High Temporal Resolution Images Research Articles

Automating superresolution microscopy image analysis with a novel deep learning architecture to probe intracellular trafficking

Optical imaging is integral to health care for diagnostics and disease mapping. With the advent of superresolution microscopy, we can now visualize the biology of health and disease at the sub-cellular level. Such cutting-edge fluorescence microscopy methods generate large amounts of high precision images and videos, demanding high-level image processing and data analysis automation. For instance, optical video microscopy of mammalian cells with high spatial (60-250 nm) and temporal (20 frames/seconds) resolution imaging system yields hundreds of gigabytes of data, depicting the fast dynamics of the nano-sized sub-cellular compartments.

A Pseudo-Siamese Deep Convolutional Neural Network for Spatiotemporal Satellite Image Fusion

Due to technology and cost limitations, it is challenging to obtain high temporal and spatial resolution images from a single satellite spectrometer, which significantly limits the specific application of such remote sensing images in earth science. To solve the problem that the existing algorithms cannot effectively balance the spatial detail preservation and spectral change reconstruction, a pseudo-Siamese deep convolutional neural network (PDCNN) for spatiotemporal fusion is proposed in this article. The method proposes a pseudo-Siamese network framework model for fusion. This framework has two independent and equal feature extraction streams, but the weights are not shared. The two feature extraction streams process the image information at the previous and later moments and reconstruct the fine image of the corresponding time to fully extract the image information at different times. In the feature extraction stream, the multiscale mechanism and dilated convolution of flexible perception are designed, which can flexibly obtain feature image information and improve the model reconstruction accuracy. In addition, an attention mechanism is introduced to improve the weight of the crucial information for the remote sensing images. Adding a residual connection enhances the reuse of the initial feature information in shallow networks and reduces the loss of feature information in deep networks. Finally, the fine images obtained from the two feature extraction streams are weighted and fused to obtain the final predicted image. The subjective and objective results demonstrate that the PDCNN can effectively reconstruct the fusion image with higher quality.

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.

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).

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.

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.

Spatiotemporal Reflectance Fusion Using a Generative Adversarial Network

The spatiotemporal reflectance fusion method is used to blend high-temporal and low-spatial resolution images with their low-temporal and high-spatial resolution counterparts that were previously acquired by various satellite sensors. Recently, a wide variety of learning-based solutions have been developed, but challenges remain. These solutions usually require two sets of data acquired before and after the prediction time, making them unsuitable for near-real-time predicting. The solutions are always trained band by band and thus do not consider the spectral correlation. High-resolution temporal changes are difficult to reconstruct accurately with the network structure used, which lowers the accuracy of the fusion result. To address these problems, this study proposes a novel spatiotemporal adaptive reflectance fusion model using a generative adversarial network (GASTFN). In GASTFN, an end-to-end network, including a generative and discriminative network, is simultaneously trained for all spectral bands. The proposed model can be applied to the one-pair case, consider the spectral correlation of each band, and improve the process of producing super-resolution imagery by adopting the discriminative network for image reflectance values rather than temporal changes in reflectance. The proposed model has been verified with two actual satellite data sets acquired in heterogeneous landscapes and areas with abrupt changes, with a comparison of the state-of-art methods. The results show that GASTFN can generate the most accurate fusion images with more detailed textures, more realistic spatial shapes, and higher accuracy, demonstrating that the GASTFN is effective for predicting near-real-time changes in image reflectance and preserves the most valuable spatial information.

Crop Monitoring using Unmanned Aerial Vehicles: A Review

Unmanned aerial vehicles (UAVs) or drones have been developed significantly over the past two decades, for a wide variety of applications such as surveillance, geographic studies, fire monitoring, security, military applications, search and rescue, agriculture, etc. In agriculture, for example, remote sensing by means of unmanned aerial vehicles has proven to be the most efficient way to monitor crops from images. Unlike remote sensing from satellite images or taken from manned aircraft, UAVs allow capturing images of high spatial and temporal resolution, thanks to their maneuverability and capability of flying at low altitude. This article presents an extensive review of the literature on crop monitoring by UAV, identifying specific applications, types of vehicles, sensors, image processing techniques, among others. A total of 50 articles related to crop monitoring applications of UAV in agriculture were reviewed. Only journal articles indexed in the Scopus database with more than 50 citations were considered. It was found that cereals are the most common crops where remote sensing has been applied so far. In addition, the most common crop remote sensing applications are related to precision agriculture, which includes the management of weeds, pests, diseases, nutrients and others. Crop phenotyping is also a common application of remote sensing, which consists of the evaluation of a crop’s physical characteristics under environmental changes, to select the plants or seeds with favorable genotype and phenotype. Besides, multirotor is the most common type of UAV used for remote sensing and RGB and multispectral cameras are mostly used as sensors for this application. Finally, there is a great opportunity for research in remote sensing related to a wide variety of crops, crop monitoring applications, vegetation indexes and photogrammetry.

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.

Improving the Breath-Holding CVR Measurement Using the Multiband Multi-Echo EPI Sequence.

Blood oxygen level-dependent (BOLD) functional MRI (fMRI) is commonly used to measure cerebrovascular reactivity (CVR), which can convey insightful information about neurovascular health. Breath-holding (BH) has been shown to be a practical vasodilatory stimulus for measuring CVR in clinical settings. The conventional BOLD fMRI approach has some limitations, however, such as susceptibility-induced signal dropout at air tissue interfaces and low BOLD sensitivity especially in areas of low . These drawbacks can potentially be mitigated with multi-echo sequences, which acquire several images at different echo times in one shot. When combined with multiband techniques, high temporal resolution images can be acquired. This study compared an advanced multiband multi-echo (MBME) echo planar imaging (EPI) sequence with an existing multiband single-echo (MB) sequence to evaluate the repeatability and sensitivity of BH activation and CVR mapping. Images were acquired from 28 healthy volunteers, of which 18 returned for repeat imaging. Both MBME and MB data were pre-processed using both standard and advanced denoising techniques. The MBME data was further processed by combining echoes using a -weighted approach and denoising using multi-echo independent component analysis. BH activation was calculated using a general linear model and the respiration response function. CVR was computed as the percent change related to the activation. To account for differences in CVR related to TE, relative CVR (rCVR) was computed and normalized to the mean gray matter CVR. Test–retest metrics were assessed with the Dice coefficient, rCVR difference, within subject coefficient of variation, and the intraclass correlation coefficient. Our findings demonstrate that rCVR for MBME scans were significantly higher than for MB scans across most of the gray matter. In areas of high susceptibility-induced signal dropout, however, MBME rCVR was significantly less than MB rCVR due to artifactually high rCVR for MB scans in these regions. MBME rCVR showed improved test–retest metrics compared with MB. Overall, the MBME sequence displayed superior BOLD sensitivity, improved specificity in areas of signal dropout on MBME scans, enhanced reliability, and reduced variability across subjects compared with MB acquisitions. Our results suggest that the MBME EPI sequence is a promising tool for imaging CVR.

Research on the Lake Surface Water Temperature Downscaling Based on Deep Learning

Lake surface water temperature (LSWT) is an important factor of water ecological environment. As global warming, LSWT is also on the rise. Research on the main reasons of LSWT rising is the basis for controlling and improving the regional ecological environment. However, it is difficult for the existing remote sensing images to take into account the temporal and spatial resolution. Low-resolution images have a serious impact on data accuracy and even produce incorrect results. Therefore, obtaining high temporal and spatial resolution images by downscaling is of great significance to more accurately analyze the temporal and spatial characteristics of LSWT. In this article, Dianchi Lake is selected as research area, and the high spatial resolution image (Landsat) and high temporal resolution image (MODIS) are taken as data. Based on the downscaling algorithm of statistics and learning, DisTrad– super-resolution convolutional neural network (SRCNN) downscaling model is proposed, and the monthly average dataset of LSWT with 50 m resolution is constructed. The results showed 1) DisTrad–SRCNN can reflect the most distribution characteristics of LSWT (SSIMday = 0.96, PSNRday = 23.97; SSIMnight = 0.95, PSNRnight = 24.99). 2) LSWT had an overall upward trend (CRday = 0.22 °C/10a, CRnight = 0.21 °C/10a), showing a cyclical change of cold–warm–cold about 4 years. 3) The northern and lakeshore area were basically in the high temperature, and the whole lake presents a 4–5-year warm–cold–warm periodic change; the LSWT closer to the urban and residential areas and its change rate were relatively high, which indirectly reflected the serious impact of human activities on LSWT.

Assessing the spatial accuracy of UAV-derived products based on variation of flight altitudes

Unmanned Aerial Vehicles (UAVs), which can carry a variety of payloads, and be operated automatically or manually with ground control stations. Nowadays, UAVs can make photogrammetric flight plans and obtain photogrammetric data with existing sensor systems. Automatic data acquisition processes provide lower cost, and high spatial and temporal resolution images in a short period of time compared to other measurement methods. As a result, orthomosaics, dense point clouds and digital surface models (DSMs) are produced and these UAV-derived data are used in various disciplines such as constructions, geomatics, earth sciences, etc. In this study, the same flight plans were realized with an UAV at different altitudes and all aerial images were obtained with the same integrated digital camera. As a result of the processing of images acquired from different altitudes, orthomosaics, DSMs and point cloud were produced. In this study, it is aimed to compare the length, areal and volumetric differences of a small geostationary object. Ground control points (GCPs), which were collected by RTK-GPS (Real-Time Kinematic) in conjunction with the flight integrated into data production process in order to highly accurate product. Ultimately, cross-correlation has been done with the produced data and the terrestrial measurement. Results show that the dimension of the object depend on the flight altitude as expected, however the volumetric changes vary due to the uncertainties in the raw point cloud data.

Improvement in EEG Source Imaging Accuracy by Means of Wavelet Packet Transform and Subspace Component Selection

The electroencephalograph (EEG) source imaging (ESI) method is a non-invasive method that provides high temporal resolution imaging of brain electrical activity on the cortex. However, because the accuracy of EEG source imaging is often affected by unwanted signals such as noise or other source-irrelevant signals, the results of ESI are often incongruous with the real sources of brain activities. This study presents a novel ESI method (WPESI) that is based on wavelet packet transform (WPT) and subspace component selection to image the cerebral activities of EEG signals on the cortex. First, the original EEG signals are decomposed into several subspace components by WPT. Second, the subspaces associated with brain sources are selected and the relevant signals are reconstructed by WPT. Finally, the current density distribution in the cerebral cortex is obtained by establishing a boundary element model (BEM) from head MRI and applying the appropriate inverse calculation. In this study, the localization results obtained by this proposed approach were better than those of the original sLORETA approach (OESI) in the computer simulations and visual evoked potential (VEP) experiments. For epilepsy patients, the activity sources estimated by this proposed algorithm conformed to the seizure onset zones. The WPESI approach is easy to implement achieved favorable accuracy in terms of EEG source imaging. This demonstrates the potential for use of the WPESI algorithm to localize epileptogenic foci from scalp EEG signals.

Mapping Surface Moisture of a Gold Heap Leach Pad at the El Gallo Mine Using a UAV and Thermal Imaging

An understanding of the spatial and temporal variations of surface moisture content over a heap leach pad (HLP) is essential for leaching production and to achieve a high ore recovery. The current practice of leach pad monitoring and data collection remains highly manual and labor-intensive, and exposes technical staff to hazardous material (i.e., cyanide solution). To address these challenges, we propose using unmanned aerial vehicles (UAVs) equipped with thermal imaging sensors to remotely obtain high temporal and spatial resolution image data for monitoring the surface moisture distribution over HLPs. A field study was conducted over a sprinkler-irrigated HLP at El Gallo gold mine in Sinaloa State, Mexico, and the acquired data were used to derive an empirical relationship between the surface moisture content and the remotely sensed surface temperature using linear regression. Moreover, the data were used to generate moisture distribution maps of the entire HLP surface. In situ samples were taken manually during the field experiments to measure the ground-truth material moisture at selected sampling locations. The results show a good agreement between the remote sensing method and the measured ground-truth samples.

Descending aortic strain quantification by intra-operative transesophageal echocardiography: Multimodality validation via cardiovascular magnetic resonance.

Whereas cardiac magnetic resonance (CMR) imaging provides high temporal resolution imaging of aortic distensibility (strain), transesophageal echocardiography (TEE) is widely used for intra-operative aortic imaging and provides a clinical alternative for aortic assessment. We tested intra-operative global circumferential aortic strain (GCS) measured on TEE in relation to the reference of CMR-derived strain among patients undergoing surgical graft repair of ascending aortic aneurysms. CMR (3T) was prospectively performed in patients scheduled for aortic repair. TEE was performed intra-operatively; images were co-localized with MRI. GCS on CMR and TEE was quantified independently, blinded to results of the other modality. 25 patients (54±10year-old, 88% male) were studied, inclusive of 13 genetically mediated and 12 degenerative aneurysms: CMR and TEE were performed within 12±9days. Pulse pressure (PP)-adjusted descending aortic TEE-derived GCS strongly correlated with cine-CMR-derived GCS (r=.75, P=.002) though absolute GCS and PP-adjusted values were slightly lower (5.40±1.11 vs 6.49±1.43% and 11.55±3.04 vs 13.99±4.53%, respectively). Similarly, TEE yielded slightly lower end-diastolic area (EDA [5.1±1.7cm2 vs 5.8±1.3cm2 , P=.004]) and end-systolic area (ESA [6.1±1.9cm2 vs 6.5±1.7cm2 , P=.10]), with significant correlations between the two modalities (r=.73, .76, P<.05 for all). This exploratory study supports feasibility of TEE for assessing aortic GCS in a surgical at-risk population, as well as magnitude of agreement between intra-operative TEE and preoperative CMR. We found that there is a significant correlation between GCS and EDA and ESA aortic areas, but that TEE-derived parameters underestimated CMR values by a small but significant amount.

A 3D hybrid-shot spiral sequence for hyperpolarized imaging.

PurposeHyperpolarized imaging experiments have conflicting requirements of high spatial, temporal, and spectral resolution. Spectral-spatial RF excitation has been shown to form an attractive magnetization-efficient method for hyperpolarized imaging, but the optimum readout strategy is not yet known.MethodsIn this work, we propose a novel 3D hybrid-shot spiral sequence which features two constant density regions that permit the retrospective reconstruction of either high spatial or high temporal resolution images post hoc, (adaptive spatiotemporal imaging) allowing greater flexibility in acquisition and reconstruction.ResultsWe have implemented this sequence, both via simulation and on a preclinical scanner, to demonstrate its feasibility, in both a 1H phantom and with hyperpolarized 13C pyruvate in vivo.ConclusionsThis sequence forms an attractive method for acquiring hyperpolarized imaging datasets, providing adaptive spatiotemporal imaging to ameliorate the conflict of spatial and temporal resolution, with significant potential for clinical translation.

In situ electrochemical scanning/transmission electron microscopy of electrode–electrolyte interfaces

Abstract

Mathematical analysis of Sentinel-2 spectral signal evolution for mapping agriculture area in Senegal: case of millet, maize and peanuts

Agricultural monitoring has become an absolute necessity in the Sahel countries, especially with climate change which constitutes a real threat for this sector. The aim of this work is to develop a methodology for identifying crops and mapping agricultural areas using Sentinel-2 data from the Copernicus program. The purpose of this work consisted in discriminating the crops of millet, maize and peanuts. This is to analyse the scientific and technical obstacles related to this problem. For this, we have made a mathematical analysis of optical satellite images. High temporal and spatial resolution images (10m to 60m) of Sentinel 2 sensors were used in this work. This unique set of data coupled with field data, has permitted to carry out a diagnosis of land cover and cultivated land surfaces, and evaluating the contribution of this type of data for crop forecast