were studied. The results show that spatial resolutions better than 1 cm can be achieved by improving the time resolution and the neutron emission angle resolution to appropriate levels. Finally, a Monte Carlo numerical simulation program was developed for the study of the APITOF and was used to calculate the spatial resolutions of the API-TOF. The comparison of the results shows that thespatial resolutions calculated based on the Monte Carlo numerical simulation are in good agreement with those calculated based on the mathematical analysis. This verifies the mathematical analysis and the evaluation of the effects of the spatial resolution of the API-TOF in this study.

A spatial resolution effect analysis of remote sensing bathymetry

A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion from different spatial resolution remote sensing images. The research experiments are divided into five groups including Quick Bird and World View-2 remote sensing images with their original spatial resolution（2.4/2.0 m）and four kinds of reducing spatial resolution（4, 8, 16 and 32 m）, and the water depth control and checking points are set up to carry out remote sensing water depth inversion. The experiment results indicate that the accuracy of the water depth remote sensing inversion increases first as the spatial resolution decreases from 2.4/2.0 to 4, 8 and16 m. And then the accuracy decreases along with the decreasing spatial resolution. When the spatial resolution of the image is 16 m, the inversion error is minimum. In this case, when the spatial resolution of the remote sensing image is 16 m, the mean relative errors（MRE） of Quick Bird and World View-2 bathymetry are 21.2% and 13.1%,compared with the maximum error are decreased by 14.7% and 2.9% respectively; the mean absolute errors（MAE） are 2.0 and 1.4 m, compared with the maximum are decreased by 1.0 and 0.5 m respectively. The results provide an important reference for the selection of remote sensing data in the study and application of the remote sensing bathymetry.

Monitoring of Karst Rocky Desertification Control Projects Based on Remote Sensing Images with Medium and High Spatial Resolution——A Case Study of Disi River Basin in Puan County

[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.

Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains,a national scenic spot in Nanjing,China

Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures.High spatial resolution remote sensing images can be used to detect subtle vegetation changes.The major objective of this study was to map and quantify forest vegetation changes in a national scenic location,the Purple Mountains of Nanjing,China,using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management.We used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in 2015.Four pixel-based direct change detection methods including the normalized difference vegetation index difference method,multi-index integrated change analysis(MIICA),principal component analysis,and spectral gradient difference analysis were compared in terms of their change detection performances.Subsequently,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes.An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results.The results showed that the MIICA method was the best pixel-based change detection method.And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA.From 2004 to 2009,areas of vegetation gain mainly occurred around the periphery of the study area,while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities,which contributed to 79%of the total area of vegetation loss.During 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of vegetation gain.In spite of this,vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas.We recommend that:(1)a local multi-agency team inspect and assess law enforcement regarding natural resource utilization;and(2)strengthen environmental awareness education.

Spatial resolution and image processing for pinhole camera-based X-ray fluorescence imaging: a simulation study

Spatial resolution and image-processing methods for full-field X-ray fluorescence(FF-XRF)imaging using X-ray pinhole cameras were studied using Geant4simulations with different geometries and algorithms for image reconstruction.The main objectives were:(1)calculating the quantum efficiency curves of specific cameras,(2)studying the relationships between the spatial resolution and the pinhole diameter,magnification,and camera binning value,and(3)comparing image-processing methods for pinhole camera systems.Several results were obtained using a point and plane source as the X-ray fluorescence emitter and an array of 100×100 silicon pixel detectors as the X-ray camera.The quantum efficiency of a back-illuminated deep depletion(BI-DD)structure was above 30%for the XRF energies in the 0.8–9 keV range,with the maximum of 93.7%at 4 keV.The best spatial resolution of the pinhole camera was 24.7μm and 31.3 lp/mm when measured using the profile function of the point source,with the diameter of 20μm,magnification of 3.16,and camera bin of 1.A blind deconvolution algorithm with Gaussian filtering performed better than the Wiener filter and Richardson iterative methods on FF-XRF images,with the signal-to-noise ratio of 7.81 dB and improved signalto-noise ratio of 7.24 dB at the diameter of 120μm,magnification of 1.0,and camera bin of 1.

High Spatial Resolution Mapping of Dykes Using Unmanned Aerial Vehicle(UAV) Photogrammetry: New Insights On Emplacement Processes

Remote sensing has played a pivotal role in our understanding of the geometry of dykes and dyke swarms on Earth,Venus and Mars(West and Ernst,1991;Mege and Masson,1995;Ernst et al.,2005).Since the 1970’s

Determination of spatial resolution of plastic scintillation fiber array with a simple method

The spatial resolution of a position sensitive gamma-ray detector configuration based on plastic scintillation fiber array was measured using a Monte Carlo simulation method. Both point spread function and modulation transfer function (MTF) were presented. The factors that influence the spatial resolution were also discussed. The results of the simulation showed that the intrinsic spatial resolution was consistent with the size of the physical pixels and a few centimeters spatial resolution could be obtained under certain circumstances.

CALCULATION OF THE SPATIAL RESOLUTION OF A SYNTHETIC APERTURE MICROWAVE RADIOMETER

The synthetic aperture microwave radiometer (SAMR) has been proved to be an interesting system for the remote sensing of the earth from space with a high spatial resolution. This paper presents the formulas for computing the spatial resolution of the SAMR. Some practical examples are also presented, and it is proved that the spatial resolution becomes more inferior with the increment of the off-nadir angle.

Feasibility of High Spatial Resolution Working Modes for Clinical PET Scanner

Contemporary PET scanners for clinical use have spatial-resolution of 4 - 5 mm, caused by fundamental factors in medical imaging: detector sizes, free path of positrons, and non-colinearity uncertainty of annihilation photon-pairs. The drawback in resolution significantly restrained the sensitivity of PET in imaging small lesions, which could be either early-stage cancers or small metastasis. In this study, the principle for a novel scanning mode to acquire high spatial-resolution images is proposed for clinical PET scanners. The concept of equivalent position was first proposed as different angular orientations of the scanner ring, at which comparable images could be achieved. Due to this concept, a typical static PET scan can be separated into m (m ≥ 2) equivalent sub-scans at different equivalent positions, when the scanner ring is systematically adjusted to m equivalent-positions of equal distance within one detector size. In this case each detector is virtually divided into m equal sub-detectors, without physical minimizing the detector size, and imaging contributions from every 1/m part of the detector can be determined by an analytically matrix, since there are m variables and m sub-scans. This novel concept is quite feasible to contemporary design because the high spatial resolution working modes (m ≥ 2) only demand the scanner to be slightly adjustable to other angular orientations. Adding high spatial resolutions modes to the scanner only has trifling influence on contrast resolutions as all imaging events at each sub-scan are independent. The time for performing a high-resolution scan could be comparable to a typical PET scan, as long as the Poisson noises are insignificant to low-uptake voxels. As a result, for a typical scanner design e.g. 80 cm in diameter with 18F as tracers, the spatial resolution of double sub-scans (m = 2) is 2.56 mm, and 2.19 mm for triple sub-scans (m = 3), which are significant improvements. The novelty of high spatial resolution design is compatible to digital PET or any other technological evolutions.

Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN

Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition&#174. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.

A stopping layer concept to improve the spatial resolution of gas-electron-multiplier neutron detector

In recent years,gas electron multiplier(GEM)neutron detectors have been developing towards high spatial resolution and high dynamic counting range.We propose a novel concept of an Al stopping layer to enable the detector to achieve sub-millimeter(sub-mm)spatial resolution.The neutron conversion layer is coated with the Al stopping layer to limit the emission angle of ions into the drift region.The short track projection of ions is obtained on the signal readout board,and the detector would get good spatial resolution.The spatial resolutions of the GEM neutron detector with the Al stopping layer are simulated and optimized based on Geant4 Garfield Interface.The spatial resolution of the detector is 0.76 mm and the thermal neutron detection efficiency is about 0.01%when the Al stopping layer is 3.0μm thick,the drift region is 2 mm thick,the strip pitch is 600μm,and the digital readout is employed.Thus,the GEM neutron detector with a simple detector structure and a fast readout mode is developed to obtain a high spatial resolution and high dynamic counting range.It could be used for the direct measurement of a high-flux neutron beam,such as Bragg transmission imaging,very small-angle scattering neutron detection and neutron beam diagnostic.