High Spatial Resolution Images Research Articles

Rapid microscopic 3D-diffusion tensor imaging fiber-tracking of mouse brain in vivo by super resolution reconstruction: validation on MAP6-KO mouse model.

Exploring mouse brains by rapid 3D-Diffusion Tensor Imaging (3D-DTI) of high spatial resolution (HSR) is challenging in vivo. Here we use the super resolution reconstruction (SRR) postprocessing method to demonstrate its performance on Microtubule-Associated-Protein6 Knock-Out (MAP6-KO) mice. Two spin-echo DTI were acquired (9.4T, CryoProbe RF-coil): (i)-multislice 2D-DTI, (echo-planar integrating reversed-gradient) acquired in vivo in the three orthogonal orientations (360μm slice-thickness, 120 × 120μm in-plane resolution, 56min scan duration); used in SRR software to reconstruct SRR 3D-DTI with HSR in slice-plane (120 × 120 × 120µm) and (ii)-microscopic 3D-DTI (µ-3D-DTI), (100 × 100 × 100µm; 8h 6min) on fixed-brains ex vivo, that were removed after paramagnetic contrast-agent injection to accelerate scan acquisition using short repetition-times without NMR-signal sensitivity loss. White-matter defects, quantified from both 3D-DTI fiber-tracking were found very similar. Indeed, as expected the fornix and cerebral-peduncle volume losses were -39% and -35% in vivo (SRR 3D-DTI) versus -34% and -32% ex vivo (µ-3D-DTI), respectively (p<0.001). This finding is robust since the µ-3D-DTI feasibility on MAP6-KO ex vivo was already validated by fluorescent-microscopy of cleared brains. First performance of the SRR to generate rapid HSR 3D-DTI of mouse brains in vivo is demonstrated. The method is suitable in neurosciences for longitudinal studies to identify molecular and genetic abnormalities in mouse models that are of growing developments.

A high-sensitivity high-resolution intravascular photoacoustic catheter through mode cleaning in a graded-index fiber

Intravascular photoacoustic imaging has been developed to evaluate the possibility of plaque rupture in atherosclerosis by high spatial resolution imaging of lipid. However, the detection sensitivity and spatial resolution are compromised by the poor focusing caused by a multimode fiber. In this work, we report an intravascular photoacoustic catheter with mode self-cleaning in a graded-index fiber to improve the beam quality and the sensitivity for lipid detection. Compared with the higher-order modes in a step-index multimode fiber, the lower-order modes generated by the self-cleaning effect in the graded-index fiber greatly enhanced the photoacoustic spatial resolution and detection sensitivity. The dominant ringing artifact caused by laser absorption of the ultrasound transducer was further reduced by using stripe suppression. A lipid plaque mimicking phantom was imaged for evaluation. Lipid particles with a small diameter of 75.7 µm were clearly observed.

A method for detecting underground natural gas pipeline micro-leakage in vegetated areas using high spatial resolution hyperspectral imagery

Natural gas is an important clean energy source that is mainly transported through buried pipelines. However, slight leakage during transportation is inevitable and needs to be identified in time to reduce the risk of related accidents. For this purpose, remote sensing can be used as an indirect and non-destructive technique to detect natural gas leakage through vegetation. Abundant spectral and detailed spatial information enables the high spatial resolution hyperspectral imagery to capture subtle stress responses from vegetation. In this study, we designed a simulation experiment of natural gas leakage in vegetated areas, and collected the high spatial resolution hyperspectral images of three plant species. Considering the shadow problem contained in high spatial resolution images, a new vegetation pixel extraction method to eliminate the influence of background shadows was proposed. Then, two spectral transformation methods, continuum removal (CR) and variational mode decomposition (VMD), were integrated to analyze the vegetation spectra, and a spectral index which could reflect the degree of vegetation stress was designed based on the spectral characteristics, named CVI (Continuum removal - Variational mode decomposition Index). Finally, the threshold segmentation methods and the cumulative minimum circumscribed circle method were used to extract the range and location of natural gas leakage. The study demonstrated the potential of using high spatial hyperspectral imagery to map and monitor natural gas leakage through its stress on vegetation. Results of this research can be a reference for the fine identification of stressed plants and are expected to be applied to provide support for related departments and stakeholders.

Guided Deep Generative Model-Based Spatial Regularization for Multiband Imaging Inverse Problems.

When adopting a model-based formulation, solving inverse problems encountered in multiband imaging requires to define spatial and spectral regularizations. In most of the works of the literature, spectral information is extracted from the observations directly to derive data-driven spectral priors. Conversely, the choice of the spatial regularization often boils down to the use of conventional penalizations (e.g., total variation) promoting expected features of the reconstructed image (e.g., piece-wise constant). In this work, we propose a generic framework able to capitalize on an auxiliary acquisition of high spatial resolution to derive tailored data-driven spatial regularizations. This approach leverages on the ability of deep learning to extract high level features. More precisely, the regularization is conceived as a deep generative network able to encode spatial semantic features contained in this auxiliary image of high spatial resolution. To illustrate the versatility of this approach, it is instantiated to conduct two particular tasks, namely multiband image fusion and multiband image inpainting. Experimental results obtained on these two tasks demonstrate the benefit of this class of informed regularizations when compared to more conventional ones.

The dynamics of normalized difference vegetation index (NDVI) of natural and agricultural landscapes in Alexandrovsky and Novoselitsky municipal districts of the Stavropol territory during the active vegetation period in 2020 based on high resolution spatial images

The article discusses the changes in the NDVI on the territory of the Alexandrovsky and Novoselitsky municipal districts of the Stavropol Territory during the active vegetation period of 2020. The data source were Sentinel-2 thematic images, the spatial resolution of which is 10 m. The schematic maps reflecting the distribution of the NDVI by dates with minimal cloud cover in June-September 2020 have been compiled and analyzed. To explain the spatial and temporal pattern of changes in the areas occupied by different gradations of the NDVI, the data of the Aleksandrovsky weather station for 2020 (the course of temperature and precipitation during the year) were analyzed here as well. The use of remote sensing data and corresponding vegetation indices allows mapping various processes and phenomena in the GIS sphere, and the compiled maps can be used to solve a wide range of theoretical and practical tasks, including crop yields assessement.

Fusing Ultra-Hyperspectral and High Spatial Resolution Information for Land Cover Classification Based on AISAIBIS Sensor and Phase Camera

Hyperspectral imaging technology are widely used in vegetation, agriculture, and other fields, especially in land cover classification of complex scenes. Higher spectral resolution has become the focus of the development of hyperspectral imaging technology for classification. The advent of airborne AISAIBIS sensor reaches 0.11 nm ultrahyperspectral resolution. The ultrahyperspectral imagery shows great advantages in classification with its increasing spectral resolution. But its spatial resolution is limited because of the imaging mechanism, which brings great difficulties to the accurate extract of fine and regular objects. Therefore, we proposed an optimal fusion and classification strategy based on the complementary advantage information of ultrahyperspectral and high spatial resolution image. The fusion feasibility and effectiveness were verified by various fusion methods. And a quality evaluation system was developed to assess the quality of fusion results. Besides, a multiresolution segmentation optimization and classification evaluation scheme was proposed to comparatively analyze the effect of optimal fusion result on improving classification accuracy. Results show that the classification accuracy of the optimal fused image reaches 88.10%, and 7.11%–19.03% higher than that of original images. It fully validates the effectiveness of the strategy proposed in this article.

Effects of Urban Redevelopment on Surface Urban Heat Island

Urban expansion and urban redevelopment can affect the surface urban heat island (SUHI) phenomenon, a major topic in the study of urban climates. The effects of urban expansion on SUHI have been studied by numerous researchers, while the effects of urban redevelopment remain unclear. We aimed to understand the effects of urban redevelopment on SUHI. Using the thermal bands of Landsat-5 TM and Landsat-8 TIRS, we retrieved the land surface temperature (LST) and calculated the SUHI intensity of the redevelopment areas during 2000–2019 in Guangzhou (China). Based on high spatial resolution images from Google Earth, 253 redevelopment areas were identified and classified as village low residential areas, low-industrial areas, middle residential areas, high residential areas, and commercial areas. Further, the change in SUHI intensity in redevelopment areas was analyzed. Results showed that urban redevelopment, including the transitions from urban village to high-rise commercial land, from low-rise industrial land to high-rise residential land, from industrial land to parking lot, from bare land to mid-rise buildings, from parking lot to high-rise commercial land, can considerably reduce the speed of increase of local SUHI intensity. These new findings have theoretical and practical implications for urban planning and redevelopment.

Full Wave Inversion and Inverse Scattering in Ultrasound Tomography/Volography.

Ultrasound breast tomography has been around for more than 40years. Early approaches to reconstruction focused on simple algebraic reconstructions and bent ray techniques. These approaches were not able to provide high-quality and high spatial-resolution images. The advent of inverse scattering approaches resulted in a shift in image reconstruction approaches for breast tomography and a subsequent improvement in image quality. Full wave inverse solvers were developed to improve the reconstruction times without sacrificing image quality. The development of GPUs has markedly decreased the time for reconstruction using inverse scatting approaches. The development of fully 3D image solvers and hardware capable of capturing out of plane scattering have resulted in further improvement in breast tomography. This chapter discusses the state-of-the-art in ultrasound breast tomography, its history, the theory behind inverse scattering, approximations that are included to improve convergence, 3D image reconstruction, and hardware implementation of the constructions.

High-Resolution Planetscope Imagery and Machine Learning for Estimating Suspended Particulate Matter in the Ebinur Lake, Xinjiang, China

Ebinur Lake is a shallow lake and vulnerable to strong winds, which can lead to drastic changes in suspended particulate matter (SPM). High spatial and temporal resolution images are therefore urgently needed for SPM monitoring over the Ebinur Lake. Hence, a high-efficiency inversion model of estimating SPM from high-resolution images using machine learning is essential to increase the amount of extracted information through band combinations quadratic optimization. This article aims to evaluate the capability of the PlanetScope images and four machine learning approaches for estimating SPM of the Ebinur Lake. The specific objectives include: to obtain the sensitive bands and band combinations for SPM using correlation analysis; to quadratically optimize the combination pattern of sensitive bands using a linear model; and to compare the accuracy of traditional linear model and machine learning models in estimating SPM. The results of the study confirm that after linear model quadratic optimization, the band combinations of B3*B4, (B2+B3)/ (B2-B3), (B3+B4)*(B3+B4), and (B3-B2)/(B2/B3) have higher accuracy than that of the single band model. By inputting the preferred four-band combinations into the partial least squares, random forest, extreme gradient boosting, gradient boosting decision tree, and categorical boosting (CatBoost) models, the performance of the SPM inversion based on PlanetScope images is better than the traditional linear model. Validation of the inversion maps with observations further indicates that the CatBoost model performed the best.

Peanut yield prediction with UAV multispectral imagery using a cooperative machine learning approach

&lt;abstract&gt; &lt;p&gt;The unmanned aerial vehicle (UAV), as a remote sensing platform, has attracted many researchers in precision agriculture because of its operational flexibility and capability of producing high spatial and temporal resolution images of agricultural fields. This study proposed machine learning (ML) models and their ensembles for peanut yield prediction using UAV multispectral data. We utilized five bands (red, green, blue, near-infra-red (NIR) and red-edge) multispectral images acquired at various growth stages of peanuts using UAV. The correlation between spectral bands and yield was analyzed for each growth stage, which showed that the maturity stages had a significant correlation between peanut yield and spectral bands: red, green, NIR and red edge (REDE). Using these four bands spectral data, we assessed the potential for peanut yield prediction using multiple linear regression and seven non-linear ML models whose hyperparameters were optimized using simulated annealing (SA). The best three ML models, random forest (RF), support vector machine (SVM) and XGBoost, were then selected to construct a cooperative yield prediction framework with both the best ML model and the ensemble scheme from the best three as comparable recommendations to the farmers.&lt;/p&gt; &lt;/abstract&gt;

Monitoring Coastal Landslides along the Northeast Black Sea of Bulgaria Using SAR data

The geological conditions along the Northeast Black sea coast of Bulgaria are favorable for landslides emergence and development. Those processes have been rigorously documented in the last fifty years by Earth science researchers. Their efforts contributed in a broader context to the landscape conservation and to mitigate the risks for the population caused by the mentioned surface disruptions. In this paper the obtained results record the temporal behavior of several landslides located in stated region during the last five years. The main data source to monitor the mentioned landslides are data from synthetic aperture radar (SAR) instrument processed by the differential interferometric SAR (DInSAR) method. The produced results were validated by means of global navigation satellite system (GNSS) measurements carried out at purposely created geodetic networks, satellite images with high spatial resolution and digital images obtained by unmanned aerial systems (UASs). The results presented in this paper unambiguously confirm the usability of the produced information for the local authorities and other stakeholders thus contributing to the improvement of landslide risk management practices and to the better planning of the territory where the researched landslides are located.

Superfast Scan of Focused X-Ray Luminescence Computed Tomography Imaging.

X-ray luminescence computed tomography (XLCT) is a hybrid molecular imaging modality having the high spatial resolution of x-ray imaging and high measurement sensitivity of optical imaging. Narrow x-ray beam based XLCT imaging has shown promise for high spatial resolution imaging of luminescent targets in deep tissues, but the slow acquisition speed limits its applications. In this work, we have introduced a superfast XLCT scan scheme based on the photon counter detector and a fly-scanning method. The new scan scheme is compared with three other scan methods. We have also designed and built a single-pixel x-ray detector to detect object boundaries automatically. With the detector, we can perform the parallel beam CT imaging with the XLCT imaging simultaneously. We have built the prototype XLCT imaging system to verify the proposed scan scheme. A phantom embedded with a set of four side-by-side cylindrical targets was scanned. With the proposed superfast scan scheme, we have achieved 43 seconds per transverse scan, which is 28.6 times faster than before with slightly better XLCT image quality. The superfast scan allows us to perform 3D pencil beam XLCT imaging in the future.

A survey of hyperspectral image super-resolution method

A survey of hyperspectral image super-resolution method

Активность геоморфологических процессов на склонах речных долин в условиях газогидротермальных проявлений (по разновременным снимкам и ЦМР)

In the geothermal zones of modern volcanic areas, geomorphological processes are considerably influenced by numerous gas-hydrothermal outcrops, which may dramatically change rock properties and, consequently, denudation peculiarities and rates. The activity of processes on the slopes of Kamchatka river valleys under conditions of gas-hydrothermal occurrences (Geysernaya river valley) and without them (Shumnaya river valley) has been studied and comparatively analyzed. For the most correct separation and comparison of the valley regions which differ in the conditions of development of the modern slope processes, ArcticDEM digital elevation model and a series of morphometric maps (slope steepness, exposition, elementary watersheds) built on its basis, as well as geological maps, results of thermal survey of different detail level and field observation data were used. Interpretation of high spatial resolution images (0.5–5 m) for the mid-1960s (KeyHole-4 CORONA) and modern images for the period 2016–2021 from different survey systems was carried out. As a result of decoding, quantitative assessments of slope conditions within the selected valley fragments selected on the basis of morphometry were obtained for two time slices separated by 55 years. It is established that under conditions of gas-hydrothermal manifestations development, the share of the total area of the slope’s sections affected by active processes, with other conditions being equal, is significantly higher than the same in the areas without gas-hydrotherms. For 55 years the share of the areas with slope processes activation on the background areas practically has not changed, and on the territories affected by gas-hydrothermal influence has increased by 10–17 %. The conducted studies allow us to conclude that gas-hydrothermal manifestations are the most powerful driver of modern geomorphological processes on slopes in the studied region.

A dataset of mid-channel bar forms and some mid-channel bars vegetation classification in the middle and lower reaches of the Yangtze River (1987–2020)

As an important geographical position in China, the middle and lower reaches of the Yangtze River play an important role in the construction and development of the Yangtze River Economic Belt. The evolution of mid-channel bar (MCB) has a good reference value for wetland protection and waterway safety, and the dynamic changes of vegetation on the continent are also crucial for river ecosystem functions and related ecosystem services. In this paper, based on the Landsat image data from 1987 to 2020, we used the ArcGIS software to synthesize the bands and improved the normalized differential water index method and the object-oriented classification method, so as to obtain the forms of MCB and some vegetation classification vectors on the continents in the middle and lower reaches of the Yangtze River in the past 30 years. In addition, we compared the data with high spatial resolution images to verify the accuracy and reliability of the data. This dataset can be used for analysis of the evolution of MCB in the middle and lower reaches of the Yangtze River, analysis of the impact of environmental factors, and research on landscape pattern changes.

A dataset of UAV multispectral images for the grassland-livestock balance

The grassland-livestock balance system plays an important role in reconciling the protection of grassland resources with the sustainable development of animal husbandry. The accurate estimation of the grassland-livestock balance is the basis for the implementation of the grassland-livestock balance system. In order to explore the accurate calculation method of the important parameter, i.e. livestock grass demand in the calculation system, in this study, we set up an experimental area in the natural grassland of Hulunbuir, Inner Mongolia, to simulate a real grazing scene, and conducted 4 grazing experiments controlling the sheep number and grazing time. We obtained multispectral images of the experimental area before and after grazing were with the help of the UAV remote sensing, and carried out a quadrat survey. After data sorting and preprocessing, we produced this dataset of high spatial resolution image data and accurate quadrat investigation data. It can be applied to the research on the daily grass demand of grazing sheep on Hulunbuir meadow steppe in autumn. This dataset provides data support for the accurate estimation of local grassland-livestock balance, and is of great significance for the adaptive dynamic management of grass and livestock.

Landscape‐hydrological phenomenon of the formation of floating islands on Lake Chebarkul (Davletovo) in the Ural River basin, Russia

Aim. To record factually the development of floating islands on Lake Chebarkul (Davletovo) as a landscape‐hydrological phenomenon in the Ural River basin and in the Southern Trans‐Urals, to determine the mechanisms of formation and to identify the specific features of their morphostructure, intra‐annual movements and long‐term dynamics.Materials and Methods. Based on images of high (2003–2020) and medium (1972–2020) spatial resolution, an analysis was made of qualitative and quantitative indicators reflecting the long‐term and intra‐ annual dynamics of the state of floating islands (mats) and coastal thickets of macrophytes («splavina» or «labza» in Russian).Results. It was established that the main source of material for the islands were the coastal thickets of macrophytes at the site of the confluence of watercourses into the lake (Yangelka River and Mogak Crrek), which periodically (during periods of extreme spring floods) lose contact with coastal shallow waters and are carried into the lake. The high variability of the states and the dynamism of the displacement of the islands, as well as the tendency to reduce the number and area of the islands and coastal thickets of macrophytes, caused, among other factors, by the consequences of the anomalous summer flood of 2013, were revealed. Being in an unfixed state, floating islands drift along the water area of the lake for considerable distances, reaching 10–15 km or more during the ice‐ free period of the year. The main driving force is the wind, which causes prevailing alongshore displacements of the islands: as a result of collisions, they are reduced in size and acquire a rounded shape in plan.Conclusion. Throughout the historical period, floating islands around the world have been perceived as a landscape phenomenon that deserves protection and rational use. Lake Chebarkul with floating islands is of undoubted interest as an object of geographical, hydrological, ecological and biological research.

A Multiscale Spatiotemporal Fusion Network Based on an Attention Mechanism

Spatiotemporal fusion is an effective and cost-effective method to obtain both high temporal resolution and high spatial resolution images. However, existing methods do not sufficiently extract the deeper features of the image, resulting in fused images which do not recover good topographic detail and poor fusion quality. In order to obtain higher quality spatiotemporal fusion images, a novel spatiotemporal fusion method based on deep learning is proposed in this paper. The method combines an attention mechanism and a multiscale feature fusion network to design a network that more scientifically explores deeper features of the image for different input image characteristics. Specifically, a multiscale feature fusion module is introduced into the spatiotemporal fusion task and combined with an efficient spatial-channel attention module to improve the capture of spatial and channel information while obtaining more effective information. In addition, we design a new edge loss function and incorporate it into the compound loss function, which helps to generate fused images with richer edge information. In terms of both index performance and image details, our proposed model has excellent results on both datasets compared with the current mainstream spatiotemporal fusion methods.

Nature of the galaxies on top of quasars producing Mg ii absorption

ABSTRACT Quasar–galaxy pairs at small separations are important probes of gas flows in the disc–halo interface in galaxies. We study host galaxies of 198 Mg ii absorbers at 0.39 ≤ zabs ≤ 1.05 that show detectable nebular emission lines in the Sloan Digital Sky Survey (SDSS) spectra. We report measurements of impact parameter (5.9 ≤ D [kpc] ≤ 16.9) and absolute B-band magnitude (−18.7 ≤ MB ≤ −22.3 mag) of host galaxies of 74 of these absorbers using multiband images from the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Survey, more than doubling the number of known host galaxies with D ≤ 17 kpc. This has allowed us to quantify the relationship between Mg ii rest equivalent width (W2796) and D, with best-fitting parameters of W2796 (D = 0) = 3.44 ± 0.20 Å and an exponential scale length of 21.6$^{+2.41}_{-1.97}\, \mathrm{ kpc}$. We find a significant anticorrelation between MB and D, and MB and W2796, consistent with the brighter galaxies producing stronger Mg ii absorption. We use stacked images to detect average emissions from galaxies in the full sample. Using these images and stacked spectra, we derive the mean stellar mass (9.4 ≤ log(M*/M⊙) ≤ 9.8), star formation rate (2.3 ≤ SFR [M⊙ yr−1] ≤ 4.5), age (2.5–4 Gyr), metallicity (12 + log(O/H) ∼ 8.3), and ionization parameter (log q [cm s−1] ∼ 7.7) for these galaxies. The average M* found is less than that of Mg ii absorbers studied in the literature. The average SFR and metallicity inferred are consistent with that expected in the main sequence and the known stellar mass–metallicity relation, respectively. High spatial resolution follow-up spectroscopic and imaging observations of this sample are imperative for probing gas flows close to the star-forming regions of high-z galaxies.

Applicability Analysis of GF-2PMS and PLANETSCOPE Data for Ground Object Recognition in Karst Region

Remote sensing image with high spatial and temporal resolution is very important for rational planning and scientific management of land resources. However, due to the influence of satellite resolution, revisit period, and cloud pollution, it is difficult to obtain high spatial and temporal resolution images. In order to effectively solve the “space–time contradiction” problem in remote sensing application, based on GF-2PMS (GF-2) and PlanetSope (PS) data, this paper compares and analyzes the applicability of FSDAF (flexible spatiotemporal data fusion), STDFA (the spatial temporal data fusion approach), and Fit_FC (regression model fitting, spatial filtering, and residual compensation) in different terrain conditions in karst area. The results show the following. (1) For the boundary area of water and land, the FSDAF model has the best fusion effect in land boundary recognition, and provides rich ground object information. The Fit_FC model is less effective, and the image is blurry. (2) For areas such as mountains, with large changes in vegetation coverage, the spatial resolution of the images fused by the three models is significantly improved. Among them, the STDFA model has the clearest and richest spatial structure information. The fused image of the Fit_FC model has the highest similarity with the verification image, which can better restore the coverage changes of crops and other vegetation, but the actual spatial resolution of the fused image is relatively poor, the image quality is fuzzy, and the land boundary area cannot be clearly identified. (3) For areas with dense buildings, such as cities, the fusion image of the FSDAF and STDFA models is clearer and the Fit_FC model can better reflect the changes in land use. In summary, compared with the Fit_FC model, the FSDAF model and the STDFA model have higher image prediction accuracy, especially in the recognition of building contours and other surface features, but they are not suitable for the dynamic monitoring of vegetation such as crops. At the same time, the image resolution of the Fit_FC model after fusion is slightly lower than that of the other two models. In particular, in the water–land boundary area, the fusion accuracy is poor, but the model of Fit_FC has unique advantages in vegetation dynamic monitoring. In this paper, three spatiotemporal fusion models are used to fuse GF-2 and PS images, which improves the recognition accuracy of surface objects and provides a new idea for fine classification of land use in karst areas.