Temporal Resolution Images Research Articles

A Flexible Image-Guided Shape Reconstruction Framework for Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) owns the advantages of safety, high temporal resolution and functional imaging characteristics, thus is considered as a promising medical/ industrial imaging modality. However, due to the ill-posedness of its inverse problem, the spatial resolution of EIT is low, which is greatly impeded its practical applications. A flexible image-guided shape reconstruction framework for EIT is proposed to alleviate the problem. A statistical inverse problem framework for image-guided inclusion boundary reconstruction is established to directly reconstruct the inclusion boundary and the corresponding conductivity distribution. The morphology prior information is extracted from the images obtained from other high resolution modes and is used to guide the boundary reconstruction process in the form of a conditional probability model. A series of simulation and experimental studies are carried out for different application backgrounds. The lung EIT imaging guided by CT image and EIT inclusion detection guided by B-ultrasound image are simulated and analyzed respectively. The results show that the proposed method has high applicability to prior images of different modes. It not only achieves high-precision shape reconstruction but also high-precision conductivity estimation under the guidance of accurate prior images. Meanwhile, the proposed method has high robustness to the possible influence of noise in prior images or incomplete structural information. Even if the information in the prior image is biased or incomplete, the proposed method can still achieve high precision boundary reconstruction and conductivity estimation. The results of quantitative analysis show that compared with the method without image prior, the proposed method has an average reduction of 70% in area error, while the conductivity estimation error is reduced by about 5 times on average.

Enhanced Spatiotemporal Fusion via MODIS-Like Images

Spatiotemporal fusion (STF) aims at generating remote-sensing data with both high spatial and temporal resolution. In the literature, one of the most widely used strategies to accomplish this goal is to fuse high temporal resolution images collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) with images with finer spatial resolution than those provided by MODIS (e.g., those collected by other satellite instruments such as Landsat or Sentinel-2). Current STF methods generally fuse an upsampled MODIS image with finer spatial resolution images. This leads to two main problems. First of all, the model uncertainty errors (resulting from the ill-posed upsampling problem) will be propagated into the fusion results, leading to spatial and spectral distortion. Furthermore, the spatial details of the upsampled MODIS image may be significantly different from those of the finer spatial resolution images, making the STF problem even more challenging. In order to tackle these issues, in this work, we develop a new linear regression-based STF strategy (LiSTF), which performs the reconstruction from a MODIS-like image (instead of from an upsampled MODIS image), thus reducing the model uncertainty errors and preserving better the spatial information. The MODIS-like images are built from the finer spatial resolution images via downsampling. Our experimental results, conducted using two publicly available datasets of Landsat–MODIS image pairs and one publicly available dataset of Sentinel–MODIS image pairs, reveal that our newly proposed LiSTF approach can significantly enhance the quantitative and qualitative performance of STF, particularly in terms of preserving the spatial information.

RFSDAF: A New Spatiotemporal Fusion Method Robust to Registration Errors

Spatiotemporal fusion is commonly used in remote sensing to generate fine spatial–temporal resolution images. Among most present spatiotemporal fusion methods, the registration error results in a mismatch in the reflectance and class abundance information within the MODIS pixel to be analyzed during the spatiotemporal fusion. In this article, a new spatiotemporal fusion model, robust flexible spatiotemporal data fusion (RFSDAF), is proposed; in other words, this method is robust to registration errors. The RFSDAF method uses a multiscale fusion strategy that is adaptive to different degrees of coregistration error. It incorporates multiscale information, which extends the analysis of the reflectance and class fraction from per-MODIS pixel to inter-MODIS pixels, and it is robust to coregistration errors. This method does not require high-accuracy preprocession coregistration algorithms and can automatically reduce the effect of the registration error to a great extent. The RFSDAF method is compared with four spatiotemporal image fusion algorithms, and the effectiveness of the method in resolving the registration error is demonstrated using both a simulated dataset and two actual satellite datasets. The experimental results show that the RFSDAF method can better reduce the impact of the registration error than the spatial and temporal adaptive reflectance fusion model (STARFM) and FSDAF, which adopts the phase cross-correlation algorithm to preprocess and coregister the MODIS–Landsat image through real image experiments. Consequently, the RFSDAF method has a great robustness for real spatiotemporal image fusion with registration errors and has the potential for monitoring land surface dynamics.

Subpixel Change Detection Based on Improved Abundance Values for Remote Sensing Images

To achieve land cover change detection (LCCD) with both fine spatial and temporal resolutions from remote sensing images, subpixel mapping-based approaches have been widely studied in recent years. The fine spatial but coarse temporal resolution image and the coarse spatial but fine temporal image are used to accomplish LCCD by combining their advantages. However, the performance of subpixel mapping is easily affected by the accuracy of spectral unmixing, thereby reducing the reliability of LCCD. In this paper, a novel subpixel change detection scheme based on improved abundance values is proposed to tackle the aforementioned problem, in which the spatial distribution of fine spatial resolution image is borrowed to promote the accuracy of spectral unmixing. First, the coarse spatial resolution image is used to generate the original abundance image by the spectral unmixing method. Second, the spatial distribution information of the fine spatial resolution image is incorporated into the original abundance image to obtain improved abundance values. Third, the fine spatial resolution subpixel map can be generated by the subpixel mapping method using the improved abundance values. At last, the fine resolution change map can be obtained by comparing the subpixel map with the fine spatial resolution image. Experiments are conducted on a simulated dataset based on Landsat-7 images and two real datasets based on Landsat-8 and MODIS images. The results of the real datasets showed that the proposed method can effectively improve the performance of LCCD with an overall accuracy of approximately 1.26% and 0.79% to the existing methods.

Assessing the reproducibility of high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging in patients with gliomas

Temporal and spatial resolution of dynamic contrast-enhanced MR imaging (DCE-MRI) is critical to reproducibility, and the reproducibility of high-resolution (HR) DCE-MRI was evaluated. Thirty consecutive patients suspected to have brain tumors were prospectively enrolled with written informed consent. All patients underwent both HR-DCE (voxel size, 1.1 × 1.1 × 1.1 mm3; scan interval, 1.6 s) and conventional DCE (C-DCE; voxel size, 1.25 × 1.25 × 3.0 mm3; scan interval, 4.0 s) MRI. Regions of interests (ROIs) for enhancing lesions were segmented twice in each patient with glioblastoma (n = 7) to calculate DCE parameters (Ktrans, Vp, and Ve). Intraclass correlation coefficients (ICCs) of DCE parameters were obtained. In patients with gliomas (n = 25), arterial input functions (AIFs) and DCE parameters derived from T2 hyperintense lesions were obtained, and DCE parameters were compared according to WHO grades. ICCs of HR-DCE parameters were good to excellent (0.84–0.95), and ICCs of C-DCE parameters were moderate to excellent (0.66–0.96). Maximal signal intensity and wash-in slope of AIFs from HR-DCE MRI were significantly greater than those from C-DCE MRI (31.85 vs. 7.09 and 2.14 vs. 0.63; p < 0.001). Both 95th percentile Ktrans and Ve from HR-DCE and C-DCE MRI could differentiate grade 4 from grade 2 and 3 gliomas (p < 0.05). In conclusion, HR-DCE parameters generally showed better reproducibility than C-DCE parameters, and HR-DCE MRI provided better quality of AIFs.

Dynamic Optical Visualization of Proton Transport Pathways at Water-Solid Interfaces.

Probing proton transport is of vital importance for understanding cellular transport, surface catalysis and fuel cells. Conventional proton transport measurements rely on the use of electrochemical conductivity and do not allow for the direct visualization of proton transport pathways. The development of novel experimental techniques to spatiotemporally resolve proton transport is in high demand. Here, building upon the general conversion of aqueous proton flux into spatially resolved fluorescence signals, we optically visualize proton transport through nanopores and along hydrophilic interfaces. We observed that the fluorescence intensity increased at negative voltage due to lateral transport. Thanks to the temporal resolution of optical imaging, our technique further empowers the analysis of proton transport dynamics.

Dynamic Optical Visualization of Proton Transport Pathways at Water–Solid Interfaces

AbstractProbing proton transport is of vital importance for understanding cellular transport, surface catalysis and fuel cells. Conventional proton transport measurements rely on the use of electrochemical conductivity and do not allow for the direct visualization of proton transport pathways. The development of novel experimental techniques to spatiotemporally resolve proton transport is in high demand. Here, building upon the general conversion of aqueous proton flux into spatially resolved fluorescence signals, we optically visualize proton transport through nanopores and along hydrophilic interfaces. We observed that the fluorescence intensity increased at negative voltage due to lateral transport. Thanks to the temporal resolution of optical imaging, our technique further empowers the analysis of proton transport dynamics.

A comparison of the integrated fuzzy object-based deep learning approach and three machine learning techniques for land use/cover change monitoring and environmental impacts assessment

ABSTRACT Recent improvements in the spatial, temporal, and spectral resolution of satellite images necessitate (semi-)automated classification and information extraction approaches. Therefore, we developed an integrated fuzzy object-based image analysis and deep learning (FOBIA-DL) approach for monitoring the land use/cover (LULC) and respective changes and compared it to three machine learning (ML) algorithms, namely the support vector machine (SVM), random forest (RF), and classification and regression tree (CART). We investigated LULC impacts on drought by analyzing Landsat satellite images from 1990 to 2020 for the Urmia Lake area in northern Iran. In the FOBIA-DL approach, following the initial segmentation steps, object features were identified for each LULC class. We then derived their respective attributes using fuzzy membership functions and deep convolutional neural networks (DCNNs), a deep learning method. The Fuzzy Synthetic Evaluation and Dempster-Shafer Theory (FSE-DST) also applied to validate and carryout the spatial uncertainties. Our results indicate that the FOBIA-DL, with an accuracy of 90.1% to 96.4% and a spatial certainty of 0.93 to 0.97, outperformed the other approaches, closely followed by the SVM. Our results also showed that the integration of Fuzzy-OBIA and DCNNs could improve the strength and robustness of the OBIA’s decision rules, while the FSE-DST approach notably improved the spatial accuracy of the object-based classification maps. While object-based image analysis (OBIA) is already considered a paradigm shift in GIScience, the integration of OBIA with fuzzy and deep learning creates more flexibility and robust OBIA decision rules for image analysis and classification. This research integrated popular data-driven approaches and developed a novel methodology for image classification and spatial accuracy assessment. From the environmental perspective, the results of this research support lake restoration initiatives by decision-makers and authorities in applications such as drought mitigation, land use management and precision agriculture programs.

The high-energy Sun - probing the origins of particle acceleration on our nearest star

As a frequent and energetic particle accelerator, our Sun provides us with an excellent astrophysical laboratory for understanding the fundamental process of particle acceleration. The exploitation of radiative diagnostics from electrons has shown that acceleration operates on sub-second time scales in a complex magnetic environment, where direct electric fields, wave turbulence, and shock waves all must contribute, although precise details are severely lacking. Ions were assumed to be accelerated in a similar manner to electrons, but γ-ray imaging confirmed that emission sources are spatially separated from X-ray sources, suggesting distinctly different acceleration mechanisms. Current X-ray and γ-ray spectroscopy provides only a basic understanding of accelerated particle spectra and the total energy budgets are therefore poorly constrained. Additionally, the recent detection of relativistic ion signatures lasting many hours, without an electron counterpart, is an enigma. We propose a single platform to directly measure the physical conditions present in the energy release sites and the environment in which the particles propagate and deposit their energy. To address this fundamental issue, we set out a suite of dedicated instruments that will probe both electrons and ions simultaneously to observe; high (seconds) temporal resolution photon spectra (4 keV – 150 MeV) with simultaneous imaging (1 keV – 30 MeV), polarization measurements (5–1000 keV) and high spatial and temporal resolution imaging spectroscopy in the UV/EUV/SXR (soft X-ray) regimes. These instruments will observe the broad range of radiative signatures produced in the solar atmosphere by accelerated particles.

Motion Extraction of the Right Ventricle from 4D Cardiac Cine MRI Using A Deep Learning-Based Deformable Registration Framework.

Cardiac Cine Magnetic Resonance (CMR) Imaging has made a significant paradigm shift in medical imaging technology, thanks to its capability of acquiring high spatial and temporal resolution images of different structures within the heart that can be used for reconstructing patient-specific ventricular computational models. In this work, we describe the development of dynamic patient-specific right ventricle (RV) models associated with normal subjects and abnormal RV patients to be subsequently used to assess RV function based on motion and kinematic analysis. We first constructed static RV models using segmentation masks of cardiac chambers generated from our accurate, memory-efficient deep neural architecture - CondenseUNet - featuring both a learned group structure and a regularized weight-pruner to estimate the motion of the right ventricle. In our study, we use a deep learning-based deformable network that takes 3D input volumes and outputs a motion field which is then used to generate isosurface meshes of the cardiac geometry at all cardiac frames by propagating the end-diastole (ED) isosurface mesh using the reconstructed motion field. The proposed model was trained and tested on the Automated Cardiac Diagnosis Challenge (ACDC) dataset featuring 150 cine cardiac MRI patient datasets. The isosurface meshes generated using the proposed pipeline were compared to those obtained using motion propagation via traditional non-rigid registration based on several performance metrics, including Dice score and mean absolute distance (MAD).

Time-Dependent Image Restoration of Low-SNR Live-Cell Ca2 Fluorescence Microscopy Data.

Live-cell Ca fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes on the subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca microdomains in T-cells; finally, to illustrate the transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.

Dipeptide Nanostructure Assembly and Dynamics via in Situ Liquid-Phase Electron Microscopy.

In this paper, we report the in situ growth of FF nanotubes examined via liquid-cell transmission electron microscopy (LCTEM). This direct, high spatial, and temporal resolution imaging approach allowed us to observe the growth of peptide-based nanofibrillar structures through directional elongation. Furthermore, the radial growth profile of FF nanotubes through the addition of monomers perpendicular to the tube axis has been observed in real-time with sufficient resolution to directly observe the increase in diameter. Our study demonstrates that the kinetics, dynamics, structure formation, and assembly mechanism of these supramolecular assemblies can be directly monitored using LCTEM. The performance of the peptides and the assemblies they form can be verified and evaluated using post-mortem techniques including time-of-flight secondary ion mass spectrometry (ToF-SIMS).

Volumetric prediction of breathing and slow drifting motion in the abdomen using radial MRI and multi-temporal resolution modeling

Abdominal organ motions introduce geometric uncertainties to radiotherapy. This study investigates a multi-temporal resolution 3D motion prediction scheme that accounts for both breathing and slow drifting motion in the abdomen in support of MRI-guided radiotherapy. Ten-minute MRI scans were acquired for 8 patients using a volumetric golden-angle stack-of-stars sequence. The first five-minutes was used for patient-specific motion modeling. Fast breathing motion was modeled from high temporal resolution radial k-space samples, which served as a navigator signal to sort k-space data into different bins for high spatial resolution reconstruction of breathing motion states. Slow drifting motion was modeled from a lower temporal resolution image time series which was reconstructed by sequentially combining a large number of breathing-corrected k-space samples. Principal components analysis (PCA) was performed on deformation fields between different motion states. Gaussian kernel regression and linear extrapolation were used to predict PCA coefficients of future motion states for breathing motion (340 ms ahead of acquisition) and slow drifting motion (8.5 s ahead of acquisition) respectively. k-space data from the remaining five-minutes was used to compare ground truth motions states obtained from retrospective reconstruction/deformation with predictions. Median distances between predicted and ground truth centroid positions of gross tumor volume (GTV) and organs at risk (OARs) were less than 1 mm on average. 95- percentile Hausdorff distances between predicted and ground truth GTV contours of various breathing motions states were 2 mm on average, which was smaller than the imaging resolution and 95-percentile Hausdorff distances between predicted and ground truth OAR contours of different slow drifting motion states were less than 0.2 mm. These results suggest that multi-temporal resolution motion models are capable of volumetric predictions of breathing and slow drifting motion with sufficient accuracy and temporal resolution for MRI-based tracking, and thus have potential for supporting MRI-guided abdominal radiotherapy.

Assessment of FSDAF Accuracy on Cotton Yield Estimation Using Different MODIS Products and Landsat Based on the Mixed Degree Index with Different Surroundings

Research on fusion modeling of high spatial and temporal resolution images typically uses MODIS products at 500 m and 250 m resolution with Landsat images at 30 m, but the effect on results of the date of reference images and the ‘mixed pixels’ nature of moderate-resolution imaging spectroradiometer (MODIS) images are not often considered. In this study, we evaluated those effects using the flexible spatiotemporal data fusion model (FSDAF) to generate fusion images with both high spatial resolution and frequent coverage over three cotton field plots in the San Joaquin Valley of California, USA. Landsat images of different dates (day-of-year (DOY) 174, 206, and 254, representing early, middle, and end stages of the growing season, respectively) were used as reference images in fusion with two MODIS products (MOD09GA and MOD13Q1) to produce new time-series fusion images with improved temporal sampling over that provided by Landsat alone. The impact on the accuracy of yield estimation of the different Landsat reference dates, as well as the degree of mixing of the two MODIS products, were evaluated. A mixed degree index (MDI) was constructed to evaluate the accuracy and time-series fusion results of the different cotton plots, after which the different yield estimation models were compared. The results show the following: (1) there is a strong correlation (above 0.6) between cotton yield and both the Normalized Difference Vegetation Index (NDVI) from Landsat (NDVIL30) and NDVI from the fusion of Landsat with MOD13Q1 (NDVIF250). (2) Use of a mid-season Landsat image as reference for the fusion of MODIS imagery provides a better yield estimation, 14.73% and 17.26% higher than reference images from early or late in the season, respectively. (3) The accuracy of the yield estimation model of the three plots is different and relates to the MDI of the plots and the types of surrounding crops. These results can be used as a reference for data fusion for vegetation monitoring using remote sensing at the field scale.

Experimental investigations on transient dynamics of cryogenic cavitating flows under different free-stream conditions

The objective of this study is to investigate the transient dynamics of liquid nitrogen cavitating flows under a wide range of free-stream conditions. The temporal and spatial resolution of cavity images was improved to analyze in detail the cavity structures and unsteady behaviors. The Proper orthogonal decomposition (POD) method based on the clear experimental images was applied to investigate the spatial structures. The unsteady evolution of discrete bubbles at different temperatures was compared to analyze the effects of heat transfer on the collapse process. The results show that: (1) as the cavitation number increases, four typical cavitation patterns through the convergent-divergent (C-D) nozzle, namely choke cavity, large-scale cloud cavity, sheet cavity and incipient cavity are observed. (2) As the temperature increases, the significant re-entrant jet becomes invisible, and the large-scale cloud cavity is absent. The contributions of the lowest POD modes decrease with the increasing temperature. The dominant POD structure changes from a single large area to several counter-rotating structures with increasing temperature. This reflects the thermal effects significantly change the shedding behaviors of the detached cloud cavities. (3) The significant thermal effects decrease the condensation rates of the discrete cavity bubbles. The collapse mechanisms of the discrete bubbles in the inertial cavitation and thermal cavitation are different. For the inertial cavitation, the condensation rates of cloud cavities and the discrete bubbles are close. For the thermal cavitation, the condensation rates of cloud cavities are much larger than that of a single bubble.

Effects of air quality and vegetation on algal bloom early warning systems in large lakes in the middle–lower Yangtze River basin

Studies of algal bloom early warning systems have rarely paid attention to the dynamics of excessive proliferation of phytoplankton (EPP), which occurs prior to algal blooms, or to the sensitivity of a lake to EPP based on multiple environmental factors. In this study, we investigated EPP dynamics in large lakes and identified major factors that influenced the lake's vulnerability to EPP, to improve algal bloom early warning systems. High temporal moderate resolution imaging spectroradiometer (MODIS) images and multi-source daily site monitoring data of large lakes in the middle–lower Yangtze River basin were analyzed. Then, the floating algal index (FAI) and resource use efficiency (RUE) by phytoplankton were used to investigate the EPP dynamics and lake's vulnerability to EPP, respectively. Moreover, generalized linear models were used to assess the relative importance of environmental factors on RUE. The results indicate that the lakes freely connected (FC) to the Yangtze River (Dongting Lake and Poyang Lake) had lower FAIs but higher RUEs than the non-connected lakes (NC; Chaohu Lake and Taihu Lake). The key factors affecting RUE-FC were standard deviation of water level within 30 days(WL30), particulate matter <10 μm(PM10), and relative humidity(Hum), which explained 15.91% of the variations in RUE. The key factors affecting RUE-NC were ozone(O3), basin normalized difference vegetation index standard deviation(BNDVISD), and dissolved oxygen(DO), which explained 35.28% of the variations in RUE. These results emphasize the importance of air quality in influencing or reflecting EPP risks in large lakes. In addition, basin vegetation and hydrological rhythms can influence NH4+ through non-point source loading. Algal bloom early warning systems can be improved by routine monitoring and forecasting of potential environmental factors such as air quality and basin vegetation.

Spatial and temporal super-resolution for fluorescence microscopy by a recurrent neural network.

A novel spatial and temporal super-resolution (SR) framework based on a recurrent neural network (RNN) is demonstrated. In this work, we learn the complex yet useful features from the temporal data by taking advantage of structural characteristics of RNN and a skip connection. The usage of supervision mechanism is not only making full use of the intermediate output of each recurrent layer to recover the final output, but also alleviating vanishing/exploding gradients during the back-propagation. The proposed scheme achieves excellent reconstruction results, improving both the spatial and temporal resolution of fluorescence images including the simulated and real tubulin datasets. Besides, robustness against various critical metrics, such as the full-width at half-maximum (FWHM) and molecular density, can also be incorporated. In the validation, the performance can be increased by more than 20% for intensity profile, and 8% for FWHM, and the running time can be saved at least 40% compared with the classic Deep-STORM method, a high-performance net which is popularly used for comparison.

Near-surface real-time seismic imaging using parsimonious interferometry

Results are presented for real-time seismic imaging of subsurface fluid flow by parsimonious refraction and surface-wave interferometry. Each subsurface velocity image inverted from time-lapse seismic data only requires several minutes of recording time, which is less than the time-scale of the fluid-induced changes in the rock properties. In this sense this is real-time imaging. The images are P-velocity tomograms inverted from the first-arrival times and the S-velocity tomograms inverted from dispersion curves. Compared to conventional seismic imaging, parsimonious interferometry reduces the recording time and increases the temporal resolution of time-lapse seismic images by more than an order-of-magnitude. In our seismic experiment, we recorded 90 sparse data sets over 4.5 h while injecting 12-tons of water into a sand dune. Results show that the percolation of water is mostly along layered boundaries down to a depth of a few meters, which is consistent with our 3D computational fluid flow simulations and laboratory experiments. The significance of parsimonious interferometry is that it provides more than an order-of-magnitude increase of temporal resolution in time-lapse seismic imaging. We believe that real-time seismic imaging will have important applications for non-destructive characterization in environmental, biomedical, and subsurface imaging.

2-D Ultrasonic Array-Based Optical Coherence Elastography.

Acoustic radiation force optical coherence elastography (ARF-OCE) has been successfully implemented to characterize the biomechanical properties of soft tissues, such as the cornea and the retina, with high resolution using single-element ultrasonic transducers for ARF excitation. Most currently proposed OCE techniques, such as air puff and ARF, have less capability to control the spatiotemporal information of the induced region of deformation, resulting in limited accuracy and low temporal resolution of the shear wave elasticity imaging. In this study, we propose a new method called 2-D ultrasonic array-based OCE imaging, which combines the advantages of 3-D dynamic electronic steering of the 2-D ultrasonic array and high-resolution optical coherence tomography (OCT). The 3-D steering capability of the 2-D array was first validated using a hydrophone. Then, the combined 2-D ultrasonic array OCE system was calibrated using a homogenous phantom, followed by an experiment on ex vivo rabbit corneal tissue. The results demonstrate that our newly developed 2-D ultrasonic array-based OCE system has the capability to map tissue biomechanical properties accurately, and therefore, has the potential to be a vital diagnostic tool in ophthalmology.

Automated estimation of daily surface water fraction from MODIS and Landsat images using Gaussian process regression

ABSTRACT Satellite remote sensing has been widely used to monitor surface water, but its application in observing rapid inundation changes remains challenging. Observations relying on only one sensor could hardly achieve both high temporal and high spatial resolutions. High spatial resolution images are not frequent enough to capture the fast-changing inundation, while high temporal resolution images do not provide sufficient detail on spatial variations. To address this resolution compromise for rapid response, here we propose a new automated method to estimate daily sub-pixel water cover, the surface water fraction, from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images. First, water indices are derived from MODIS and Landsat images, and the Landsat derived water indices are used to generate a binary water map. We then aggregate the Landsat water map to the coarser MODIS resolution, making a surface water fraction map. Second, the relationship between the MODIS derived water indices and the Landsat derived surface water fraction is fitted using a Gaussian Process Regression (GPR) model. Lastly, the fitted GPR model is applied to new MODIS imagery to estimate surface water fraction. The experiment results showed that the proposed method could provide water fraction with Root Mean Squared Errors of less than 7%. By mapping fractional water cover automatically and regularly, the proposed method could facilitate daily emergency responses and long-term analyses.