2D Wavelet Research Articles

Damage imaging in composite curved panels based on 2D wavelet analysis of guided wavefields

The existing guided wavefield damage imaging theory is mainly Fourier transform-based wavenumber domain analysis, which essentially processes signals in a global sense and easily loses local information of structural damage. The current research on wavefield damage imaging methods focuses on flat plate-type structures, with few studies involving composite curved panels. By utilizing the ability of two-dimensional continuous wavelet transform (2D CWT) to amplify the singular components of the signal, it is introduced into spatial domain wavefield analysis to generate 2D CWT wavefields in composite curved panels. Furthermore, a damage imaging algorithm is constructed based on 2D CWT wavefield energy fusion. The results show that the algorithm can accurately image damage in composite curved panels. The novelty of this paper lies in conducting a spatial domain analysis of the guided wavefield for the purpose of damage imaging in composite curved panels.

Integrating spatial and channel attention mechanisms with domain knowledge in convolutional neural networks for friction coefficient prediction

AbstractThe pavement skid resistance is crucial for ensuring driving safety. However, the reproducibility and comparability of field measurements are constrained by various influencing factors. One solution to these constraints is utilizing laser‐based 3D pavement data, which are notably stable and can be employed to estimate pavement skid resistance indirectly. However, the integration of tire–road friction mechanisms and deep neural networks has not been fully studied. This study employed spatial‐channel attention mechanisms to integrate frictional domain knowledge and convolutional neural networks (CNNs) that predict the friction coefficient as the output. The models’ inputs include 3D texture data, corresponding finite element (FE) simulation outcomes, and 2D wavelet decomposition outcomes. An additional spatial attention (ASA) mechanism guided the CNNs to focus on the tire–road contact region, using tire–road contact stress from FE simulation as domain knowledge. Multi‐scale channel attention (MSCA) mechanisms enabled the CNNs to learn the channel weights of 2D‐wavelet‐based multi‐scale inputs, thereby assessing the contribution of different texture scales to tire–road friction. A multi‐attention and feature fusion mechanism was designed, and the performances of various combinations were compared. The results showed that the fusion of ASA and MSCA achieved the best performance, with a regression R2 of 0.8470, which was a 20.25% improvement over the baseline model.

Damage identification in composite structures using high-speed 3D digital image correlation and wavelet analysis of mode shapes

ABSTRACT In this study, the damage identification procedure combining the advantages of high-speed 3D digital image correlation (DIC) that is used for modal analysis and wavelet-based processing algorithm is presented. The proposed procedure has been successfully validated on sandwich composite structures with internal damage of a core and impact damage, and its performance was demonstrated by detection, localisation, and identification of shapes of damage in tested structures, comparable to the results from scanning laser Doppler vibrometry (SLDV). The effectiveness in damage identification was achieved by the development of processing algorithm of acquired mode shapes, which uses a 2D double-density directional wavelet transform with increased sensitivity with entropic weights allowing to automatically select those wavelet coefficients and mode shapes, which are most sensitive to damage. The procedure presented in this paper demonstrates the performance of 3D DIC for damage identification as a cost-effective alternative to the procedures based on SLDV measurements.

Coherent fading suppression of distributed acoustic sensing based on time–frequency domain linkage algorithm

Abstract Due to the inherent characteristic of coherent fading phenomenon, the performance of distributed acoustic sensing system will be severely degraded, producing anomalous information and positioning errors. In order to suppress the influence of this phenomenon, the data array of demodulated results is obtained after processing the collected distributed Rayleigh scattering signals along the sensing fiber, which can be utilized to construct a two-dimension (2D) image with the time–distance domain information. Thus, this allows us to remove the abnormal information that are introduced by fading noise. Based on this principle, a morphology compositional analysis (MCA) scheme, an effective time–frequency domain linkage algorithm, is applied to the constructed 2D images to enhance the sensing accuracy. Compared with traditional methods including the 2D mean value filter (MVF) and 2D wavelet transform (WT), the proposed method makes full use of the independence of image source property and abnormity feature so that the abnormal information caused by the coherent fading can be eliminated without deteriorating the source information along the sensing fiber. Experimental results show that the anomaly rate of the time–distance domain information along the sensing fiber for the proposed method, the MVF method and the WT method can be reduced by 92.43%, 65.05% and 70.18% respectively. In addition, the isolation degree of the MCA, MVF and WT methods are 41, 16.02 and 23.35 times higher than that treated by traditional method, respectively. Also, the stability of the time-domain vibration can be improved and the spatial resolution would not be influenced by the proposed method compared with the traditional method. This scheme will promote the development of distributed acoustic sensor system with high-accuracy and high-quality without any hardware modification.

Brain tumor segmentation by combining MultiEncoder UNet with wavelet fusion.

Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learning methods cope with situations of multimodal MRI by an early fusion strategy that implicitly assumes that the modal relationships are linear, which tends to ignore the complementary information between modalities, negatively impacting the model's performance. Meanwhile, long-range relationships between voxels cannot be captured due to the localized character of the convolution procedure. Aiming at this problem, we propose a multimodal segmentation network based on a late fusion strategy that employs multiple encoders and a decoder for the segmentation of brain tumors. Each encoder is specialized for processing distinct modalities. Notably, our framework includes a feature fusion module based on a 3D discrete wavelet transform aimed at extracting complementary features among the encoders. Additionally, a 3D global context-aware module was introduced to capture the long-range dependencies of tumor voxels at a high level of features. The decoder combines fused and global features to enhance the network's segmentation performance. Our proposed model is experimented on the publicly available BraTS2018 and BraTS2021 datasets. The experimental results show competitiveness with state-of-the-art methods. The results demonstrate that our approach applies a novel concept for multimodal fusion within deep neural networks and delivers more accurate and promising brain tumor segmentation, with the potential to assist physicians in diagnosis.

Intelligent road extraction from high resolution remote sensing images based on optimized SVM

Road recognition and extraction based on high-resolution remote sensing images is currently a hot issue at the forefront of image processing and other disciplines, and its results not only help to enrich geographic information, but also have important application value in national defence construction and other aspects. For the intelligent extraction of roads in high-resolution remote sensing images, the study firstly preprocesses these images, and then extracts the roads by using variational function and support vector machine. As the extraction results are not satisfactory enough, the 3D wavelet transform technique is used to extract the spectral features of roads in the frequency domain, combined with the texture in the time domain, and optimized the support vector machine in order to extract the road images more accurately. The outcomes denote that in contrast with other methods, the optimized method can have optimal correctness and quality while guaranteeing higher integrity. The satellite road image of Chengdu A industrial park is rich in extracted information, having the highest completeness of 94.23%, correctness of 60.73%, and quality of 59.42%, and the satellite road image of Chengdu B industrial park is similarly rich in extracted information, having the highest completeness of 83.47%, correctness of 85.31%, and quality of 72.85%. The highest value of CPU usage for road extraction was 2000 kb at 37 s, and the average value of CPU usage for the whole 60 s was 500 kb. It shows that the research method has good results in high resolution remote sensing image road extraction.

A new approach based on 2D wavelet transform in the analysis of wettability behavior and evaporation rate for the surface of PTFE dielectric material used in high voltage applications

In this study, a new approach based on 2D wavelet transform is proposed for the analysis of wettability behavior and evaporation rate. Droplet images were taken from the PTFE dielectric sample surface at 30th, 270th, 510th, 750th, 990th, 1230th, 1470th, and 1710th seconds. The droplet images were decomposed into sub-bands using functions from six different wavelet families. The energy, entropy, mean, standard deviation, skewness, and kurtosis measures were calculated using wavelet coefficients for each sub-band image. Linear, exponential, and polynomial fitting methods were used to estimate the contact angle of the droplet images. The energy and standard deviation values, which are the selected optimal measures, were used as input in the fitting methods, and the regression coefficient (R2) and Root Mean Square Error (RMSE) values of the fitting methods were calculated. Thus, a high-performing contact angle prediction model with 2nd level sub-band image wavelet coefficients has been obtained.

Non-contact surface roughness evaluation of milled Al and Cu specimens by 1D and 2D wavelet transformation using histogram based linear regression model

Non-contact surface roughness evaluation of milled Al and Cu specimens by 1D and 2D wavelet transformation using histogram based linear regression model

PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation

Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The growing research on ACPs as therapeutic agent is increasing due to its minimal side effects. However, identifying novel ACPs using wet-lab experiments are generally time-consuming, labor-intensive, and expensive. Leveraging computational methods for fast and accurate prediction of ACPs would harness the drug discovery process. Herein, a machine learning-based predictor, called PLMACPred, is developed for identifying ACPs from peptide sequence only. PLMACPred adopted a set of encoding schemes representing evolutionary-property, composition-property, and protein language model (PLM), i.e., evolutionary scale modeling (ESM-2)- and ProtT5-based embedding to encode peptides. Then, two-dimensional (2D) wavelet denoising (WD) was employed to remove the noise from extracted features. Finally, ensemble-based cascade deep forest (CDF) model was developed to identify ACP. PLMACPred model attained superior performance on all three benchmark datasets, namely, ACPmain, ACPAlter, and ACP740 over tenfold cross validation and independent dataset. PLMACPred outperformed the existing models and improved the prediction accuracy by 18.53%, 2.4%, 7.59% on ACPmain, ACPalter, ACP740 dataset, respectively. We showed that embedding from ProtT5 and ESM-2 was capable of capturing better contextual information from the entire sequence than the other encoding schemes for ACP prediction. For the explainability of proposed model, SHAP (SHapley Additive exPlanations) method was used to analyze the feature effect on the ACP prediction. A list of novel sequence motifs was proposed from the ACP sequence using MEME suites. We believe, PLMACPred will support in accelerating the discovery of novel ACPs as well as other activities of microbial peptides.

How informative are summaries of the cosmic 21 cm signal?

The cosmic 21 cm signal will bring data-driven advances to studies of Cosmic Dawn (CD) and the Epoch of Reionization (EoR). Radio telescopes such as the Square Kilometre Array (SKA) will eventually map the HI fluctuations over the first billion years -- the majority of our observable Universe. With such large data volumes, it becomes increasingly important to develop optimal summary statistics, which will allow us to learn as much as possible about the CD and EoR. In this work we compare the astrophysical parameter constraining power of several 21 cm summary statistics, using the determinant of the Fisher information matrix, $ F $. Since we do not have an established fiducial model for the astrophysics of the first galaxies, we computed for each summary the distribution of $ F $ across the prior volume. Using a large database of cosmic 21 cm light cones that include realizations of telescope noise, we compared the following summaries: (i) the spherically averaged power spectrum (1DPS), (ii) the cylindrically averaged power spectrum (2DPS), (iii) the 2D wavelet scattering transform (WST), (iv) a recurrent neural network (RNN) trained as a regressor; (v) an information-maximizing neural network (IMNN); and (vi) the combination of 2DPS and IMNN. Our best performing individual summary is the 2DPS, which provides relatively high Fisher information throughout the parameter space. Although capable of achieving the highest Fisher information for some parameter choices, the IMNN does not generalize well, resulting in a broad distribution across the prior volume. Our best results are achieved with the concatenation of the 2DPS and IMNN. The combination of only these two complimentary summaries reduces the recovered parameter variances on average by factors of sim 6.5 -- 9.5, compared with using each summary independently. Finally, we point out that that the common assumption of a constant covariance matrix when doing Fisher forecasts using 21 cm summaries can significantly underestimate parameter constraints.

Forecast Cosmological Constraints with the 1D Wavelet Scattering Transform and the Lyman-α Forest.

We make forecasts for the constraining power of the 1D wavelet scattering transform when used with a Lyman-α forest cosmology survey. Using mock simulations and a Fisher matrix, we show that there is considerable cosmological information in the scattering transform coefficients not captured by the flux power spectrum. We estimate mock covariance matrices assuming uncorrelated Gaussian pixel noise for each quasar at a level drawn from a simple log-normal model. The extra information comes from a smaller estimated covariance in the first-order wavelet power and from second-order wavelet coefficients that probe non-Gaussian information in the forest. Forecast constraints on cosmological parameters from the wavelet scattering transform are more than an order of magnitude tighter than for the power spectrum, shrinking a 4D parameter space by a factor of 10^{6}. Should these improvements be realized with the Dark Energy Spectroscopic Instrument, inflationary running would be constrained to test common inflationary models predicting α_{s}=-6×10^{-4} and neutrino mass constraints would be improved enough for a 5-σ detection of the minimal neutrino mass.

Abstract PO3-07-01: Multiscale Computational Analysis of Patient-Matched Multi-Modal Imaging of Breast Tissue Microenvironment

Abstract Introduction: Mammographic breast density is a strong quantitative cancer risk factor, but its link to underlying tissue and cellular level changes are weakly understood. Understanding the complex restructuring changes in the breast tumor microenvironment and its impact on breast density is integral to improving risk assessment and treatment strategies. A novel approach was developed to stratify mammographic dense breast tissue into areas of active dense tissue and passive dense tissue. Active dense tissue is structurally reorganizing and links to cancer dynamics, whereas passive dense mammographic tissue remains organized.1,2 A complementary wavelet-based analysis technique to measure the multiscale anisotropy of collagen fibers from Second-Harmonic Generation (SHG) imaging demonstrated clinical cancer diagnostic potential in mouse carcinoma and human pancreatic cancer.3,4 This tool is now leveraged for a multi-modal study in which patient-matched H&E tissue biopsies are imaged using both bright-field microscopy and SHG imaging to compare with the patient’s corresponding mammograms. Stratifying the mammographic density into active and passive dense areas, a correlation between biopsy tissue anisotropy and mammographic density subtypes can be calculated to help understand how tumor microenvironment changes affect a patient’s mammographic breast tissue composition. Methods: SHG and brightfield-imaged biopsy slides from 10 patients (five malignant, five benign) collected from Maine Medical Center (Maine, US) were analyzed using the 2D Wavelet Transform Modulus Maxima (WTMM) Anisotropy Method generating a large-scale and a small-scale anisotropy factor as well as a scale-combined anisotropy calculated from their large- and small-scale anisotropy separation. A multi-modal anisotropy score was calculated by adding the imaged area’s H&E combined anisotropy score with its respective SHG combined anisotropy score. This analysis was done on eight randomly selected ductal areas for each imaging modality on a slide and correlated with the measured areas of density subtypes from the whole mammographic view. Linear regression was used for determining correlation between H&E and SHG anisotropy factors as well as with active and passive mammographic density subtype measurements. Results: The malignant patients’ small-scale H&E anisotropy factor positively correlated with their small scale SHG anisotropy factor (R2=0.571). No correlation was found for the benign patients (R2=0.011). The strongest correlations were found between benign patients’ mammographic passive (positive correlation) and active (negative correlation) dense tissues vs the H&E anisotropy scale-combined factor (R2=0.858 and R2=0.961, respectively). The largest malignant patient correlation was a negative relationship between SHG anisotropy scale-combined factor and overall mammographic density (inclusive of both active and passive dense tissue) (R2=0.891). The multi-modal anisotropy score correlated with overall mammographic density for both benign (positive correlation, R2=0.698) and malignant patients (negative correlation, R2=0.773). Combining the multi-modal anisotropy factor from SHG and H&E with the active dense tissue measurement using K-Means clustering, 5/5 malignant and 4/5 benign patients were correctly classified. Conclusion: Density in mammograms correlates with underlying quantitative changes in a patient’s tissue and collagen anisotropy. Combining both H&E and SHG analysis on tissue biopsy with active and passive density metrics from computational mammography leads to the best predictive potential than any one method on their own.

LAMA-WAVELET: IMAGE IMPAINTING WITH HIGH QUALITY OF FINE DETAILS AND OBJECT EDGES

Context. The problem of the image impainting in computer graphic and computer vision systems is considered. The subject of the research is deep learning convolutional neural networks for image inpainting.
 Objective. The objective of the research is to improve the image inpainting performance in computer vision and computer graphics systems by applying wavelet transform in the LaMa-Fourier network architecture.
 Method. The basic LaMa-Fourier network decomposes the image into global and local texture. Then it is proposed to improve the network block, processing the global context of the image, namely, the spectral transform block. To improve the block of spectral transform, instead of Fourier Unit Structure the Simple Wavelet Convolution Block elaborated by the authors is used. In this block, 3D wavelet transform of the image on two levels was initially performed using the Daubechies wavelet db4. The obtained coefficients of 3D wavelet transform are splitted so that each subband represents a separate feature of the image. Convolutional layer, batch normalization and ReLU activation function are sequentially applied to the results of splitting of coefficients on each level of wavelet transform. The obtained subbands of wavelet coefficients are concatenated and the inverse wavelet transform is applied to them, the result of which is the output of the block. Note that the wavelet coefficients at different levels were processed separately. This reduces the computational complexity of calculating the network outputs while preserving the influence of the context of each level on image inpainting. The obtained neural network is named LaMa-Wavelet. The FID, PSNR, SSIM indexes and visual analysis were used to estimate the quality of images inpainted with LaMa-Wavelet network.
 Results. The proposed LaMa-Wavelet network has been implemented in software and researched for solving the problem of image inpainting. The PSNR of images inpainted using the LaMa-Wavelet exceeds the results obtained using the LaMa-Fourier network for narrow and medium masks in average by 4.5%, for large masks in average by 6%. The LaMa-Wavelet applying can enhance SSIM by 2–4% depending on a mask size. But it takes 3 times longer to inpaint one image with LaMa-Wavelet than with LaMa-Fourier network. Analysis of specific images demonstrates that both networks show similar results of inpainting of a homogeneous background. On complex backgrounds with repeating elements the LaMa-Wavelet is often more effective in restoring textures.
 Conclusions. The obtained LaMa-Wavelet network allows to improve the image inpainting with large masks due to applying wavelet transform in the LaMa network architecture. Namely, the quality of reconstruction of image edges and fine details is increased.

Radial and azimuthal gradients of the moving groups in Gaia DR3: The slow and fast bar degeneracy problem

The structure and dynamics of the central bar of the Milky Way (MW) are still under debate whilst being fundamental ingredients for the evolution of our Galaxy. The recent Gaia DR3 offers an unprecedented detailed view of the 6D phase space of the MW, allowing for a better understanding of the complex imprints of the bar on the phase space. We aim to identify and characterise the dynamical moving groups across the MW disc, and use their large-scale distribution to help constrain the properties of the Galactic bar. We used 1D wavelet transforms of the azimuthal velocity ($V_ distribution in bins of radial velocity to robustly detect the kinematic substructure in the Gaia DR3 catalogue. We then connected these structures across the disc to measure the azimuthal (phi ) and radial ($R$) gradients of $V_ of the moving groups. We simulated thousands of perturbed distribution functions using backward integration, sweeping a large portion of parameter space of feasible Galaxy models that include a bar, in order to compare them with the data and to explore and quantify the degeneracies. The radial gradient of the Hercules moving group ($ V_ R$ = 28.1pm 2.8 $) cannot be reproduced by our simple models of the Galaxy that show much larger slopes both for a fast and a slow bar. This suggests the need for more complex dynamics (e.g. a different bar potential, spiral arms, a slowing bar, a complex circular velocity curve, external perturbations, etc.). We measured an azimuthal gradient for Hercules of $ V_ = -0.63pm $deg$^ $ and find that it is compatible with both the slow and fast bar models. Our analysis points out that in using this type of analysis, at least two moving groups are needed to start breaking the degeneracies. We conclude that it is not sufficient for a model to replicate the local velocity distribution; it must also capture its larger-scale variations. The accurate quantification of the gradients, especially in the azimuthal direction, will be key for the understanding of the dynamics governing the disc.

Genetic Algorithm Framework for 3D Discrete Wavelet Transform based Hyperspectral Image Classification

Genetic Algorithm Framework for 3D Discrete Wavelet Transform based Hyperspectral Image Classification

Changes in regional daily precipitation intensity and spatial structure from global reanalyses

AbstractWe conducted an analysis of hydrological cycle variations across 13 regions of varying sizes distributed across different continents. The analysis is based on five reanalysis datasets of daily precipitation, all produced by the European Centre on Medium‐Range Weather Forecasts (ECMWF): ERA5 high‐resolution, ERA5 ensemble, CERA‐20C, ERA20‐C and ERA20‐CM. We examined several climate indicators, including the daily mean precipitation, the 75th and 99th percentiles, the precipitation area fraction and the area fractions with precipitations exceeding 10 and 20 mm. We evaluated the ability of the reanalyses to capture precipitation at specific spatial scales using scale‐separation diagnostics based on 2D wavelet decomposition. The climatological energy spectra of precipitation derived from the analysis describe the scales that each reanalysis can accurately reproduce, serving as a unique signature for each dataset. We compared the spatial scales that were comparable across the different reanalyses and examined the temporal trends of energy on those scales. The results indicate that the hydrological cycle is undergoing changes in all regions, with some variations observed across different regions. Common features include an increase in intense precipitation events and a decrease in the corresponding spatial extent. The ensemble of ERA5 reanalyses exhibited the smallest effective resolution, as determined by the scale‐separation method, and displayed more pronounced trends compared to other reanalyses. Notably, an acceleration of changes is evident in the last 20 years. However, Central Asia may be an exception, showing relatively less noticeable changes in the hydrological cycle.

Pseudo Light Field Image and 4D Wavelet-Transform-Based Reduced-Reference Light Field Image Quality Assessment

Reduced-reference light field image (LFI) quality assessment (RR LFIQA) automatically assesses image quality with only partial information about the reference LFI is available. Existing RR LFIQA has difficulty extracting effective RR information and perceptual features to represent the LFI quality. In this paper, we propose an RR LFIQA model based on pseudo LFI (PLFI) and four-dimensional (4D) wavelet transform. To extract RR information related to LFI perceptual quality, a PLFI is created as the RR information of the LFI using a view synthesis algorithm. Considering that the high-dimensional characteristics of the PLFI, 4D wavelet transform is used to decompose the original and distorted PLFIs. The 4D wavelet transform essentially performs a continuous 1D wavelet transform for the 4D signal to enable the local 4D structure of the PLFIs to be characterized effectively in the 4D wavelet domain. A novel spatial-angular weighting strategy is proposed to describe the importance of each location for quality evaluation, to further improve the performance of the proposed method. Experimental results on four benchmark datasets show that the proposed model performs better than the representative 2DIQA and LFIQA models. Our code will be publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JianjunXiang/RRLFIQA_4DDWT</uri> .

Contactless Biometric Based on Palm Vein Recognition Using Wavelet and Local Line Binary Patterns

To support the roadmap for coexistence with Covid-19, contactless biometrics is needed as an individual identity verification technology in daily activities such as control systems, recording attendance at offices/schools/agencies and access rights to a room. An example of contactless biometrics is palm vein-based biometrics. Because it is contactless, this biometric system does not require direct contact between the user and the sensor device, providing several advantages in terms of comfort during acquisition and is more hygienic. In the palm vein recognition system, the palm vein pattern can be considered as a texture feature. Therefore, this study proposes a contactless biometric system based on palm vein recognition using the Local Line Binary Pattern method to extract texture features of palm vein images resulting from the decomposition of the 2D Wavelet Transformation, so as to produce a small texture descriptor that is compatible with the texture characteristics of thin veins. The proposed texture feature extraction method has been tested using the fuzzy k-NN classification method on 600 palm images with a CRR accuracy of 95.0% with a computation time of 0.057 seconds.

Multi-sensor observations for monitoring groundwater depletion and land subsidence

Study regionThe Kabudarahang Plain and the Razan-Qahavand Plain. Study focusImproper use of water resources has reduced groundwater levels and created land subsidence (LS) in many plains of Iran. The aim and innovation of this research are to study multi-sensor observations for LS and groundwater depletion and explore the relationships of the involved variables with high confidence. The gravity recovery and climate experiment (GRACE) observations can be used to evaluate water storage changes at the Earth’s surface. GRACE has stripe errors, leakage and various noises that multilevel 3D wavelet decomposition (M3WD) has been suggested to mitigate noises and downscale for small scale. This study has investigated the interferometric synthetic-aperture radar (InSAR) of Sentinel-1 images from October 2014 to September 2019, the GRACE data from March 2002 to July 2016, and groundwater hydrograph (GH) from 2014 to 2020. New hydrological insight for the regionThe maximum LS rate, obtained from small baseline subset-differential of InSAR is 20 mm/year at the Kabudarahang Plain (KP) and 30 mm/year at Razan-Qahavand Plain (RQP). The groundwater storage variations (ΔGW) have a decreasing trend of 78.45 ± 0.2 million cubic meters/year. The GH for the KP and RQP shows a downward trend of 3.25 and 1.81 m/year, respectively. Based on the outcomes, the M3WD can increase the correlation of ΔGW with other sensors by 15 %. Also, validation between sensors with normalized cross-correlation has remarkable compatibility. The multi-sensor study of ΔGW and LS revealed various dimensions with high reliability and can facilitate the water resource management.

Landslides monitoring with SBAS-InSAR and GNSS

Satellite radar images have many applications in the determination of landslide displacement. In this study, three landslides of Tabriz have been studied with 150 Sentinel-1 images and a small baseline subset (SBAS) method from Q4 2014 to 2020. 3D wavelet decomposition (3WD) has been used to noise mitigation and the correct reconstruction of phase. The average landslide displacement (ALD) of Pasdaran Highway, Negin Park and Shahid Valipour Street has increased after the precipitation in mid-2017. In 2018, landslides displacement has been determined through global navigation satellite system (GNSS) observations. GNSS results are fully consistent with ALD and have a correlation of 0.95. The ALD correlation of the study areas with precipitation is more than 0.8 and the displacement modeled with 3WD increased the correlation by 20 %.