Nearest-neighbor Diffusion Research Articles

Hyperspectral Image Sharpening Using Fusion Techniques -A Case Study at Salah Al-Din Province/Iraq-

The data fusion process includes merging two or more pieces of information obtained from different sensors. Satellite image fusion research aims to create a new image by combining two images captured by different sensors using various methodologies. In this research, image sharpening tools were used to combine a hyperspectral image with a low spatial resolution captured by a Hyperion sensor mounted on the Earth Observation 1 (EO-1) satellite with a grayscale high spatial resolution image captured by Enhanced Thematic Mapper Plus (ETM +) sensor mounted on Landsat-8 (resampling first one to ensure equal spatial resolution of both images). In addition, three techniques were adopted for implementing the Fusion mechanism: the Principal Component Analysis PCA, the Nearest Neighbor Diffusion NNDifuse, and the Gram-Schmidt method; these were used to sharpen hyperspectral data using high spatial resolution. The result showed that the Gram-Schmidt method could give Hyperspectral images with higher spectral and spatial resolution in panchromatic image data more accurately than the other methods.

A image fusion and U-Net approach to improving crop planting structure multi-category classification in irrigated area

Some subtle features of planting structures in irrigation areas could only be visible on high-resolution panchromatic spectral images. However, low spatial resolution multispectral image makes it hard to recognize them. It is challenging to accurately obtain crop planting structure when using traditional methods. This paper proposes an extraction method of crop planting structure based on image fusion and U-Net depth semantic segmentation network, which can automatically and accurately extract multi-category crop planting structure information. This method takes Landsat8 commercial multispectral satellite data set as an example, chooses RGB pseudo-color synthetic image which highlights vegetation characteristics, and uses HLS(Hue, Luminance, Saturation), NND(Nearest-Neighbor Diffusion) and G-S(Gram-Schmidt) methods to fuse panchromatic band to obtain 15m high-resolution fusion image to obtain training set and test set, six types of land features including cities and rivers were labeled by manual to obtain the verification set. The training and validation sets are cut and enhanced to train the U-Net semantic segmentation network. Taking the Xiaokaihe irrigation area in Binzhou City, Shandong Province, China, as an example, the planting structure was classified, and the overall accuracy was 87.7%, 91.2%, and 91.3%, respectively. The accuracy of crop planting structures (wheat, cotton, woodland) was 74.2%, 82.5%, 82.3%, and the Kappa coefficient was 0.832, 0.880, and 0.881, respectively. The results showed that the NND-UNet method was suitable for large-scale continuous crop types (wheat, cotton), and the GS-UNet method had a better classification effect in discrete areas of cash crops (Jujube and many kinds of fruit trees).

Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas

Image fusion of satellite sensors can generate a high-resolution multi-spectral image from inputs of a high spatial resolution panchromatic image and a low spatial resolution multi-spectral image for feature extraction and target recognition, such as enclosure seines and floating rafts. However, there is currently no clear and definite method of image fusion for different aquaculture areas distribution extraction from high-resolution satellite images. This study uses three types of high-resolution remote sensing images, GF-1 (Gaofen-1), GF-2 (Gaofen-2), and WV-2 (WorldView-2), covering the raft and enclosure seines aquacultures in the Xiangshan Bay, China, to evaluate panchromatic and multispectral image fusion techniques to determine which is the best. This study applied PCA (principal component analysis), GS (Gram-Schmidt), and NNDiffuse (nearest neighbor diffusion) algorithms to panchromatic and multispectral images fusion of GF-1, GF-2, and WV-2. Two quantitative methods are used to evaluate the fusion effect. The first used seven statistical parameters, including gray mean value, standard deviation, information entropy, average gradient, correlation coefficient, deviation index, and spectral distortion. The second is the CQmax index. Comparing the evaluation results by these seven common statistical indicators with the results of the image fusion evaluation by index CQmax, the results prove that the CQmax index can be applied to the evaluation of image fusion effects in different aquaculture areas. For the floating raft cultured area, the conclusion is consentaneous; NNDiffuse was also optimal for GF-1 and GF-2 data, and PCA was optimal for WV-2 data. For the enclosure seines culture area, the conclusion of quantitative evaluations is not consistent and it shows that there is no definite good method that can be applied to all areas; therefore, careful evaluation and selection of the best applicable image fusion method are required according to the study area and sensor images.

A Novel Method for Estimating Spatial Distribution of Forest Above-Ground Biomass Based on Multispectral Fusion Data and Ensemble Learning Algorithm

Optical remote sensing technology has been widely used in forest resources inventory. Due to the influence of satellite orbits, sensor parameters, sensor errors, and atmospheric effects, there are great differences in vegetation spectral information captured by different satellite sensor images. Spectral fusion technology can couple the advantages of different multispectral sensor images to produce new multispectral data with high spatial and spectral resolution, it has great potential for improving the spectral sensitivity of forest vegetation and alleviating the spectral saturation. However, how to quickly and effectively select the multi-spectral fusion data suitable for forest above-ground biomass (AGB) estimation is a very critical issue. This study proposes a scheme (RF-S) to comprehensively evaluate multispectral fused images and develop the appropriate model for forest AGB estimation, on the basis of random forest (RF) and the stacking ensemble algorithm. First, four classic fusion methods are used to fuse the preprocessed GaoFen-2 (GF-2) multispectral image with Sentinel-2 image to generate 12 fused Sentinel-like images. Secondly, we apply a comprehensive evaluation method to quickly select the optimal fused image for the follow-up research. Subsequently, two feature combination optimization methods are used to select feature variables from the three feature sets. Finally, the stacking ensemble algorithm based on model dynamic integration and hyperparameter automatic optimization, as well as some classic machine learners, are used to construct the forest AGB estimation model. The results show that the fused image NND_B3 (based on nearest neighbor diffusion pan sharpening method and Band3_Red) selected by the evaluation method proposed in this study has the best performance in AGB estimation. Using the stacking ensemble method and NND_B3 image, we get the highest estimation accuracy, with the adjusted R2 and relative root mean square error (RMSEr) of 0.6306 and 15.53%, respectively. The AGB estimation RMSEr of NND_B3 is 19.95% and 24.90% lower than those of GF-2 and Sentinel-2, respectively. We also found that the multi-window texture factor has better performance in the area with low AGB, and it can suppress the overestimation significantly. The AGB spatial distribution estimated using the NND_B3 image matches the field observations well, indicating that the multispectral fusion image combined with the Stacking algorithm can increase the accuracy and saturation of the AGB estimates.

ENHANCING UAV COASTAL MAPPING USING INFRARED PANSHARPENING

Abstract. Ecosystems must now cope with climate change such as rising sea levels. These major changes have a direct impact on the coastal fringe. However, in recent years, coastal ecosystems such as saltmarshes have proven their adaptive capacity. Unmanned Aerial Vehicles (UAV) are an inexpensive and easily deployable alternative which offer us the possibility to monitor these geomorphological and ecological systems, have been perfected over the years, making it possible to achieve high or even very high (VH) spectral and spatial resolution. Detection of changes at VH temporal and spatial resolution such as coastline evolution or seasonal monitoring of plant communities is facilitated. The red-green-blue (RGB) camera is the basic equipment of low-cost UAVs. Many studies have demonstrated the interest of infrared sensors for vegetation or water detection. In this original study, a pansharpening method has been developed to generate a red-edge (RE) and near infrared channel based on the VH resolution of RGB. Out of the three different pansharpening algorithms tested, Gram-Schmidt showed correlation (0.61 and 0.63 for RE and NIR channels respectively), followed by nearest neighbor diffusion and finally, principal component spectral pansharpening. The maximum likelihood, support vector machine and convolutional neural network classifiers were used to discriminate the main objects of the study area. The classification results revealed that at the classifier scale the ML outperforms the others with an overall accuracy of 80.75%. At the spectral band scale, the RE obtains the best performances with 80.04% of OA with ML and 78.34% of OA with SVM.

Fusion of China ZY-1 02D Hyperspectral Data and Multispectral Data: Which Methods Should Be Used?

ZY-1 02D is China’s first civil hyperspectral (HS) operational satellite, developed independently and successfully launched in 2019. It can collect HS data with a spatial resolution of 30 m, 166 spectral bands, a spectral range of 400~2500 nm, and a swath width of 60 km. Its competitive advantages over other on-orbit or planned satellites are its high spectral resolution and large swath width. Unfortunately, the relatively low spatial resolution may limit its applications. As a result, fusing ZY-1 02D HS data with high-spatial-resolution multispectral (MS) data is required to improve spatial resolution while maintaining spectral fidelity. This paper conducted a comprehensive evaluation study on the fusion of ZY-1 02D HS data with ZY-1 02D MS data (10-m spatial resolution), based on visual interpretation and quantitative metrics. Datasets from Hebei, China, were used in this experiment, and the performances of six common data fusion methods, namely Gram-Schmidt (GS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Modified Intensity-Hue-Saturation (IHS), Wavelet Transform (Wavelet), and Color Normalized Sharping (Brovey), were compared. The experimental results show that: (1) HPF and GS methods are better suited for the fusion of ZY-1 02D HS Data and MS Data, (2) IHS and Brovey methods can well improve the spatial resolution of ZY-1 02D HS data but introduce spectral distortion, and (3) Wavelet and NND results have high spectral fidelity but poor spatial detail representation. The findings of this study could serve as a good reference for the practical application of ZY-1 02D HS data fusion.

Evaluation of fusion methods for GF-6 aiming to water body observation

Gaofen-6 (GF-6) has the advantages of wide coverage, multiple resolutions, and multiple bands, and can provide richer information for remote sensing interpretation. Image fusion method is the key process in high resolution remote sensing application. Guangdong-Hong Kong-Macao Greater Bay Area was selected as experiment area and four image fusion methods of HPF (High-Pass Fliter), NND (Nearest Neighbor Diffusion), GS (Gram-Schmidt) and Pansharp were employed to process panchromatic and multispectral imaging. In order to evaluate the result performances, firstly, four kinds of fusion results were evaluated by visual，and then established mean, standard deviation, entropy, average gradient, correlation coefficient and spectral distortion for quantitative evaluation of fusion results. The results demonstrated that The Pansharp algorithm and the GS algorithm have the best comprehensive evaluation of the GF-6 satellite fusion effect, taking into account the color effect of the image and the enhancement of spatial details, which can meet most fusion requirements. For land-based fusion areas, the Pansharp fusion method can be selected, and the GS fusion method can better reflect the water body information and is the most suitable fusion method for GF-6 satellite water body information enhancement among the four algorithms.

Response of topographic control on nearest-neighbor diffusion-based pan-sharpening using multispectral MODIS and AWiFS satellite dataset

Remote sensing plays a significant role in monitoring of the undulating the Himalayas. With continuous monitoring, the preservation of natural resources and mitigation of natural hazards is possible. Currently, satellite sensors are not capable enough to deliver the earth surface image at a very high temporal, spectral, and spatial resolution, simultaneously. Therefore, it is essential to perform the pan-sharpening of spatially high-resolution (HR) panchromatic (PAN) spectral band with low-resolution (LR) multispectral (MS) imagery which must be acquired on the same temporal date from multiple sensors. On the other hand, due to the rugged topography of the Himalayas, topographic effects are generally induced in the form of shadow and affect the spatial information or spectral information. Therefore, the focus of the present work is to implement and analyze the performance of topographic correction on nearest-neighbor diffusion (NND)-based pan-sharpening algorithm. In order to evaluate the effectiveness, K-mean classification (KMC) has been implemented over topographically corrected and topographically uncorrected NND pan-sharpened images. From the experimental outcomes, it is inferred that topographically corrected NND pan-sharpened classified image has achieved better accuracy (81.33%) as compared with topographically uncorrected NND pan-sharpened classified imagery (78%). It is expected that the integration of topographic correction and NND algorithm facilitates the better extraction of spectral and spatial information and leads to improvement in results for further analysis such as change detection procedure and classification. The applications of the present study are in monitoring of climate change and mapping land cover changes over rugged terrain regions.

Fusion Method Evaluation and Classification Suitability Study of Wetland Satellite Imagery

Based on HJ-1A HSI data and Landsat-8 OLI data, RS image fusion experiments were carried out using three fusion methods: principal component (PC) transform, Gram Schimdt (GS) transform and nearest neighbor diffusion (NND) algorithm. Four evaluation indexes, namely mean, standard deviation, information entropy and average gradient, were selected to evaluate the fusion results from the aspects of image brightness, clarity and information content. Wetland vegetation was classified by spectral angle mapping (SAM) to find a suitable fusion method for wetland vegetation information extraction. The results show that PC fusion image contains the largest amount of information, GS fusion image has certain advantages in brightness and clarity maintenance, and NND fusion method can retain the spectral characteristics of the image to the maximum extent; Among the three fusion methods, PC transform is the most suitable for wetland information extraction. It can retain more spectral information while improving spatial resolution, with classification accuracy of 89.24% and Kappa coefficient of 0.86.

Effect of α phase on evolution of oxygen-rich layer on titanium alloys

Abstract In order to understand the evolution of oxygen-rich layer (ORL) on titanium alloys, the near α titanium alloy TA15 and α+β type titanium alloy TC4 were thermally exposed in air at 850 °C to evaluate the effect of α phase content on formation and evolution of ORL, and the stability and diffusion of oxygen in α- and β-Ti were investigated by first principles calculations to reveal the oxygen diffusion rate. TA15 with more α phases has a higher diffusion coefficient of ORL evolution than TC4, resulting in forming thicker ORL on TA15 under the same thermal exposure condition. The first principles calculations indicate that octahedral interstice of α-Ti is the most stable site for oxygen atom. The nearest neighbor diffusion between octahedral interstices along the [0001] direction in α-Ti presenting the lowest activation energy is the most favorable oxygen diffusion mechanism in α- and β-Ti.

Assessing temporal-spatial land use simulation effects with CLUE-S and Markov-CA models in Beijing.

In order to solve the problem of extensive land use in rural residential areas and promote the construction of a new socialist countryside and farmland protection, it is important to graspe the temporal and spatial evolution of rural settlements. In this study, with Beijing as an example, the efficiencies of CLUE-S and Markov-CA models in simulating spatial temporal evolution of rural residential areas are analyzed. With 14 driving factors chosen, the land uses in Beijing in 2000 and 2005 are simulated and predicted using the two models. Grid size of 220m × 220m is used. The accuracies and Kappa coefficients of the simulation and prediction results are analyzed and the following conclusions are made. The core part of CLUE-S model is logistic regression, which grants it obvious advantage in capturing the trends of land use changes with more discrete distributions. Meanwhile, Markov-CA model takes neighborhood into consideration, making it relatively more advantageous in simulating and predicting changes of land uses with the character of nearest-neighbor diffusion. However, among various land uses, only urban land use has this character, the correct prediction rates of the CLUE-S model in 2000 and 2005 were 60.99% and 81.35%, respectively, while the accuracy of the Markov-CA model prediction was 51.33% and 73.68%, and it is the main reason that CLUE-S model returns better simulation and prediction results of rural residential areas for both 2000 and 2005 than Markov-CA model.

Integrating spatial and spectral information for enhancing spatial features in the Gough map of Great Britain

Abstract The Gough map has been recognized as the earliest surviving map of Great Britain. The map, dated to late 14th or early 15th century, depicts many and sophisticated cartographical local details, which was unusual in European medieval maps. Moreover, the rewriting and re-inking of some names or areas could indicate the map was re-touched after its original production. The Gough map, today with restricted access and in a conservation environment, was displayed for some time at the Bodleian Library in Oxford, where it was frequently in contact with scholars. Despite this familiarization, the Gough map has not been widely researched and inquiries about the original purpose of the map or the context in which it was made remain unknown. Recent interest in the map has lead to the use of new technologies to image the map and produce sophisticated data that allows scholars and scientists to examine it taking advantage of the new information. In this paper, a technique for Pan-sharpening hyperspectral images (HSI) is introduced to the cultural heritage community. The Pan-sharpening process is applied specifically to HSI images of the Gough map of Great Britain with the purpose of using relatively low spatial resolution hyperspectral analysis techniques on its very fine features. The Pan-sharpening technique is based on Nearest-neighbor diffusion (NNDiffuse) and the spatial enhancing is aimed at faded features such as handwriting and some distinctive details that are only visible in high-resolution conservation photographs. It is shown here that the use of the NNDiffuse Pan-sharpening improves spatial features in HSI of historical artifacts without impacting the spectral fidelity, and that the findings in the analysis of these features could contribute to the understanding of the Gough map and its importance in the historical context of Great Britain.

Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features

Land Use and Land Cover (LULC) classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigated for Sentinel-2. This paper bridged this gap by comparing the differences between upscaling and downscaling as well as different downscaling algorithms from the point of view of LULC classification accuracy. The studied downscaling algorithms include nearest neighbor resampling and five popular pansharpening methods, namely, Gram-Schmidt (GS), nearest neighbor diffusion (NNDiffusion), PANSHARP algorithm proposed by Y. Zhang, wavelet transformation fusion (WTF) and high-pass filter fusion (HPF). Two spatial features, textural metrics derived from Grey-Level-Co-occurrence Matrix (GLCM) and extended attribute profiles (EAPs), are investigated to make up for the shortcoming of pixel-based spectral classification. Random forest (RF) is adopted as the classifier. The experiment was conducted in Xitiaoxi watershed, China. The results demonstrated that downscaling obviously outperforms upscaling in terms of classification accuracy. For downscaling, image sharpening has no obvious advantages than spatial interpolation. Different image sharpening algorithms have distinct effects. Two multiresolution analysis (MRA)-based methods, i.e., WTF and HFP, achieve the best performance. GS achieved a similar accuracy with NNDiffusion and PANSHARP. Compared to image sharpening, the introduction of spatial features, both GLCM and EAPs can greatly improve the classification accuracy for Sentinel-2 imagery. Their effects on overall accuracy are similar but differ significantly to specific classes. In general, using the spectral bands downscaled by nearest neighbor interpolation can meet the requirements of regional LULC applications, and the GLCM and EAPs spatial features can be used to obtain more precise classification maps.

Fault Detection Based on Diffusion Maps and <i>k</i> Nearest Neighbor Diffusion Distance of Feature Space

Dimensionality reduction is a fundamental task of high-dimensional data analysis in order to reduce redundant information of the collected data. In this paper, we apply diffusion maps framework to address this problem, and then propose a novel fault detection technique based on the k nearest neighbor diffusion distance method of feature space. First, normal high-dimensional data sets are mapped into a low-dimensional feature space by analyzing the insightful relationship between data points, and feature space can represent major information of raw data. Subsequently, like the traditional kNN method, the sum of k nearest neighbor diffusion distance is computed and the kernel density estimation method is used to set a threshold of a normal process. Comparing these two methods, modeling using k nearest neighbor diffusion distance method of feature space can economize storage space and increase the speed of fault detection. In addition, this method can solve nonlinear equation of industrial process data, and the non-Gaussian characteristics of modeling data can be solved by using kernel density estimation method. Finally, the effectiveness of diffusion maps algorithm in the aspect of data classification is verified by the numerical examples and the superiority of the proposed method is illustrated by the monitoring of TE process.

The most general autonomous two-lane diffusion

Diffusion of single-species particles with exclusion on a two-lane lattice is considered. The most general autonomous nearest neighbor diffusion on this two-lane lattice is introduced. The dynamics of the particle density is investigated, and the large time behavior of these one-point functions is obtained. Some special cases are investigated in more detail.

Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images

Commercial multispectral satellite datasets, such as WorldView-2 and Geoeye-1 images, are often delivered with a high-spatial resolution panchromatic image (PAN) as well as a corresponding lower resolution multispectral image (MSI). Certain fine features are only visible on the PAN but are difficult to discern on the MSI. To fully utilize the high-spatial resolution of the PAN and the rich spectral information from the MSI, a pan-sharpening process can be carried out. However, difficulties arise in maintaining radiometric accuracy, particularly for applications other than visual assessment. We propose a fast pan-sharpening process based on nearest-neighbor diffusion with the aim to enhance the salient spatial features while preserving spectral fidelity. Our approach assumes that each pixel spectrum in the pan-sharpened image is a weighted linear mixture of the spectra of its immediate neighboring superpixels; it treats each spectrum as its smallest element of operation, which is different from the most existing algorithms that process each band separately. Our approach is shown to be capable of preserving salient spatial and spectral features. We expect this algorithm to facilitate fine feature extraction from satellite images.

First-principles study of hydrogen storage and diffusion in B2 FeTi alloy

The structural, elastic properties and electronic structure of FeTi and its hydrides, and hydrogen diffusion behavior in FeTi alloy were investigated by the first-principles calculations within the frame work of density functional theory (DFT). The calculated lattice constants for FeTi were in good agreement with the experimental values, and the lattice distortion of hydrides increases with the rise of hydrogen content. The calculation results of formation enthalpy, fusion heat of hydrogen and octahedron interstice size showed that hydrogen atom is more likely to exist in the central site of octahedron with the frame of four Ti atoms and two Fe atoms. The independent elastic constants as well as polycrystalline elastic parameters (bulk modulus B, shear modulus G, Young’s modulus E, Poisson’s ratio ν and anisotropy value A) were calculated, and then the ductility and elastic anisotropy of FeTi and its hydrides were discussed in details. In addition, the density of states, electron density difference and population analysis for FeTi and its hydrides were also calculated, which reveals the underlying mechanism of structural stability and chemical bonding. Finally, the activation energy of next nearest neighbor diffusion for H between octahedron interstices in FeTi alloy was calculated to be 2.92eV, clarifying the atomic mechanism of hydrogen diffusion.

Symmetry-mediatedH2Odiffusion on Al{100}

Based upon the full-electron ab initio calculations, we found orientational preferences and correlation for the atop-to-atop motions of an ${\mathrm{H}}_{2}\mathrm{O}$ on Al{100}, which originates from the symmetry match between an ${\mathrm{H}}_{2}\mathrm{O}$ and the surface square lattice. We have provided theoretical evidence that the favored mechanisms can be classical hopping coupled with either the molecular flipping or in-plane reorientations, with reduced energy barriers compared to those of the simple translational motions; while quantum tunneling assisted motion is not favored for an ${\mathrm{H}}_{2}\mathrm{O}$ via the nearest neighbor diffusion.

Effects of lateral diffusion on morphology and dynamics of a microscopic lattice-gas model of pulsed electrodeposition

The influence of nearest-neighbor diffusion on the decay of a metastable low-coverage phase (monolayer adsorption) in a square lattice-gas model of electrochemical metal deposition is investigated by kinetic Monte Carlo simulations. The phase-transformation dynamics are compared to the well-established Kolmogorov-Johnson-Mehl-Avrami theory. The phase transformation is accelerated by diffusion, but remains in accord with the theory for continuous nucleation up to moderate diffusion rates. At very high diffusion rates the phase-transformation kinetic shows a crossover to instantaneous nucleation. Then, the probability of medium-sized clusters is reduced in favor of large clusters. Upon reversal of the supersaturation, the adsorbate desorbs, but large clusters still tend to grow during the initial stages of desorption. Calculation of the free energy of subcritical clusters by enumeration of lattice animals yields a quasiequilibrium distribution which is in reasonable agreement with the simulation results. This is an improvement relative to classical droplet theory, which fails to describe the distributions, since the macroscopic surface tension is a bad approximation for small clusters.

Self-diffusion of Al and Pb atoms in Al–Pb immiscible alloy system

Al–Pb binary alloy system exhibits a positive heat mixing that precludes intermixing by conventional techniques, it is possible to achieve solid state alloying between these two immiscible elements in nanostructures by mechanical alloying (MA). The studies of detailed atomistic diffusion process of dynamic movement for Al and Pb were carried out by computer simulation, in conjunction with modified analytic embedded atom method (MAEAM). Computational results have clarified that the nearest-neighbor diffusion path is the dominant jump vector for both Al and Pb atom diffusion. The migration energies along this path have been estimated to be 0.35083 and 0.10538 eV, respectively. The self-diffusion activation energies for Al and Pb along NN jump are 0.99557 and 0.85919 eV, respectively. The calculated results reveal that Pb atom diffuses much faster than Al atom does, which concluded that Pb atom is the diffusive species in Al–Pb immiscible alloy system. The calculated self-diffusion activation energies for nano-sized Al and Pb grains are 0.83192 and 0.70455 eV, respectively. They are lower than that for normal sized Al and Pb grains, which facilitates the atomic diffusion and alloying during MA process in Al–Pb immiscible alloy system.