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Abstract Lanthanide‐based NIR‐II‐emitting materials (1000–1700 nm) show promise for optoelectronic devices, phototherapy, and bioimaging. However, one major bottleneck to prevent their widespread use lies in low quantum efficiencies, which are significantly constrained by various quenching effects. Here, a highly oriented (222) facet is achieved via facet engineering for Cs2NaErCl6 double perovskites, enabling near‐complete suppression of NIR‐II luminescence quenching. The optimally (222)‐oriented Cs2Ag0.10Na0.90ErCl6 microcrystals emit Er3+ 1540 nm light with unprecedented high quantum efficiencies of 90 ± 6% under 379 nm UV excitation (ultralarge Stokes shift >1000 nm), and a record near‐unity quantum yield of 98.6% is also obtained for (222)‐based Cs2NaYb0.40Er0.60Cl6 microcrystallites under 980 nm excitation. With combined experimental and theoretical studies, the underlying mechanism of facet‐dependent Er3+ 1540 nm emissions is revealed, which can contribute to surface asymmetry‐induced breakdown of parity‐forbidden transition and suppression of undesired non‐radiative processes. Further, the role of surface quenching is reexamined by molecular dynamics based on two facets, highlighting the drastic two‐phonon coupling effect of a hydroxyl group to 4I13/2 level of Er3+. Surface‐functionalized facets will provide new insights for tunable luminescence in double perovskites, and open up a new avenue for developing highly efficient NIR‐II emitters toward broad applications.

The traditional Chinese medicine (TCM) formula Ento-PB containing Periplaneta americana (Linnaeus) (Blattidae) and Taraxacum mongolicum Hand.-Mazz. (Compositae) has great potential for treating inflammation. Thus, this study aimed to explore the pharmacodynamic effect of Ento-PB on DSS-induced ulcerative colitis in BALB/c mice, and its effects on immune function, JAK2/STAT3-related signaling pathways and intestinal flora in UC mice. It was identified that the extract Ento-PB mainly contained 20 compounds, accounting for 78.50 % of the total peak area. Compared with the model group, each dose group of Ento-PB could reduce the DAI score, colon index, CMDI score and colon HS score of mice to varying degrees (P

Mismatch removal is a crucial step in multiview matching of lunar orbiter images. This process involves complex challenges like terrain-induced distortion and nonunique geometric structures due to repetitive textures. Traditional methods, whether global constraints or local constraints, fall short in adequately addressing these issues in orbiter imagery. Therefore, this article proposes an effective method for mismatch removal of orbiter images based on global constraint and local geometry preservation combined with the imaging model. In this method, a clean neighborhood of each matching point based on the characteristic of centralized distribution of the back-projection residuals globally is constructed. In the local region, we define a local minimum geometric polygon consisting of the center feature point and its three neighbors, and combine the similarity of the back-projection difference vectors with the affine invariance of the polygon to distinguish the correct matches and mismatches by measuring the degree of local geometry preservation. A series of experiments encompassing parameter sensitivity analysis, comparison studies and ablation experiments were conducted on the lunar reconnaissance orbiter image datasets to demonstrate the effectiveness and reliability of the proposed method. The results indicate that our method exhibits a notable insensitivity to parameter variations, and outperforms other advanced methods in both qualitative and quantitative evaluations. Moreover, a large-scale orbiter images multiview matching tie points extraction framework is extended based on the proposed mismatch removal method, which can achieve better results than commonly used photogrammetric software in terms of the number and accuracy of tie points.

Matching planetary remote sensing images (PRSI) is essential for deep space exploration. Through matching optical images collected by different probes, the terrain of planets can be accurately mapped; however, PRSI lacks surface texture information and exhibits noticeable nonlinear radiation differences, e.g., illumination differences. It is not easy to achieve satisfactory results through traditional matching methods. In order to cope with the above problems, a new PRSI matching method has been proposed. It can extract low–high frequency features by building double-frequency scale space. In addition, we also use the nonmaximum suppression strategy for rejecting overlapped feature points, which reduces the time consumption and improves the matching accuracy. The experimental results show that the proposed method can effectively match PRSI and be superior to comparable methods.

The semantic segmentation of point clouds plays a crucial role in the interpretation of 3-D scene. However, the majority of supervised learning methods needs a great number of annotated data to train an effective model, which requires labor-intensive and time-consuming annotation of 3-D points. In this work, we presented a simple but practical framework that adopts a few-shot learning strategy to semantically segment 3-D point clouds using merely sparse annotations. Major contributions lie in two major aspects. 1) A few-shot learning framework for semantic segmentation with sparse annotation is designed, which requires less manual labeling work than conventional methods. 2) A distribution calibration method is introduced to achieve transfer learning based on pretrained models and requires no retraining of neural networks. The accuracy of semantic segmentation has been greatly boosted by adapting the model trained with a small number of annotated data to a wide range of data through synchronous transfer learning. The proposed method is tested by two groups of benchmarks, namely the combination of the TUM-MLS-2016 dataset and the Toronto-3D MLS dataset and the combination of the DALES dataset and the ISPRS benchmark dataset. We have obtained impressive results reaching the same level as the results of the state-of-the-art method using dense annotation with an overall accuracy of 0.81 for segmenting eight semantic classes on the Toronto-3D MLS dataset, and an overall accuracy of 0.81 for segmenting nine semantic classes on the ISPRS benchmark dataset, proving the effectiveness of the proposed framework.

With the increasing interest in the south polar region of the Moon, there is an urgent need for high-resolution mapping products to support future exploration. Bundle block adjustment (BBA) is able to improve the spatial positioning accuracy of images used to generate high-precision mapping products. However, the impacts of weak-convergence geometry and poor illumination conditions in lunar south pole on BBA still need to be solved. This article proposes a large-scale robust BBA method for narrow angle camera (NAC) images in south pole. The NAC images are taken from the Lunar Reconnaissance Orbiter, which are scanner-type images requiring special treatment beyond the classical BBA of framing camera images. To handle the weak-convergence geometry issue of stereo NAC imagery, a topographic constraint using reference digital elevation model is integrated into BBA with an appropriate weighting scheme to estimate local topographic relief. In addition, a two-stage outlier elimination strategy for BBA with absolute-relative thresholding and iteratively reweighting methods is presented to reject outliers caused by the poor illumination conditions in lunar south pole. Thousands of images are used for experimental tests and lunar orbiter laser altimeter digital elevation models (DEMs) with different resolutions are used as reference data. The satisfactory experimental results demonstrate the effectiveness and reliability of our BBA method. The proposed large-scale BBA method reduces the relative positioning errors in block network to within 1 m, and decreases the inconsistency between NAC images caused by the orientation errors to less than 0.5 pixel, which offers a reliable solution for large-scale controlled mapping.

The ICESat-2 satellite equipped with a new photon-counting laser altimeter has received much attention as a source of accurate elevation observations. However, in this research field, there is a lack of an open-source high-accuracy elevation control point dataset with the specific quality requirements at a global scale. To this end, using ICESat-2 altimeter data as the main data source, we constructed and organized a dataset as a useful supplement for this research field. The dataset was generated by a methodology based on detection environment evaluation, photon spatial analysis, and the redundant observation statistics. The dataset includes more than 600 million elevation control points and covers the global land areas, except for Greenland and Antarctica. The dataset has been validated by multiple digital elevation models (DEMs) from around the world (sourced from airborne LiDAR data). The results show that the dataset has high-accuracy elevation control points. The overall root-mean-square error (RMSE) of the original elevations of ICESat-2 is about 1.384–4.820 m, but the overall RMSE of the elevation control points in the new dataset is about 0.279–0.642 m. Moreover, the results obtained in this study show that the dataset is suitable for application within high vegetation cover areas.

Male sterility plays an important role in the utilization of heterosis in rice. The establishment of male sterile lines in rice is one of the key technologies in hybrid rice production systems. The currently widely used male sterile line breeding systems mainly include: three-line hybrid rice based on cytoplasmic male sterility, two-line hybrid rice based on environmental sensitive gene male sterility, and third-generation hybrid rice based on nuclear gene male sterility Seed production system. This study reviewed the types and mechanisms of male sterility in rice, and looked forward to the development direction of hybrid rice.

To establish the freeze-drying process and the HPLC method to determine the main effective components and content of freeze-dried Polygonatum sibiricum of China. The results show that the freeze-drying process is a slice thickness at 3 mm, boiling time 2 min, pre-freezing temperature at −35 °C, low-temperature holding at −10 °C, low temperature sublimation at 10 °C and drying temperature at 35 °C. The optimum HPLC conditions of freeze-dried Polygonatum sibiricum was SepaxGp-C18 (4.6 × 250 mm, 5 μm), gradient elution of mobile phase A (acetonitrile)-B (ultra-pure water), detection wavelength of 254 nm, the flow rate of 1 mL/min, column temperature of at 30 °C and injection volume of 10 μL. Polygonatum sibiricum from different producing areas contains a variety of amino acids, diosgenin, sugars, and other active ingredients. The protein content of Polygonatum sibiricum from Yunnan Pu'er is the highest among them, and that of Shangluo in Shaanxi is the lowest. The total sugar content of Polygonatum sibiricum in Puer is the highest, while that of Polygonatum sibiricum in Wenshan is the lowest. And the diosgenin of Polygonatum sibiricum in Lijiang is the highest. In this study, the freeze-drying process of Polygonatum sibiricum was established, and the main effective components and contents of freeze-dried Polygonatum sibiricum was determined as the better HPLC method, provided theoretical and technical support for the rational development and utilization of Polygonatum sibiricum.

Laser altimeters are capable of achieving fine mapping of the permanently shadowed regions (PSRs) of the Moon, which can provide fundamental topographic data for planetary missions. However, various factors can cause uncertainty in the geolocation of laser spots, which in turn causes terrain artifacts. In this article, we present an iterative self-constrained adjustment method to reduce the uncertainty of laser spot positioning. First, grid search was conducted for each altimetric profile from the lunar orbiter laser altimeter (LOLA), to minimize the weighted root-mean-square error (RMSE), constrained by the other altimetric profiles. Second, the updated profiles were iteratively adjusted until the adjustment value for the plane position converged. In addition, statistics from the standardized de-trended slope and residual were created to eliminate outliers, which were indeed some pseudo-topographic observations. In order to validate the results, the deviation of the elevation by projecting the adjusted laser profiles onto the improved LOLA digital elevation model (DEM) were calculated. The mean absolute error between the two is 0.25 m and the RMSE is 0.46 m. For the local terrain features with large differences, high resolution optical images were used for visual interpretation. The analysis shows that the obtained results appear to be more reasonable. Finally, using the corrected LOLA altimetric data, we made a new DEM of the PSRs within 89°S of the lunar south pole, which can provide a refined and reliable topographic dataset for follow-up research.

Sea-ice segmentation is of great importance for environmental research, ship navigation, and ice hazard forecasting. Remote sensing (RS) images have been a unique data source for rapid and large-scale sea-ice monitoring. The 2021 Gaofen Challenge has offered a track of sea-ice segmentation based on optical RS images. For the initial competition, our team ranked 3rd place (deepjoker) in the accuracy leaderboard and the solution has been the most efficient algorithm to achieve a segmentation score above 97.79%. In this article, we briefly introduce our three strategies of the achievement including: 1) decoding the partial pretrained networks which can simultaneously capture the complex boundaries of sea ices and decrease the computational cost without the performance drop; 2) employing the classwise Dice loss for solving the gradient vanishing problem when most ground-truth maps are backgrounds; and 3) replacing the commonly exploited decoder with the one proposed by Silva et al. (2021). The main contributions are twofold: 1) an efficient and effective sea-ice segmentation method is proposed and 2) the gradient vanishing problem of binary Dice loss is investigated under some scenarios and solved by introducing its classwise version. Comparison and ablation experiments demonstrate the effectiveness of the proposed method with respect to other commonly adopted deep segmentation models.

Visibility analysis plays a vital role in the design and placing of traffic signs in the urban street environment. This work investigates the occlusion detection of traffic lights and traffic signs caused by vegetation. The presented analysis method is built upon the inputs from the expected situation reflected by a highly detailed 3D city model and the as-is situation captured by 3D Mobile Laser Scanning (MLS). The model contains the location and orientation of streets, traffic lights, and traffic signs; the measurements add detail on irregular-shaped and morphing objects such as vegetation, respectively. The analysis covers the visibility of traffic lights and traffic signs by ray-tracing in an occupancy grid that is generated by the voxelization of the space. The voxels facilitate the distinction between occupied and empty space. The identification of unknown volumes is added and considered in the decision process, to cope with the regions invisible to the sensor. As output, we provide a visibility metric and detailed 3D space descriptions on different levels of granularity, including the knowledge of the semantic classes of traversed voxels. During the whole process, the awareness of unknown volumes is added to an otherwise binary decision between visible and invisible targets. Experiments are conducted on the TUM-MLS-2016 dataset. Results demonstrate that the proposed method is feasible for the detection of occlusions by vegetation in the street scenario, and reveal that the identification of unknown volumes proves necessary for a reliable interpretation of the measurements.

Point cloud registration is a fundamental task in both remote sensing, photogrammetry, and computer vision, which is to align multiple point clouds to the same coordinate frame. Especially in LiDAR odometry, by conducting the transformation between two adjacent scans, the pose of the platform can be estimated. To be specific, the goal is to recover the relative six-degree-of-freedom (6 DoF) pose between the source point cloud and the target point cloud. In this paper, we explore the use of robust estimators in the phase correlation when registering two point clouds, enabling a 6 DoF pose estimation between point clouds in a sub-voxel accuracy. The estimator is a rule for calculating an estimate of a given quantity based on observed data. A robust estimator is an estimation rule that is insensitive to nonnormality and can estimate parameters of a given objective function from noisy observations. The proposed registration method is theoretically insensitive to noise and outliers than correspondence-based methods. Three core steps are involved in the method: transforming point clouds from the spatial domain to the frequency domain, decoupling of rotations and translations, and using robust estimators to estimate phase shifts. Since the estimation of transformation parameters lies in the calculation of phase shifts, robust estimators play a vital role in shift estimation accuracy. In this paper, we have tested the performance of six different robust estimators and provide comparisons and discussions on the contributions of robust estimators in the 3D phase correlation. Different point clouds from two urban scenarios and one indoor scene are tested. Results validate the proposed method can reach performance that predominant rotation and translation errors reaching less than 0.5° and 0.5m, respectively. Moreover, the performance of various tested robust estimators is compared and discussed.

Coal fly ash is one of the most promising secondary sources for extracting high value-added rare earth elements. Nevertheless, the majority of rare earth elements in coal fly ash are associated with the aluminosilicate glassy phase, hindering their solubility during the acid leaching process and resulting in the traditional rare earth elements extraction method, which is unavoidably complex in operation and poor in the economy. In this study, prior to the conventional acid leaching, the realization of the coal fly ash activation was considered. This consisted of two steps involving the coal fly ash calcination at the elevated temperatures using recyclable Na2CO3 and the water and alkali washing. It helped in developing the pore structures in coal fly ash, facilitating the leaching solution to rare earth elements, and reducing the acid consumption of rare earth elements leaching. Simultaneously, the generated aqueous solutions could precipitate two new valuable products, the purified silica oxide powder (257.58 g·kg−1, 338.1 m2·g−1 BET, 40 nm grain size, 93.43% purity) and porous zeolites (410.3 g·kg−1). The residual rare earth elements in the pretreated solid residue can be easily extracted, with an extraction efficiency of 91.24% and an acid saving rate of 74.5%. Therefore, a multiple of value-added products can be obtained by this new extraction method with great economic significance.

ObjectiveTo explore the clinical efficacy, characteristics and safety of endoscopic-assisted resection of single-segment posterior longitudinal ligament ossification in the treatment of thoracic spinal stenosis (TSS).MethodFifteen TSS patients, including 6 males and 9 females aged 43–70 years treated with endoscopic-assisted resection of single-segment posterior longitudinal ligament ossification through the transfacet joint approach by our team from November 2016 to June 2020 were retrospectively analyzed. The operation time, intraoperative blood loss, and postoperative complications were recorded. The VAS score, ODI and JOA score (full score, 11 points) were recorded before the operation, after the operation and at the last follow-up to evaluate the clinical efficacy and calculate the improvement rate.ResultsThe ventral side of the spinal cord was decompressed in all patients, providing improvements in neurological symptoms and significant pain relief. The mean follow-up time was 20.27 ± 3.87 months. Mean operation time, intraoperative blood loss, and hospitalization time were found to be 84.80 ± 13.23 min, 36.33 ± 7.41 mL, 5.13 ± 1.02 days; respectively.The JOA score at the last follow-up was 8.6 ± 1.25, which was significantly better than the preoperative (5.53 ± 1.20) and postoperative (6.87 ± 1.31) scores (p

Nowadays, point clouds acquired through laser scanning and stereo matching have deemed to be one of the best sources for mapping urban scenes. Spatial coordinates of 3-D points directly reflect the geometry of object surfaces, which significantly streamlining the 3-D reconstruction and modeling of objects. The construction industry has utilized point clouds in various tasks, including but not limited to, building reconstruction, field inspection, and construction progress tracking. However, it is mandatory to generate a high-level (i.e., geometrically accurate, semantically rich, and simply described) representation of 3-D objects from those 3-D measurements (i.e., points), so that the acquired information can be fully utilized. The reconstruction of 3-D objects in a scene of man-made infrastructure and buildings is one of the core tasks using point clouds, which involves both the 3-D data acquisition and processing. There are few systematic reviews summarizing the ways of acquiring 3-D points and the techniques for reconstructing 3-D objects from point clouds for application scenarios in a built environment or construction site. This article therefore intends to provide a thorough review of the state-of-the-art acquisition and processing techniques for building reconstruction using point clouds. It places particular focus on data acquisition and on the strengths and weaknesses of key processing techniques. This review work will discuss the limitations of current data acquisition and processing techniques, as well as the current research gap, ultimately providing recommendations on future research directions in order to fulfill the pressing needs of the intended construction applications in the foreseeable future.

Interpretation of 3-D scene through LiDAR point clouds has been a hot research topic for decades. To utilize measured points in the scene, assigning unique tags to the points of the scene with labels linking to individual objects plays a crucial role in the analysis process. In this article, we present a supervised classification approach for the semantic labeling of laser scanning points. A novel method for extracting geometric features is proposed, removing redundant and insignificant information in the local neighborhood of the supervoxels. The proposed feature extraction method uses the supervoxel-based local neighborhood instead of points as basic elements, encapsulating the geometric features of local points. Based on the initial classification results, the graph-based optimization is used to spatially smooth the labeling results, based on the graphical model using the perception weighted edges. Benefiting from the graph-based optimization process, our supervised classification method required only a few training datasets. Experiments were carried out by comparing the semantic labeling results with manually generated ground truth datasets. The performance of the proposed methods with different characteristics was analyzed. By using our testing datasets, we have achieved an overall accuracy of better than 0.8 for assigning the measured points to eight semantic classes.

Multimodal images refer to images obtained by different sensors, and there are serious nonlinear radiation differences (NRDs) between multimodal images for photos of the same object. Traditional multimodal image matching methods cannot achieve satisfactory results in most cases. In order to better solve the NRD in multimodal image matching, as well as the rotation and scale problems, we propose a two-step matching method based on co-occurrence scale space combined with the second-order Gaussian steerable filter (G-CoFTM). We first use the second-order Gaussian steerable filter and co-occurrence filter to construct the image’s scale space to preserve the image’s edge and detail features. Secondly, we use the second-order gradient direction to calculate the images’ principal direction, and describe the images’ feature points through improved GLOH descriptors. Finally, after obtaining the rough matching results, the optimized 3DPC descriptors are used for template matching to complete the fine matching of the images. We validate our proposed G-CoFTM method on eight different types of multimodal datasets and compare it with five state-of-the-art methods: PSO-SIFT, CoFSM, RIFT, HAPCG, and LPSO. Experimental results show that our proposed method has obvious advantages in matching success rate (SR) and the number of correct matches (NCM). On eight different types of datasets, compared with CoFSM, RIFT, HAPCG, and LPSO, the mean SRs of G-CoFSM are 17.5%, 6.187%, 30.462%, and 32.21%, respectively, and the mean NCMs are 5.322, 11.503, 8.607, and 16.429 times those of the above four methods.

Efficient dense reconstruction of objects or scenes has substantial practical implications, which can be applied to different 3D tasks (for example, robotics and autonomous driving). However, because of the expensive hardware required and the overall complexity of the all-around scenarios, efficient dense reconstruction using lightweight multi-view stereo methods has received much attention from researchers. The technological challenge of efficient dense reconstruction is maintaining low memory usage while rapidly and reliably acquiring depth maps. Most of the current efficient multi-view stereo (MVS) methods perform poorly in efficient dense reconstruction, this poor performance is mainly due to weak generalization performance and unrefined object edges in the depth maps. To this end, we propose EMO-MVS, which aims to accomplish multi-view stereo tasks with high efficiency, which means low-memory consumption, high accuracy, and excellent generalization performance. In detail, we first propose an iterative variable optimizer to accurately estimate depth changes. Then, we design a multi-level absorption unit that expands the receptive field, which efficiently generates an initial depth map. In addition, we propose an error-aware enhancement module, enhancing the initial depth map by optimizing the projection error between multiple views. We have conducted extensive experiments on challenging datasets Tanks and Temples and DTU, and also performed a complete visualization comparison on the BlenedMVS validation set (which contains many aerial scene images), achieving promising performance on all datasets. Among the lightweight MVS methods with low-memory consumption and fast inference speed, our F-score on the online Tanks and Temples intermediate benchmark is the highest, which shows that we have the best competitiveness in terms of balancing the performance and computational cost.

PurposeThe m5C RNA methylation regulators are closely related to tumor proliferation, occurrence, and metastasis. This study aimed to investigate the gene expression, clinicopathological characteristics, and prognostic value of m5C regulators in triple-negative breast cancer (TNBC) and their correlation with the tumor immune microenvironment (TIM).MethodsThe TNBC data, Luminal BC data and HER2 positive BC data set were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, and 11 m5C RNA methylation regulators were analyzed. Univariate Cox regression and the least absolute shrinkage and selection operator regression models were used to develop a prognostic risk signature. The UALCAN and cBioportal databases were used to analyze the gene characteristics and gene alteration frequency of prognosis-related m5C RNA methylation regulators. Gene set enrichment analysis was used to analyze cellular pathways enriched by prognostic factors. The Tumor Immune Single Cell Hub (TISCH) and Timer online databases were used to explore the relationship between prognosis-related genes and the TIM.ResultsMost of the 11 m5C RNA methylation regulators were differentially expressed in TNBC and normal samples. The prognostic risk signature showed good reliability and an independent prognostic value. Prognosis-related gene mutations were mainly amplified. Concurrently, the NOP2/Sun domain family member 2 (NSUN2) upregulation was closely related to spliceosome, RNA degradation, cell cycle signaling pathways, and RNA polymerase. Meanwhile, NSUN6 downregulation was related to extracellular matrix receptor interaction, metabolism, and cell adhesion. Analysis of the TISCH and Timer databases showed that prognosis-related genes affected the TIM, and the subtypes of immune-infiltrating cells differed between NSUN2 and NSUN6.ConclusionRegulatory factors of m5C RNA methylation can predict the clinical prognostic risk of TNBC patients and affect tumor development and the TIM. Thus, they have the potential to be a novel prognostic marker of TNBC, providing clues for understanding the RNA epigenetic modification of TNBC.

Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L1-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.

The semantic labeling of the urban area is an essential but challenging task for a wide variety of applications such as mapping, navigation, and monitoring. The rapid advance in Light Detection and Ranging (LiDAR) systems provides this task with a possible solution using 3D point clouds, which are accessible, affordable, accurate, and applicable. Among all types of platforms, the airborne platform with LiDAR can serve as an efficient and effective tool for large-scale 3D mapping in the urban area. Against this background, a large number of algorithms and methods have been developed to fully explore the potential of 3D point clouds. However, the creation of publicly accessible large-scale annotated datasets, which are critical for assessing the performance of the developed algorithms and methods, is still at an early age. In this work, we present a large-scale aerial LiDAR point cloud dataset acquired in a highly-dense and complex urban area for the evaluation of semantic labeling methods. This dataset covers an urban area with highly-dense buildings of approximately 1 km2 and includes more than three million points with five classes of objects labeled. Moreover, experiments are carried out with the results from several baseline methods, demonstrating the feasibility and capability of the dataset serving as a benchmark for assessing semantic labeling methods.

In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an indicator and ruler in the evaluation and comparison. In this work, we introduce and present large-scale Mobile LiDAR point clouds acquired at the city campus of the Technical University of Munich, which have been manually annotated and can be used for the evaluation of related algorithms and methods for semantic point cloud interpretation. We created three datasets from a measurement campaign conducted in April 2016, including a benchmark dataset for semantic labeling, test data for instance segmentation, and test data for annotated single 360 ° laser scans. These datasets cover an urban area of approximately 1 km long roadways and include more than 40 million annotated points with eight classes of objects labeled. Moreover, experiments were carried out with results from several baseline methods compared and analyzed, revealing the quality of this dataset and its effectiveness when using it for performance evaluation.

Dense image matching is a crucial step in many image processing tasks. Subpixel accuracy and fractional measurement are commonly pursued, considering the image resolution and application requirement, especially in the field of remote sensing. In this study, we conducted a practical analysis and comparative study on area-based dense image matching with subpixel accuracy for remote sensing applications, with a specific focus on the subpixel capability and robustness. Twelve representative matching algorithms with two types of correlation-based similarity measures and seven types of subpixel methods were selected. The existing matching algorithms were compared and evaluated in a simulated experiment using synthetic image pairs with varying amounts of aliasing and two real applications of attitude jitter detection and disparity estimation. The experimental results indicate that there are two types of systematic errors: displacement-dependent errors, depending on the fractional values of displacement, and displacement-independent errors represented as unexpected wave artifacts in this study. In addition, the strengths and limitations of different matching algorithms on the robustness to these two types of systematic errors were investigated and discussed.

Attitude jitter is a common phenomenon in the application of high resolution satellites, which may result in large errors of geo-positioning and mapping accuracy. Therefore, it is critical to detect and compensate attitude jitter to explore the full geometric potential of high resolution satellites. In this paper, a framework of jitter detection and compensation for high resolution satellites is proposed and some preliminary investigation is performed. Three methods for jitter detection are presented as follows. (1) The first one is based on multispectral images using parallax between two different bands in the image; (2) The second is based on stereo images using rational polynomial coefficients (RPCs); (3) The third is based on panchromatic images employing orthorectification processing. Based on the calculated parallax maps, the frequency and amplitude of the detected jitter are obtained. Subsequently, two approaches for jitter compensation are conducted. (1) The first one is to conduct the compensation on image, which uses the derived parallax observations for resampling; (2) The second is to conduct the compensation on attitude data, which treats the influence of jitter on attitude as correction of charge-coupled device (CCD) viewing angles. Experiments with images from several satellites, such as ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiaometer), LRO (Lunar Reconnaissance Orbiter) and ZY-3 (ZiYuan-3) demonstrate the promising performance and feasibility of the proposed framework.

This article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segmented ground areas for registration.Therefore, the proposed approach offers the possibility to fuse point clouds of different sensors in rural areas within an accuracy of fine registration. In general, such registration is solved with extensive use of control points. The source point cloud is used to calculate a DEM of the ground which is further used to calculate point to raster distances of all points of the target point cloud. Furthermore, each cell of the raster DEM gets a height variance, further addressed as reconstruction accuracy, by calculating the grid. An outlier removal based on a dynamic threshold of distances is used to gain more robustness against noise and small geometry variations. The transformation parameters are calculated with an iterative least-squares optimization of the distances weighted with respect to the reconstruction accuracies of the grid. Evaluations consider two flight campaigns of the Mangfall area inBavaria, Germany, taken with different airborne LiDAR sensors with different point density. The accuracy of the proposed approach is evaluated on the whole flight strip of approximately eight square kilometers as well as on selected scenes in a closer look. For all scenes, it obtained an accuracy of rotation parameters below one tenth degrees and accuracy of translation parameters below the point spacing and chosen cell size of the raster. Furthermore, the possibility of registration of airborne LiDAR and photogrammetric point clouds from UAV taken images is shown with a similar result. The evaluation also shows the robustness of the approach in scenes where a classical iterative closest point (ICP) fails.

The structural safety factor is an essential parameter in aircraft design, representing the ratio of the design load to the operating load. Traditional design methods rely on subjective determination of safety factor values based on experience, lacking objectivity in quantifying uncertainty. However, with advancements in aircraft design technology and increasing competition in the commercial space market, new-generation hypersonic aircraft with complex load environments require a more optimal approach. Applying a uniform safety factor to each component within subregions of the aircraft leads to overly conservative results and impacts flight performance. To address this limitation, a design scheme that incorporates subregional, differentiated safety factors is necessary. This approach allows for better material utilization and ensures compliance with safety requirements. This paper utilizes reliability-based design optimization theory to consider uncertainty in structural systems. It establishes a mapping relationship between structural reliability and differentiated safety factors, providing safety under uncertainty while guaranteeing weight reduction. Additionally, this paper develops a subregional, differentiated safety factors distribution program to determine the safety factors of different subregions of the structure. Consequently, a refined subregional differentiated safety factors scheme that balances safety and economy is derived. [ABSTRACT FROM AUTHOR]