Pixel Selection Research Articles

Real-time deconvolution of light fields through pixel selection in the point-spread-function and direct inversion of the image formation process

This paper explores the optimization of light field deconvolution, a key process in image processing that reconstructs a 3D object space or a 2D refocus plane from a light field. Despite the critical role of deconvolution in light field technology, existing methods are often slow, computationally intensive, and unsuitable for real-time processing. Existing algorithms, such as the Richardson-Lucy approach, while groundbreaking, still suffer performance limitations due to their iterative nature and high computational costs. Central to our approach is the strategic selection of influential pixels within the point-spread-function, reducing redundant computations by focusing only on pixels contributing to a significant portion of the point-spread-function’s total intensity. In addition, we explore the potential to directly invert the image formation model, bypass iterative computations, and further accelerate the deconvolution process. Our findings reveal notable improvements in computational efficiency, with some of our methods achieving real-time performance. The reconstruction quality, measured using metrics such as the mean squared error, remained comparable to existing approaches, indicating a favorable balance between speed and reconstruction quality.

Enhanced Dual-Layer Video Steganographic Approach: Enhancing Security and Imperceptibility

In a digital age where the role of digital information is continually expanding, the imperative to ensure secure communication and data protection has intensified. Steganography, a field that conceals sensitive information within apparently innocuous carriers like multimedia files, holds promise for achieving this goal. Recent times have seen a growing interest in video-based steganography, driven by the proliferation of videos online. However, a review has unveiled a noteworthy gap in efforts to increase the safety of video steganography employing the popular Least Significant Bit (LSB) process. LSB steganography is at risk to exploits that could jeopardize the confidentiality of the concealed message. In this research, we present a video steganographic solution achieved through the integration of advanced encryption algorithms and a dual approach involving randomized frame selection using the Fisher-Yates method and pixel selection utilizing the 8-directional technique. Additionally, we have devised an upgraded imperceptible video steganographic approach in the spatial domain, capitalizing on an XOR-based LSB substitution embedding approach. The outcomes underscore the effectiveness of our proposed methodology, revealing significant imperceptibility when compared to recent solutions, thereby reinforcing data security against unauthorized access. This comprehensive solution holds wide-ranging applications across various domains, providing heightened security and confidentiality for sensitive information. Notable applications include secure communications within intelligence agencies, safeguarding patient data in medical sciences, ensuring privacy in video surveillance, fortifying video transmission against errors and data breaches, securing business communications, preventing data leakage, and enhancing rights management for digital content.

Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring

Monitoring displacement at transport infrastructure using Sentinel‑1 Interferometric Synthetic Aperture Radar (InSAR) faces challenges due to the sensor’s medium spatial resolution, which limits the pixel coverage over the infrastructure. Therefore, carefully selecting coherent pixels is crucial to achieve a high density of reliable measurement points and to minimize noisy observations. This study evaluates the effectiveness of various pixel selection methods for displacement monitoring within transport infrastructures. We employ a two-step InSAR time series processing approach. First, high-quality first-order pixels are selected using temporal phase coherence (TPC) to estimate and correct atmospheric contributions. Then, a combination of different pixel selection methods is applied to identify coherent second-order pixels for displacement analysis. These methods include amplitude dispersion index (ADI), TPC, phase linking coherence (PLC), and top eigenvalue percentage (TEP), targeting both point-like scatterer (PS) and distributed scatterer (DS) pixels. Experiments are conducted in two case studies: one in Germany, characterized by dense vegetation, and one in Spain, with sparse vegetation. In Germany, the density of measurement points was approximately 30 points/km², with the longest segment of the infrastructure without any coherent pixels being 2.8 km. In Spain, the density of measurement points exceeded 500 points/km², with the longest section without coherent pixels being 700 meters. The results indicate that despite the challenges posed by medium-resolution data, the sensor is capable of providing adequate measurement points when suitable pixel selection methods are employed. However, careful consideration is necessary to exclude noisy pixels from the analysis. The findings highlight the importance of choosing a proper method tailored to infrastructure characteristics. Specifically, combining TPC and PLC methods offers a complementary set of pixels suitable for displacement measurements, whereas ADI and TEP are less effective in this context. This study demonstrates the potential of Sentinel‑1 InSAR for capturing both regional-scale and localized displacements at transport infrastructure.

An improved time series SAR interferometry (TSInSAR) for investigating earthquake-induced active unstable slopes (AUS) in Pakistan

ABSTRACT Time series interferometric SAR (TSInSAR) techniques face challenges related to the availability of highly coherent point targets in non-urban, vegetated, and mountainous regions. In this study, we tested an improved TSInSAR approach with the help of distributed scatterers (DS) detected through the fast statistically homogeneous pixel selection (faSHP) approach and jointly processed with persistent scatterers (PS) within the interferometric point target analysis (IPTA) framework. The method is applied to a seismically active and highly gradient terrain to investigate the earthquake-induced active unstable slopes (AUS) in the Muzaffarabad-Balakot region of northern Pakistan, particularly using sixteen PALSAR-2 images acquired between October 2014 and May 2020. However, its effectiveness was also assessed by using Sentinel-1 data from the same time period. The quantitative assessment showed the effectiveness of the method in delineating the sliding surfaces of the unstable slopes and achieving approximately four times higher point density as compared to the standard PSI approach when applied to PALSAR-2 data and approximately nine times higher point targets when Sentinel-1 data is used. Exhibiting an average deformation rate varying between −40 mm yr−1 and 20 mm yr−1 along the line of sight (LOS) direction, the obtained results from PALSAR-2 data revealed the existence of 451 AUS in the region. The study also reports the discovery of a giant destabilized slope (~2.30 sq. km) located near Patika Village along the Neelam River. The majority of these unstable slopes are found at altitudes above 780 m, with slope angles ranging from 15 to 50 degrees. The study also found a significant correlation between deformation patterns and local precipitation. The rainwater gradually penetrates into the joints and cracks, accelerating the processes of deformation and slope failure. The research and its findings could help decision-making entities by providing first-hand information for managing the risks associated with slope failure.

Determining pair distribution functions of thin films using laboratory-based X-ray sources.

This article demonstrates the feasibility of obtaining accurate pair distribution functions of thin amorphous films down to 80 nm, using modern laboratory-based X-ray sources. The pair distribution functions are obtained using a single diffraction scan without the requirement of additional scans of the substrate or of the air. By using a crystalline substrate combined with an oblique scattering geometry, most of the Bragg scattering of the substrate is avoided, rendering the substrate Compton scattering the primary contribution. By utilizing a discriminating energy filter, available in the latest generation of modern detectors, it is demonstrated that the Compton intensity can further be reduced to negligible levels at higher wavevector values. Scattering from the sample holder and the air is minimized by the systematic selection of pixels in the detector image based on the projected detection footprint of the sample and the use of a 3D-printed sample holder. Finally, X-ray optical effects in the absorption factors and the ratios between the Compton intensity of the substrate and film are taken into account by using a theoretical tool that simulates the electric field inside the film and the substrate, which aids in planning both the sample design and the measurement protocol.

Investigating the method of selection of background pixel values for the calibration of EBT‐XD film dosimetry

AbstractPurposeThis study investigates three different calibration methods for the selection of background pixel intensity.MethodsFilm‐by‐Film (FBF) Method: Each film serves as its own control. Batch‐by‐Film (BBF) Method: A single film is used as a control for all calibration films. Generic (GEN) Method: A generic value (65535) is used as the background pixel value for all calibration films.Three calibration curves were established for the red, green, blue, and RGB channels, and the Radbard NIH (image) curve‐fitting model was used to predict the dose. Sensitivity at different dose levels was quantified by calculating the first derivative of each color channel.ResultsThe GEN method exhibited a difference of up to 6% between the predicted and delivered doses below 2 Gy. The changes in optical density when using the GEN method differed significantly (p<0.0001) from those of the FBF and BBF methods. In the dose range 5–30 Gy, the percentage difference between the predicted and delivered doses for the FBF, BBF, and GEN methods was within 2%. Both the red and green channels demonstrated higher sensitivity than the blue channel over the dose range of 2–30 Gy.ConclusionsThe FBF method is more accurate than the BBF and GEN methods because it accounts for inter‐film variations. The Radbard NIH (image) curve‐fitting function proved suitable for predicting the dose for all the three calibration methods.

Predictive monitoring of soil organic carbon using multispectral UAV imagery: a case study on a long-term experimental field

Effective monitoring of the soil organic carbon (SOC) content at the field scale is crucial for supporting sustainable agricultural practices. This study evaluates the utility of multispectral data acquired by an unmanned aerial vehicles (UAV) during bare soil conditions for predicting the SOC content of a long-term experimental field site (LTE) in Saxony-Anhalt, Germany. Our methodology involves constructing predictive models using multiple algorithms (CUBIST, MARS, linear regression) and applying image correction techniques to enhance prediction accuracy by mitigating the influence of confounding factors such as crop residuals. Among the tested models, the CUBIST algorithm, combined with a pixel selection strategy employing a 2 m radius and stratified image correction, demonstrates the most promising results, achieving an R-squared value of 0.54 and an RMSE of 1.9 g kg−1. Spatial distribution maps generated by this optimized model effectively depict the impact of organic fertilization on the SOC content, although the clarity of these patterns varies depending on the image processing method and algorithm used. Our findings highlight the potential of utilizing UAV-derived multispectral data for SOC monitoring at the LTE scale. However, further research is warranted to assess the generalizability of this approach to agricultural fields with lower SOC variability.

Hybrid machine learning-based breast cancer segmentation framework using ultrasound images with optimal weighted features.

One of the most dangerous conditions in clinical practice is breast cancer because it affects the entire life of women in recent days. Nevertheless, the existing techniques for diagnosing breast cancer are complicated, expensive, and inaccurate. Many trans-disciplinary and computerized systems are recently created to prevent human errors in both quantification and diagnosis. Ultrasonography is a crucial imaging technique for cancer detection. Therefore, it is essential to develop a system that enables the healthcare sector to rapidly and effectively detect breast cancer. Due to its benefits in predicting crucial feature identification from complicated breast cancer datasets, machine learning is widely employed in the categorization of breast cancer patterns. The performance of machine learning models is limited by the absence of a successful feature enhancement strategy. There are a few issues that need to be handled with the traditional breast cancer detection method. Thus, a novel breast cancer detection model is designed based on machine learning approaches and employing ultrasonic images. At first, ultrasound images utilized for the analysis is acquired from the benchmark resources and offered as the input to preprocessing phase. The images are preprocessed by utilizing a filtering and contrast enhancement approach and attained the preprocessed image. Then, the preprocessed images are subjected to the segmentation phase. In this phase, segmentation is performed by employing Fuzzy C-Means, active counter, and watershed algorithm and also attained the segmented images. Later, the segmented images are provided to the pixel selection phase. Here, the pixels are selected by the developed hybrid model Conglomerated Aphid with Galactic Swarm Optimization (CAGSO) to attain the final segmented pixels. Then, the selected segmented pixel is fed in to feature extraction phase for attaining the shape features and the textual features. Further, the acquired features are offered to the optimal weighted feature selection phase, and also their weights are tuned tune by the developed CAGSO. Finally, the optimal weighted features are offered to the breast cancer detection phase. Finally, the developed breast cancer detection model secured an enhanced performance rate than the classical approaches throughout the experimental analysis.

An improved measurement pixel selection method of GB-MIMO radar image applied for bridge vibration measurement

An improved measurement pixel selection method of GB-MIMO radar image applied for bridge vibration measurement

On the pixel selection criterion for the calculation of the Pearson's correlation coefficient in fluorescence microscopy.

Colocalisation microscopy analysis provides an intuitive and straightforward way of determining if two biomolecules occupy the same diffraction-limited volume. A popular colocalisation coefficient, the Pearson's correlation coefficient (PCC), can be calculated using different pixel selection criteria: PCCALL includes all image pixels, PCCOR only pixels exceeding the intensity thresholds for either one of the detection channels, and PCCAND only pixels exceeding the intensity thresholds for both detection channels. Our results show that PCCALL depends on the foreground to background ratio, producing values influenced by factors unrelated to biomolecular association. PCCAND focuses on areas with the highest intensities in both channels, which allows it to detect low levels of colocalisation, but makes it inappropriate for evaluating spatial cooccurrence between the signals. PCCOR produces values influenced both by signal proportionality and spatial cooccurrence but can sometimes overemphasise the lack of the latter. Overall, PCCAND excels at detecting low levels of colocalisation, PCCOR provides a balanced quantification of signal proportionality and spatial coincidence, and PCCALL risks misinterpretation yet avoids segmentation challenges. Awareness of their distinct properties should inform their appropriate application with the aim of accurately representing the underlying biology.

Phase-Based Similarly Decorrelated Pixel Selection and Phase-Linking in InSAR using Circular Statistics

Phase-Based Similarly Decorrelated Pixel Selection and Phase-Linking in InSAR using Circular Statistics

Privacy‐preserving in the smart healthcare system using steganography and chaotic functions based on DNA

AbstractThe smart healthcare system is one of the Internet of Things‐based applications that are increasingly used nowadays. One of the requirements of this system is privacy‐preserving, which should protect the disclosure of the patient record contents. In the present paper, a combination of chaotic functions and a new DNA‐based steganography method, and image blocking are suggested to protect patients' privacy‐preserving. First, the image is blocked, and the initial key for the chaotic function will be gained by using the centers of the blocks. Then the data and image are transformed into DNA sequences. Finally, one of the randomly chosen DNA strands of data is XOR with the center of one randomly selected block and will be placed in one of the block pixels. The failure to send the initial key separately to generate random numbers, as well as the random selection of secret data bits and image pixels for steganography has increased the security of the proposed method. The simulation results not only indicate the quality of the stego‐image but also show better performance of the proposed method than the existing methods.

Surface temperature adjustment in METRIC model for monitoring crop water consumption in North China Plain

Evapotranspiration is the real water consumption element in agricultural water management. In order to improve the efficiency of agricultural water use and maintain the sustainable development of agriculture, more and more attention has been paid to the improvement of monitoring and management of farmland evapotranspiration. In this study, MODIS data and the Temperature Vegetation Dryness Index (TVDI) were used in the METRIC model to estimate evapotranspiration for winter wheat and summer corn in the North China Plain. In order to decrease the error caused by the spatial heterogeneity of air temperature in the estimation of evapotranspiration, based on the characteristics of cold pixels with surface temperatures close to their air temperature, the TVDI was used to assist the selection of cold pixels and obtain the spatial variation trend of air temperature with latitude from all cold pixels in the study area. The influence of the spatial heterogeneity of air temperature on the surface temperature at the time of the satellite overpass was corrected accordingly, thereby the estimation accuracy of evapotranspiration was improved. A METRIC-TVDI model was established and the evapotranspiration of winter wheat and summer corn in Hebei Plain was estimated from 2017 to 2019. The average evapotranspiration of the winter wheat over these three years were 361 mm, 327 mm and 354 mm respectively, and that of summer corn were 394 mm, 388 mm and 405 mm respectively. Based on the farmland water balance, the irrigation water consumption (evapotranspiration minus precipitation) of winter wheat and summer corn rotation were 324 mm, 134 mm and 360 mm respectively. This kind of crop rotation pattern consumed more groundwater for irrigation, especially the winter wheat. In order to realize the balance of groundwater between exploitation and replenishment in Hebei Plain, more water-saving irrigation technologies should be adopted to reduce the evapotranspiration and improving water use efficiency.

Systematic reduction of hyperspectral images for high-throughput plastic characterization

Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, and has diverse applications in food quality control, pharmaceutical processes, and waste sorting. However, due to the large size of HSI datasets, it can be challenging to analyze and store them within a reasonable digital infrastructure, especially in waste sorting where speed and data storage resources are limited. Additionally, as with most spectroscopic data, there is significant redundancy, making pixel and variable selection crucial for retaining chemical information. Recent high-tech developments in chemometrics enable automated and evidence-based data reduction, which can substantially enhance the speed and performance of Non-Negative Matrix Factorization (NMF), a widely used algorithm for chemical resolution of HSI data. By recovering the pure contribution maps and spectral profiles of distributed compounds, NMF can provide evidence-based sorting decisions for efficient waste management. To improve the quality and efficiency of data analysis on hyperspectral imaging (HSI) data, we apply a convex-hull method to select essential pixels and wavelengths and remove uninformative and redundant information. This process minimizes computational strain and effectively eliminates highly mixed pixels. By reducing data redundancy, data investigation and analysis become more straightforward, as demonstrated in both simulated and real HSI data for plastic sorting.

Local imperceptible adversarial attacks against human pose estimation networks

Deep neural networks are vulnerable to attacks from adversarial inputs. Corresponding attack research on human pose estimation (HPE), particularly for body joint detection, has been largely unexplored. Transferring classification-based attack methods to body joint regression tasks is not straightforward. Another issue is that the attack effectiveness and imperceptibility contradict each other. To solve these issues, we propose local imperceptible attacks on HPE networks. In particular, we reformulate imperceptible attacks on body joint regression into a constrained maximum allowable attack. Furthermore, we approximate the solution using iterative gradient-based strength refinement and greedy-based pixel selection. Our method crafts effective perceptual adversarial attacks that consider both human perception and attack effectiveness. We conducted a series of imperceptible attacks against state-of-the-art HPE methods, including HigherHRNet, DEKR, and ViTPose. The experimental results demonstrate that the proposed method achieves excellent imperceptibility while maintaining attack effectiveness by significantly reducing the number of perturbed pixels. Approximately 4% of the pixels can achieve sufficient attacks on HPE.

Adaptive Speckle Filter for Multi-Temporal PolSAR Image with Multi-Dimensional Information Fusion

Polarimetric synthetic aperture radar (PolSAR) is an important sensor for earth observation. Multi-temporal PolSAR images obtained by successive observations of the region of interest contain rich polarimetric–temporal–spatial information of the land covers, which has wide applications. Speckle filtering becomes a necessary pre-processing for many subsequent applications. Currently, it is common to filter multi-temporal PolSAR data by directly using a speckle filter developed for single SAR or PolSAR data. The cross-correlation between different time series contains rich information in multi-temporal PolSAR images. How to utilize complete polarimetric–temporal–spatial information becomes a large challenge to achieve more satisfied performances of speckle reduction and details preservation simultaneously. This work dedicates to this issue and develops a novel speckle filtering approach for multi-temporal PolSAR data by multi-dimensional information fusion. The core idea is to establish an adaptive and efficient strategy of similar pixel selection based on the similarity test of multi-temporal polarimetric covariance matrices. This similar pixel selection scheme fuses the complete information of multi-temporal PolSAR data. The sensitivity of the proposed scheme is demonstrated with several typical and challenging texture patterns. Then, an adaptive speckle filter is established specifically for multi-temporal PolSAR data. Intensive comparison studies are carried out with airborne UAVSAR datasets and spaceborne ALOS/PALSAR datasets. Quantitative investigations in terms of the equivalent number of looks (ENL) and the figure of merit (FOM) indexes demonstrate and validate the superiority of the proposed method.

Monitoring Surface Subsidence Using Distributed Scatterer InSAR with an Improved Statistically Homogeneous Pixel Selection Method in Coalfield Fire Zones

Statistically homogeneous pixel (SHP) selection is an important process in the distributed scatterer interferometric synthetic aperture radar (DS-InSAR) approach. However, prevalent methods struggle to appropriately balance the efficiency and accuracy of selection. To solve this problem, the authors of this study improved the Hypothesis Test of Confidence Interval (HTCI) to propose an adaptive method to select the level of saliency and confidence interval for the HTCI, called Adp-HTCI. The proposed method can accurately select homogeneous pixels while inheriting the high efficiency of the HTCI. Once homogeneous pixels have been chosen, the eigenvalue decomposition of the covariance matrix is used to optimize their phase and perform temporal processing. We used the proposed method along with data on 31 scenes from the Sentinel-1 satellite from 2 June 2021 to 28 May 2022 to monitor the deformation of the surface of the fire zone in the Sikeshu coalfield. The selection results of homogeneous pixels indicate that the proposed Adp-HTCI algorithm can increase the number of selected homogeneous pixels while ensuring the accuracy of the selection results, thereby enhancing the estimation accuracy and reliability of subsequent parameter solving. The DS-InSAR results showed that the cumulative maximum subsidence in the study area within a year reached—138 mm and the point density used by the DS-InSAR approach was 17.28 times higher than that used by the StaMPS approach. The results of cross-analysis with the results of StaMPS verified the accuracy of the DS-InSAR-based approach.

Seeing the Forest for the Trees: Mapping Cover and Counting Trees from Aerial Images of a Mangrove Forest Using Artificial Intelligence

Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks.

Image reconstruction using superpixel clustering and tensor completion

This paper presents a pixel selection method for compact image representation based on superpixel segmentation and tensor completion. Our method divides the image into several regions that capture important textures or semantics and selects a representative pixel from each region to store. We experiment with different criteria for choosing the representative pixel and find that the centroid pixel performs the best. We also propose two smooth tensor completion algorithms that can effectively reconstruct different types of images from the selected pixels. Our experiments show that our superpixel-based method achieves better results than uniform sampling for various missing ratios.

A new sequential homogeneous pixel selection algorithm for distributed scatterer InSAR

ABSTRACT Distributed scatterer interferometric synthetic aperture radar (DS InSAR) technology has been widely used in various fields. Homogeneous pixel selection is a crucial step in the use of DS InSAR, directly affecting the estimation precision and reliability of subsequent parameter calculations. The existing algorithms for selecting homogeneous pixels have inherent limitations, such as requiring many heterogeneous samples and strict requirements surrounding the required number of synthetic aperture radar (SAR) images. To address these problems, a new sequential selection algorithm for homogeneous pixels is proposed, based on the Baumgartner – Weiss–Schindler (BWS) test algorithm and dynamic interval estimation (DIE) theory. According to Monte Carlo simulation experiments, the average standard deviation (STD) of the mean of the rejection of the BWS-DIE algorithm under six sample conditions is 0.014. Compared with three existing algorithms, including the Kolmogorov‒Smirnov (KS), BWS and fast statistically homogeneous pixel selection (FaSHPS) algorithms, the BWS-DIE algorithm improves homogeneous pixel selection precision by 64.3%, 69.4% and 25.3%, respectively. In the real data experiment, 12 scenes of Advanced Land Observing Satellite-1 Phased Array type L-band Synthetic Aperture Radar (ALOS-1 PALSAR) data from February 2007 to March 2011 were used and the BWS-DIE multitemporal InSAR (MT InSAR) method based on the BWS-DIE algorithm was applied to surface subsidence monitoring in the western mining area of Xuzhou, Jiangsu Province, China. The experimental results show that, compared with the Stanford Method for Persistent Scatterers (StaMPS), the BWS-DIE MT InSAR method improves the ability to monitor the maximum subsidence by 12.3%, increases the point density by 5.7 times and decreases the root mean square error (RMSE) by 50%. In addition, new surface deformation patterns are found in the spatial-temporal evolution. The above experimental results show that the proposed BWS-DIE algorithm exhibits remarkable advantages in selection power and selection precision and is not limited by the number of SAR images. The proposed algorithm can further broaden the application scenarios for DS InSAR and provide high-quality and reliable monitoring data for subsequent scientific research.