Splicing Method Research Articles

VCU-Net: a vascular convolutional network with feature splicing for cerebrovascular image segmentation.

Cerebrovascular image segmentation is one of the crucial tasks in the field of biomedical image processing. Due to the variable morphology of cerebral blood vessels, the traditional convolutional kernel is weak in perceiving the structure of elongated blood vessels in the brain, and it is easy to lose the feature information of the elongated blood vessels during the network training process. In this paper, a vascular convolutional U-network (VCU-Net) is proposed to address these problems. This network utilizes a new convolution (vascular convolution) instead of the traditional convolution kernel, to extract features of elongated blood vessels in the brain with different morphologies and orientations by adaptive convolution. In the network encoding stage, a new feature splicing method is used to combine the feature tensor obtained through vascular convolution with the original tensor to provide richer feature information. Experiments show that the DSC and IOU of the proposed method are 53.57% and 69.74%, which are improved by 2.11% and 2.01% over the best performance of the GVC-Net among several typical models. In image visualization, the proposed network has better segmentation performance for complex cerebrovascular structures, especially in dealing with elongated blood vessels in the brain, which shows better integrity and continuity.

Formononetin derivatives containing benzyl piperidine: A brand new, highly efficient inhibitor targeting Xanthomonas spp

IntroductionPlant bacterial diseases take an incalculable toll on global food security. The indiscriminate use of chemical synthetic pesticide not only facilitates pathogen resistance of pathogenic bacteria, but also poses a major threat to human health and environmental protection. Therefore, it is of great economic value and scientific significance to develop a new antibacterial drug with environmental friendliness and unique mechanism of action. ObjectivesTo design and synthesize formononetin derivatives based on natural products, evaluate their in vitro and in vivo antibacterial activities and elucidate the mechanisms involved. MethodsThe synthesis was carried out by classical active group splicing method. The antibacterial activities were evaluated using turbidimetry and pot experiments. The antibacterial mechanism was further investigated using scanning electron microscopy (SEM), virulence factors, defense enzymes activities, proteomics and metabolomics. Results40 formononetin derivatives containing benzyl piperidine were designed and synthesized. The antibacterial results demonstrated that H32 exhibited the most potent inhibitory effect against Xanthomonas oryzae pv. Oryzae (Xoo) with the EC50 of 0.07 μg/mL, while H6 displayed the highest inhibitory activity against Xanthomonas axonopodis pv. Citri (Xac) with the EC50 of 0.24 μg/mL. Furthermore, the control efficacy of H32 against rice bacterial leaf blight (BLB) and H6 against citrus canker (CC) was validated through pot experiments. SEM, virulence factors and host enzyme activities assay indicated that H32 could not only reduce the virulence of Xoo, but also activate the activities of defense enzymes and improve the disease resistance of host plants. The proteomics and metabolomics analysis demonstrated that H32 could inhibit the synthesis of branched-chain amino acids, make Xoo cells in a starvation state, inhibit its proliferation, weaken its virulence and reduce its colonization and infection of host cells. ConclusionFormononetin derivatives containing benzyl piperidine could be used as potentially effective inhibitors against Xanthomonas spp.

Design, Synthesis, and Antimicrobial Activity of Amide Derivatives Containing Cyclopropane.

As an important small organic molecule, cyclopropane is widely used in drug design. In this paper, fifty-three amide derivatives containing cyclopropane were designed and synthesized by introducing amide groups and aryl groups into cyclopropane through the active splicing method, and their antibacterial and antifungal activities were evaluated in vitro. Among them, thirty-five compounds were new compounds, and eighteen compounds were known compounds (F14, F15, F18, F20-F26, F36, and F38-F44). Bioassay results disclosed that four, three, and nine of the compounds showed moderate activity against Staphylococcus aureus, Escherichia coli, and Candida albicans, respectively. Three compounds were sensitive to Candida albicans, with excellent antifungal activity (MIC80 = 16 μg/mL). The molecular docking results show that compounds F8, F24, and F42 have good affinity with the potential antifungal drug target CYP51 protein.

Development of preferred sizes for building-integrated lighting units (BILUs)

Building-Integrated Lighting Unit (BILU) is a new type of LED lighting product that is organically integrated with building elements and components. To promote the digital design, standardized production, and assembly construction of new lighting forms, it is necessary to establish Preferred Sizes (PSs) of BILU that are coordinated with building modules. With the aim of reduction in quantity and strength in universality, PSs were selected from BILU modular arrays in this paper. Based on the preliminary survey, a set of target sizes involving building, furniture, and home decoration elements was established to cover all application scenarios of PSs. Through a traversal search of feasible splicing methods of all PSs, the distribution of alternative PSs was analyzed with their universality quantified. Then, multidimensional metrics of alternative PSs were established, including the average feasible scheme proportion and feasible PS proportion of Line-BILU, as well as the feasible coverage-rate proportion and average coverage rate of Surface-BILU. A weighted Euclidean distance method was applied in the multi-criterion decision-making, realizing a quantitative optimization of PSs in multiple scenarios and under multiple objectives. A total of nineteen Line-BILU preferred sizes and sixty-five Surface-BILU preferred sizes were finally obtained, with their feasibility and advantages validated based on existing modular coordination standards and actual lighting project examples. These results can provide data support for establishing product specifications and design standards of BILUs, promote building industrialization from the lighting perspective, and stimulate the sustainable construction of architectural lighting.

LOD2-Level+ Low-Rise Building Model Extraction Method for Oblique Photography Data Using U-NET and a Multi-Decision RANSAC Segmentation Algorithm

Oblique photography is a regional digital surface model generation technique that can be widely used for building 3D model construction. However, due to the lack of geometric and semantic information about the building, these models make it difficult to differentiate more detailed components in the building, such as roofs and balconies. This paper proposes a deep learning-based method (U-NET) for constructing 3D models of low-rise buildings that address the issues. The method ensures complete geometric and semantic information and conforms to the LOD2 level. First, digital orthophotos are used to perform building extraction based on U-NET, and then a contour optimization method based on the main direction of the building and the center of gravity of the contour is used to obtain the regular building contour. Second, the pure building point cloud model representing a single building is extracted from the whole point cloud scene based on the acquired building contour. Finally, the multi-decision RANSAC algorithm is used to segment the building detail point cloud and construct a triangular mesh of building components, followed by a triangular mesh fusion and splicing method to achieve monolithic building components. The paper presents experimental evidence that the building contour extraction algorithm can achieve a 90.3% success rate and that the resulting single building 3D model contains LOD2 building components, which contain detailed geometric and semantic information.

Does Price Level Increase Income Inequality in Pakistan? A Disaggregated Analysis

The crucial emphasis of economic policies is to enhance socio-c wellbeing of the people in an economy. If the society is economically polarized it hampers the impact of economic policies. The crucial economic problem about which fewer economists gutsy to talk is income inequality in the world because it is the answer to a pinching question that who will benefits from the economic growth in an economy. Despite the generation of trillions of dollars each year in the world, a huge population is deprived of basic needs such as food, sanitation, security, health and education etc. When it comes to the effect of inflation on masses the situation further aggravates. Escalated price levels deprive more people from essential goods and lead towards increasing income inequality. This study has tried to estimate the impact of inflation on income distribution in Pakistan. The data was collected from Pakistan Economic Survey and Handbook of Statistics by SBP for a period of 1990 to 2022. The present study has applied Auto Regressive Distributed Lag Model (ARDL) to estimate the linkage among price levels and income inequality in Pakistan. The findings of the study have revealed both positive and negative linkage among constituents of the CPI and the income inequality. The novelty of this study is on two grounds. Firstly, the study has dissolved Consumer Price Index (CPI) into its constituents, and their Impact on income inequality has been examined, as far as finest information available to us this has never been investigated in Pakistan before. Secondly, the study has applied two proxies the GINI coefficient and Mean Log Deviation (MLD) to estimate income inequality. Thirdly, we have applied the data splicing method[1] to make one base year of CPI from various base years that has been changed over the past years in Pakistan. The study offers some useful policy implications for policy formulation in Pakistan.

A Method for Full-Depth Sound Speed Profile Reconstruction Based on Average Sound Speed Extrapolation

The speed of sound in seawater plays a crucial role in determining the accuracy of multibeam bathymetric measurements. In deep-sea multibeam measurements, the challenge of inadequate longitudinal coverage of sound speed profiles arises from variations in seafloor topography, meteorological conditions, measurement equipment, and operational efficiency, resulting in diminished measurement precision. Building upon the EOF (Empirical Orthogonal Function), a method employed to analyze spatiotemporal data such as sound speeds, this paper addresses the limitations of the EOF method caused by the shallowest sampling depth of the sound speed profile samples. It proposes two methods for EOF reconstruction of measured sound speed profiles extended to full water depth by splicing measured sound speed profiles at non-full water depths with historical average sound speed profiles of the surveyed sea area. Specially, Method 2 introduces the latest metaheuristic optimization algorithm, CPO (Crested Porcupine Optimizer), which exhibited superior performance on multiple standard test functions in 2024. The study reconstructs randomly sampled measured sound speed profiles using the two proposed methods and commonly employed substitution and splicing methods, followed by a comparative analysis of the experimental outcomes. At a sampling depth of 200 m, Method 2 demonstrates performance superior to other methods, with RMSE, MAE, MAPE, and R2 values of 0.9511 m/s, 0.8492 m/s, 0.0566%, and 0.9963, respectively. Method 1 yields corresponding values of 0.9594 m/s, 0.8492 m/s, 0.0568%, and 0.9962, respectively. Despite its slightly inferior performance compared with Method 2, it offers substantial advantages over the substitution and splicing methods. Varying the sampling depth of measured sound speed profiles reveals that Methods 1 and 2 exhibit inferior reconstruction performance in shallow water compared with the substitution and splicing methods. Nevertheless, when the sampling depth surpasses the depth range of initial spatial modes with abrupt variations, both methods achieve notably higher reconstruction accuracy compared with the substitution and splicing methods, reaching a stabilized state. Sound ray tracing reveals that the reconstructed sound speed profiles from both methods meet the stringent accuracy standards for bathymetric measurements, achieving an effective beam ratio of 100%. The proposed methods not only provide rapid reconstruction of sound speed profiles, thereby improving the efficiency of multibeam bathymetric surveys, but also provide references for the reasonable determination of sampling depths of sound speed profiles to ensure reconstruction accuracy, demonstrating practical application value.

Experimental Study on Flexural Performance of lap-spliced and mechanical-spliced (clamp type) rebars in RC Beams

Various methods can be employed to splice reinforcement bars, including lap splicing, welding, and mechanical splicing. Lap splicing is the most used method among these techniques. However, this study primarily focuses on evaluating the potential of mechanical splicing to replace the lap splice method. The investigation involved assessing the flexural performance of six RC beams in a series of laboratory experiments. These beams represented a control beam, an RC beam with lap-spliced rebars, and an RC beam with clamp-type mechanical spliced rebars. The laboratory testing results indicated that the RC beam with lap-spliced rebars had the highest load-bearing capacity, resulting in the highest nominal flexural moment (Mn). In contrast, the control beam demonstrated the highest deflection, signifying a greater level of ductility. However, RC beams with mechanical splices (clamp type), while not achieving the highest flexural moment and ductilities, are still competitive, as the experiment revealed that the difference is not significant. Consequently, it can be concluded that mechanical splicing has the potential to compete with or replace lap splicing in RC beams.

Seismic behavior of horizontally spliced single-side superposed shear wall using a concealed column

The single-side superposed (SSS) shear wall is an innovative precast concrete sandwich panel that better meets the requirements of building industrialization. However, the seismic behavior of SSS shear walls remains inadequately understood. In this paper, a full-scaled horizontally spliced SSS shear wall using a concealed column and an integral SSS shear wall of the same dimensions were tested. The seismic behavior was investigated considering various aspects including hysteresis performance, bearing capacity, ductility performance, energy dissipation capacity, and stiffness degradation curve. The simulation models were established by the Abaqus, which provided the verification and further research for the test. Furthermore, two innovative splicing forms were proposed to optimize the existing specimens through the Abaqus. The experimental results indicated that implementing the vertical joint would lead to a decrease in the yield-bearing capacity by 24%, but it would also enhance the ultimate bearing capacity by 12.7%. Meanwhile, the deformation capacity would improve by 9.7%. The numerical simulations demonstrated that the suggested splicing methods effectively meet the demands of building industrialization.

Multilateral indices in official price statistics and a new additive splicing method

A substitution of the traditional data sources allows National Statistical Institutes to collect large samples of product items with corresponding prices recorded over more frequent time periods. For some product categories, a frequent replacement of product items requires a dynamic approach to define a product sample that can be achieved by a monthly update of the sample. In a month-on-month comparison of price changes, bilateral indices must be chained that can lead to a chain drift bias. Multilateral methods are designed to reduce the chain drift, albeit not completely eliminate it. A disadvantage of multilateral methods is its ambiguous interpretability and a revision problem. The main objective of this paper is to show an implementation of multilateral methods for official price statistics in practice. The multilateral GEKS method is decomposed into elementary components that identifies the effect of successive price relatives used in its compilation. An importance of the decomposition is shown when interpreting the GEKS index in the context of official price statistics. We demonstrate that it can lead to an inaccurate public dissemination of the consumer price index. Moreover, we propose a new method for the splice extension that is required to avoid the revision of previously published indices. The method uses a Stein-type shrinkage estimator for the splice extension that is applied additively to the last published index. In the empirical study, we use web scraped data, which are obtained from the Slovak market, for comparison of bilateral and multilateral indices.

LADM procedure to find the analytical solutions of the nonlinear fractional dynamics of partial integro-differential equations

Abstract Generally, fractional partial integro-differential equations (FPIDEs) play a vital role in modeling various complex phenomena. Because of the several applications of FPIDEs in applied sciences, mathematicians have taken a keen interest in developing and utilizing the various techniques for its solutions. In this context, the exact and analytical solutions are not very easy to investigate the solution of FPIDEs. In this article, a novel analytical approach that is known as the Laplace adomian decomposition method is implemented to calculate the solutions of FPIDEs. We obtain the approximate solution of the nonlinear FPIDEs. The results are discussed using graphs and tables. The graphs and tables have shown the greater accuracy of the suggested method compared to the extended cubic-B splice method. The accuracy of the suggested method is higher at all fractional orders of the derivatives. A sufficient degree of accuracy is achieved with fewer calculations with a simple procedure. The presented method requires no parametrization or discretization and, therefore, can be extended for the solutions of other nonlinear FPIDEs and their systems.

First Report of Lily Virus X Infecting Paris polyphylla var. yunnanensis in Yunnan, China

Lily virus X (LVX) is a positive-sense ssRNA virus belonging to the genus Potexvirus in the family Alphaflexiviridae. LVX is known to infect plants of the genera Lilium and Tricyrtis in the family Liliacea. LVX was first reported in an asymptomatic lily (Lilium formosanum) from England (Stone, 1980), but has been shown to infect plants in the Netherlands (Chen et al. 2005), the United States (Jordan et al. 2008) and Japan (Nijo et al. 2018). To date, the complete genomes of two LVX isolates from the Netherlands and Japan have been reported. Paris polyphylla var. yunnanensis, known as Dianchonglou in China, is a perennial plant of the family Melanthiaceae (formerly belonging to the family Trillium). In China, its rhizome is commonly used as an antispasmodic agent for stroke and cancer treatment (Chang et al. 2017). From 2019 to 2022, leaf mottle and shrinkage which are typical symptoms of viral infections were observed on the leaves of P. polyphylla var. yunnanensis plants in Dianchonglou fields in Qujing, Yunnan. Disease incidence ranged from 19% to 45% across 5 fields (90 plants per field) in Qujing. To identify the possible viral pathogen(s) associated with the disease, the mirVanaTM miRNA isolation Kit was used to extract total RNA was from a mixed sample pool of 5 symptomatic leaf samples collected from the 5 fields. RNA sequencing library was constructed using TruSeqTM RNA sample preparation kit. Sequencing on the Illumina HiSeqTM 2500 platform (Illumina, USA) with 125-bp paired-end reads yielded 23,077,786 raw reads. 22,534,100 clean reads were obtained by removing reads of low quality and poly-N using Trimmomatic software (Bolger et al. 2014). By utilizing the paired-end splicing method in Trinity software (Grabherr et al. 2011) the the raw reads were De novo assembled into 184,596 contigs, of which 303 were related to viruses, including Paris mosaic necrosis virus (PMNV), Pear alphapartitivirus (PAPV), Dahlia mosaic virus (DMV), and Lily virus X (LVX). BLASTn analysis revealed that 12 contigs (lengths ranging from 344 nt to 5,981 nt, query cover 6% to 99%) were most similar (57.32% to 91.67% nt identities) to the genome sequences of LVX, suggesting a possible infection of LVX in the plants. To confirm the result, a full-length genomic sequence of LVX was obtained by reverse transcription polymerase chain reaction (RT-PCR) using specific primers designed based on the sequence of the assembled contigs. The PCR products were cloned into pGEM-T vector (Promega Corporation, USA) and sequenced using the Sanger method (Sangon Biotech, Shanghai, China). The obtained full-length genomic sequence of the LVX isolate (LVX-PP, accession number OM100017) was 5,981 nt in length. BLASTp analysis demonstrated that the putative Rep and CP of LVX-PP shared 76.27% to 81.05% and 80.81% to 81.82% aa sequence similarities with that of other LVX isolates, respectively. Maximum-likelihood phylogenetic trees inferred from the Rep and CP aa sequences showed that LVX-PP clustered closely with LVX isolates. The leaf samples were further analyzed using a lily virus X (LVX) ELISA kit (DEIAPV181, Creative Diagnostics, U.S.A.). Healthy P. polyphylla var. yunnanensis leaves were taken as a negative control and buffer solution as a blank control. The results showed a positive reaction for all five symptomatic plants (OD = 1.259 ± 0.007) relative to the negative (OD = 0.099) and blank (OD = 0.073) controls. These results indicate that LVX can infect P. polyphylla var. yunnanensis. To our knowledge, this is the first report that LVX has been detected in P. polyphylla var. yunnannensis. This study will serve as an important reference for the study of the host range of LVX. Further studies will be required to determine how LVX spreads between P. polyphylla var. yunnannensis and other host plants.

Research on the method of diesel particulate filters carbon load recognition based on deep learning

Because the carbon load inside a diesel particulate filters (DPF) affects the DPF regeneration, and the carbon load recognition is significant for the particulate matter (PM) emission control. It is necessary to investigate an on-board DPF carbon load recognition method because the carbon load cannot be directly measured by sensors. Aiming to build a DPF carbon load prediction model adopting the deep learning method, this paper proposes a DPF carbon load identification model based on different experimental parameters using a layered one dimension convolutional neural network (1D-CNN) method. To improve data validity, this paper adopts two data-processing methods. The data pre-processing adopts data splicing method to complete the construction of the original sample set, and the data after-processing uses wavelet packet transform method to establish the feature sample sets. The model adopts the optimal feature dataset constructed by three input parameters, i.e., temperature difference, pressure difference, and exhaust mass flow, and has both high training accuracy and test accuracy above 90 %. The pressure difference is the most important influencing input parameter, and the three-parameter sample set (ΔT + ΔP + Q) has great recognition accuracy and good model stability with the high training accuracy and test accuracy as well as less iteration.

A Magnet Splicing Method for Constructing a Three-Dimensional Self-Decoupled Magnetic Tactile Sensor

Tactile sensory organs for three-dimensional (3D) force perception are essential for most living organisms and enable them to perform complex and sophisticated tasks to survive and evolve. Magnetic-based tactile sensors have been developed rapidly in recent years due to the exploitability of 3D force decoupling. Here, a method of magnet splicing is introduced, which can be applied to a magnetic tactile sensor to realize 3D self-decoupling of magnets’ displacements. This method enables the magnets to produce a completely consistent magnetic field distribution as the ideal magnetization model within a certain working range, eliminating the compensation and correction of the 3D magnetic flux density signal. This method carves out a new way for the practical application of 3D decoupling theory, showcasing the great potential in the fields of magnetic sensors and magnetic actuators.

Signal-to-Noise Ratio Enhancement of MR Images With Variable Gains and K-Space Splicing Method

Signal-to-Noise Ratio Enhancement of MR Images With Variable Gains and K-Space Splicing Method

Diversity Design of Traditional Cultural Elements in Interior Design Based on Combinatorial Optimization Algorithm

Abstract There are great limitations in the imagination and play space of traditional interior design methods, and it is very costly to make changes after the design is completed. For this reason, this paper combines and optimizes SIFT and image splicing methods to recognize traditional cultural elements in images. Gray scale processing is performed on the image, unstable edge response points are removed and low contrast feature points are filtered, and the Gaussian pyramid is established by convolving the image with the Gaussian kernel function under different scale factors to establish the dimensions of the feature points. On this basis, an image splicing optimization algorithm that combines the two-dimensional information entropy of the image is proposed to divide the image into detail regions and flat regions, assign corresponding weights to the feature points in the optimization stage of the uni-responsive matrix, and minimize the residual sum to obtain the optimal uni-responsive matrix using the Levenberg-Marquardt algorithm, to improve the splicing quality. In the actual design test, the Chinese chair has the lowest success rate of 94.1%, and the output quality is 0.9215 and 0.9527. The output quality decreases as the scene becomes more complex. The SSIM and DoEM values for the Chinese chair are 0.9582 and 0.9682 for a simple scenario and decrease to a minimum of 0.8543 and 0.8472 as the number of interior pieces and other items in the scenario increases. By proposing the approach in this paper, labor costs and expenses can be saved and a new direction for interior design development can be taken.

Synthesis of straight-chain C49H100 alkane using a modular splicing strategy

Ultra-long n-alkanes are highly valuable in both scientific research and as major constituents of specialty high-melting-point waxes. Unlike conventional methods (e.g., Fischer–Tropsch (FT), ethylene oligomerization, and polyethylene cracking) typically resulting in wide n-alkane distributions, the elaborate design strategy presented herein allows the direct synthesis of pure, long n-alkanes using a modular splicing method with acetone, furfural, and fatty acid anhydrides or acyl chlorides as bio-blocks. The herein approach is based on a simple four-step catalytic reaction scheme involving C–C chain elongation and C–O bond activation. The synthesized pure n-alkanes had a carbon chain length as high as C49 (total yield of 49%). The synthesis approach also allows to selectively prepare n-alkanes with even and odd carbon numbers ranging from C15 to C49. This process represents a great breakthrough in the synthesis of long-chain pure n-alkanes, surpassing the carbon number limitations reported in previous methodologies.

Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarity

Symbolic event recognition systems detect event occurrences using first-order logic rules. Although existing online structure learning approaches ease the discovery of such rules in noisy data streams, they assume the existence of fully labelled training data. Splice is a recent online graph-based approach that estimates the labels of unlabelled data and makes it possible to learn such rules from semi-supervised training sequences of logical interpretations. However, Splice labelling depends significantly on the metric used to compute the distances of unlabelled examples to their labelled counterparts. Moreover, there is no guarantee about the quality of the labelling found in the local graphs that are built as the data stream in. In this paper, we propose a new online learning method, which includes an enhanced hybrid measure that combines an optimised structural distance, and a data-driven one. The former is guided by feature selection targeted to kNN classification, while the latter is a mass-based dissimilarity. Additionally, the enhanced Splice method, improves the graph construction process, by storing a synopsis of the past, in order to achieve more informed labelling on the local graphs. We evaluate our approach by learning Event Calculus theories for the tasks of human activity recognition, maritime monitoring, and fleet management. The evaluation suggests that our approach outperforms its predecessor, in terms of inferring the missing labels and improving the predictive accuracy of the underlying structure learning system.

Circular RNA: A new expectation for cardiovascular diseases.

Circular RNA (circRNA) is a class of RNA with the 5' and 3' ends connected covalently to form a closed loop structure and characterized by high stability, conserved sequences and tissue specificity, which is caused by special reverse splicing methods. Currently, it has become a hot spot for research. With the discovery of its powerful regulatory functions and roles, the molecular mechanisms and future value of circRNA in participating in and regulating biological and pathological processes are becoming increasingly apparent. Among them is the increasing prevalence of cardiovascular diseases (CVDs). Many studies have elucidated that circRNA plays a crucial role in the development and progression of CVDs. Therefore, circRNA shows its advantages and brilliant expectations in the field of CVDs. In this review, we describe the biogenesis, bioinformatics detection and function of circRNA and discuss the role of circRNA and its effects on CVDs, including atherosclerosis, myocardial infarction, cardiac hypertrophy and heart failure, myocardial fibrosis, cardiac senescence, pulmonary hypertension, and diabetic cardiomyopathy by different mechanisms. That shows circRNA advantages and brilliant expectations in the field of CVDs.

A multi-frame deformation velocity splicing method for wide-area InSAR measurement based on uncontrolled block adjustment: A case study of long-term deformation monitoring in Guangdong, China

Wide-area deformation measurements require multi-frame InSAR datasets for joint monitoring due to the limited swath width of SAR images. However, variations in the stability of reference points, error signal magnitude and distribution, and spatio-temporal filtering parameters result in different deformation rates between adjacent frames, leading to centimeter-level errors in the results. To obtain spatially consistent wide-area results, here we propose a multi-frame deformation velocity splicing method that combines InSAR datasets and block adjustment. We convert the line-of-sight deformation of each frame to vertical using an improved vertical deformation solution method. We construct an uncontrolled block adjustment model and use the results in the automatically identified overlapping areas as redundant observations to solve the deviation of each frame in a wide area. Finally, the multi-frame seamless results are sampled into a uniform spatial resolution cell network, to generate a wide-area deformation product with consistent reference. We validate the proposed method in Guangdong Province, China, covering an area of 1.96 × 105 km2. By correcting and fusing a total of 78 ALOS-1/PALSAR frames (2007–2010) and 11 Sentinel-1 frames (2015–2021) separately, we obtain the wide-area deformation products for Guangdong during the two periods. Compared with the original results, the inner precision of the ALOS-1/PALSAR and Sentinel-1 data is improved by 82.4% and 73.7%, respectively. Compared with GNSS, the accuracy of the corrected Sentinel-1 results is improved by more than 19%. The proposed method contributes to standardizing InSAR deformation production and facilitating the engineering application of wide-area InSAR measurements.