Multi-level spatial details cross-extraction and injection network for hyperspectral pansharpening.

Hyperspectral (HS) pansharpening, which fuses the HS image with a high spatial resolution panchromatic (PAN) image, provides a good solution to overcome the limitation of HS imaging devices. However, most existing convolutional neural network (CNN)-based methods are hard to understand and lack interpretability due to the black-box design. In this Letter, we propose a multi-level spatial details cross-extraction and injection network (MSCIN) for HS pansharpening, which introduces the mature multi-resolution analysis (MRA) technology to the neural network. Following the general idea of MRA, the proposed MSCIN divides the pansharpening process into details extraction and details injection, in which the missing details and the injection gains are estimated by two specifically designed interpretable sub-networks. Experimental results on two widely used datasets demonstrate the superiority of the proposed method.

Local model-based hyperspectral pansharpening algorithm via optimization constraint equation and sliding window.

Injection model-based algorithms have been proved to be effective techniques to solve the pansharpening problem. However, the existing injection model-based algorithms often face an imbalance between over-sharpening and image blurring in the fused image. This paper proposes a local model-based pansharpening method to solve this problem from two aspects. First, an optimization constraint equation formed by the quality index is proposed to reduce the difference between the details of hyperspectral (HS) images and panchromatic (PAN) images. Second, the sliding-window-based fusion scheme is proposed for the first time, to the best of our knowledge, to adaptively fuse the details of HS and PAN images to reduce redundancy. Simulation experiments show that the proposed algorithm has excellent fusion performance from the aspects of subjectivity and objectivity.

Graph Embedding Interclass Relation-Aware Adaptive Network for Cross-Scene Classification of Multisource Remote Sensing Data.

The unsupervised domain adaptation (UDA) based cross-scene remote sensing image classification has recently become an appealing research topic, since it is a valid solution to unsupervised scene classification by exploiting well-labeled data from another scene. Despite its good performance in reducing domain shifts, UDA in multisource data scenarios is hindered by several critical challenges. The first one is the heterogeneity inherent in multisource data complicates domain alignment. The second challenge is the incomplete representation of feature distribution caused by the neglect of the contribution from global information. The third challenge is the inaccuracies in alignment due to errors in establishing target domain conditional distributions. Since UDA does not guarantee the complete consistency of the distribution of the two domains, networks using simple classifiers are still affected by domain shifts, resulting in poor performance. In this paper, we propose a graph embedding interclass relation-aware adaptive network (GeIraA-Net) for unsupervised classification of multi-source remote sensing data, which facilitates knowledge transfer at the class level for two domains by leveraging aligned features to perceive inter-class relation. More specifically, a graph-based progressive hierarchical feature extraction network is constructed, capable of capturing both local and global features of multisource data, thereby consolidating comprehensive domain information within a unified feature space. To deal with the imprecise alignment of data distribution, a joint de-scrambling alignment strategy is designed to utilize the features obtained by a three-step pseudo-label generation module for more delicate domain calibration. Moreover, an adaptive inter-class topology based classifier is constructed to further improve the classification accuracy by making the classifier domain adaptive at the category level. The experimental results show that GeIraA-Net has significant advantages over the current state-of-the-art cross-scene classification methods.

Cycle-Refined Multidecision Joint Alignment Network for Unsupervised Domain Adaptive Hyperspectral Change Detection.

Hyperspectral change detection, which provides abundant information on land cover changes in the Earth's surface, has become one of the most crucial tasks in remote sensing. Recently, deep-learning-based change detection methods have shown remarkable performance, but the acquirement of labeled data is extremely expensive and time-consuming. It is intuitive to learn changes from the scene with sufficient labeled data and adapting them into an unlabeled new scene. However, the nonnegligible domain shift between different scenes leads to inevitable performance degradation. In this article, a cycle-refined multidecision joint alignment network (CMJAN) is proposed for unsupervised domain adaptive hyperspectral change detection, which realizes progressive alignment of the data distributions between the source and target domains with cycle-refined high-confidence labeled samples. There are two key characteristics: 1) progressively mitigate the distribution discrepancy to learn domain-invariant difference feature representation and 2) update the high-confidence training samples of the target domain in a cycle manner. The benefit is that the domain shift between the source and target domains is progressively alleviated to promote change detection performance on the target domain in an unsupervised manner. Experimental results on different datasets demonstrate that the proposed method can achieve better performance than the state-of-the-art change detection methods.

Deep Spatial-Spectral Joint-Sparse Prior Encoding Network for Hyperspectral Target Detection.

Hyperspectral target detection aims to locate targets of interest in the scene, and deep learning-based detection methods have achieved the best results. However, black box network architectures are usually designed to directly learn the mapping between the original image and the discriminative features in a single data-driven manner, a choice that lacks sufficient interpretability. On the contrary, this article proposes a novel deep spatial-spectral joint-sparse prior encoding network (JSPEN), which reasonably embeds the domain knowledge of hyperspectral target detection into the neural network, and has explicit interpretability. In JSPEN, the sparse encoded prior information with spatial-spectral constraints is learned end-to-end from hyperspectral images (HSIs). Specifically, an adaptive joint spatial-spectral sparse model (AS 2 JSM) is developed to mine the spatial-spectral correlation of HSIs and improves the accuracy of data representation. An optimization algorithm is designed for iteratively solving AS 2 JSM, and JSPEN is proposed to simulate the iterative optimization process in the algorithm. Each basic module of JSPEN one-to-one corresponds to the operation in the optimization algorithm so that each intermediate result in the network has a clear explanation, which is convenient for intuitive analysis of the operation of the network. With end-to-end training, JSPEN can automatically capture the general sparse properties of HSIs and faithfully characterize the features of background and target. Experimental results verify the effectiveness and accuracy of the proposed method. Code is available at https://github.com/Jiahuiqu/JSPEN.

A Principle Design of Registration-Fusion Consistency: Toward Interpretable Deep Unregistered Hyperspectral Image Fusion.

For hyperspectral image (HSI) and multispectral image (MSI) fusion, it is often overlooked that multisource images acquired under different imaging conditions are difficult to be perfectly registered. Although some works attempt to fuse unregistered images, two thorny challenges remain. One is that registration and fusion are usually modeled as two independent tasks, and there is no yet a unified physical model to tightly couple them. Another is that deep learning (DL)-based methods may lack sufficient interpretability and generalization. In response to the above challenges, we propose an unregistered HSI fusion framework energized by a unified model of registration and fusion. First, a novel registration-fusion consistency physical perception model (RFCM) is designed, which uniformly models the image registration and fusion problem to greatly reduce the sensitivity of fusion performance to registration accuracy. Then, an HSI fusion framework (MoE-PNP) is proposed to learn the knowledge reasoning process for solving RFCM. Each basic module of MoE-PNP one-to-one corresponds to the operation in the optimization algorithm of RFCM, which can ensure clear interpretability of the network. Moreover, MoE-PNP captures the general fusion principle for different unregistered images and therefore has good generalization. Extensive experiments demonstrate that MoE-PNP achieves state-of-the-art performance for unregistered HSI and MSI fusion. The code is available at https://github.com/Jiahuiqu/MoE-PNP.

Immature platelet fraction levels predict the development of prolonged thrombocytopenia after haematopoietic stem cell transplantation.

Prolonged thrombocytopenia (PT) is a serious complication after haematopoietic stem cell transplantation (HSCT). PT has been suggested to be associated with an increased platelet transfusion requirement and poor outcomes after transplantation. Due to the complex mechanism of PT development, it is difficult to diagnose in the early post-transplant period. Our study aimed to identify an early predictive marker for PT after HSCT. Previous studies showed that the clinical utility of immature platelet fraction (IPF) predicts platelet recovery after chemotherapy and successful engraftment. However, the relationship between IPF and PT after HSCT remains unclear. Fifty-two patients with malignant haematological diseases who underwent HSCT were included in the study. We observed the kinetics of recovery of haematological parameters after transplantation and performed receiver operating characteristics (ROC) curve analysis using data from the 52 HSCT patients. The days to rise and peak of IPF, absolute IPF count (A-IPF) and highly fluorescent IPF (H-IPF) were almost synchronised in all patients, at day 10 and day 15, respectively. The begin to rise levels of IPF, H-IPF and A-IPF were all significantly lower in the PT group than in the good engraftment (GE) group (p=0.0016, p=0.0094, p=0.0086, respectively). The peak levels of IPF were significantly lower in the PT group than the GE group (p=0.0036). However, the peaks of H-IPF and A-IPF were not statistically significant between the two groups (p=0.3383, p=0.0887, respectively). The area under the ROC curve (AUC) of IPF rise was 0.739 (95% CI 0.583-0.896; p<0.05) and the cut-off value was 3.5%, while the AUC of IPF peak was 0.800 (95% CI 0.637-0.962; p<0.01) and the cut-off value was 8.0%. In conclusion, early low levels of IPF predict the development of PT after HSCT. These findings may help improve the management and treatment strategies for PT after HSCT.

Microplastics distribution and microbial community characteristics of farmland soil under different mulch methods.

The widespread use of plastic film in agricultural production has resulted in the accumulation of large amounts of residual plastic film in the soil, and most of the plastic residuals eventually break up into microplastics (MPs). However, the effects of different film mulching methods on the soil ecosystems are largely unexplored. Therefore, we investigated the MPs distribution and the physicochemical properties and microbial community structure in the farmland soil tillage layer covered with different mulching method of film. The results indicate that the film mulching method had no significant effect on the soil pH and organic matter content, however, the respiration intensity of the soil covered with mulching film (MF) (60.11-84.99 µg/g) and shed film (SF) (56.10-65.68 µg/g) was significantly higher than that covered with shed film & mulching film (SMF) (17.25-39.16 µg/g). The MPs abundance in the soil covered with MF (1367 particles/kg soil) was significantly higher than that covered with SF (800 particles/kg soil) and slightly higher than that with SMF (1000 particles/kg soil). The small-sized (0-0.5 mm) MPs abundance was increased with the tillage layer depth (0-20 cm), while the large-sized (1-5 mm) MPs abundance was the opposite. In addition, in the soil covered with agricultural film, the dominant phylum and genera of the bacteria were Proteobacteria (relative abundance was 64.06 %) and Pseudomonas (13.16 %), respectively. In the soil without agricultural film application as a control treatment, the diversity of the soil bacterial community was higher than that in the soil covered with agricultural film, and the relative abundances of the top 10 genera were all less than 5 %. Overall, this study provides essential information for understanding the effects of different film mulching methods on the agricultural systems. Overall, this study provides essential information for understanding the effects of different film mulching methods on the distribution of MPs and the biogeochemical properties of farmland soils.

An Interpretable Unsupervised Unrolling Network for Hyperspectral Pansharpening.

Existing deep convolutional neural networks (CNNs) have recently achieved great success in pansharpening. However, most deep CNN-based pansharpening models are based on "black-box" architecture and require supervision, making these methods rely heavily on the ground-truth data and lose their interpretability for specific problems during network training. This study proposes a novel interpretable unsupervised end-to-end pansharpening network, called as IU2PNet, which explicitly encodes the well-studied pansharpening observation model into an unsupervised unrolling iterative adversarial network. Specifically, we first design a pansharpening model, whose iterative process can be computed by the half-quadratic splitting algorithm. Then, the iterative steps are unfolded into a deep interpretable iterative generative dual adversarial network (iGDANet). Generator in iGDANet is interwoven by multiple deep feature pyramid denoising modules and deep interpretable convolutional reconstruction modules. In each iteration, the generator establishes an adversarial game with the spatial and spectral discriminators to update both spectral and spatial information without ground-truth images. Extensive experiments show that, compared with the state-of-the-art methods, our proposed IU2PNet exhibits very competitive performance in terms of quantitative evaluation metrics and qualitative visual effects.

Feature Pyramid Fusion Network for Hyperspectral Pansharpening.

Hyperspectral (HS) pansharpening aims at fusing an observed HS image with a panchromatic (PAN) image, to produce an image with the high spectral resolution of the former and the high spatial resolution of the latter. Most of the existing convolutional neural networks (CNNs)-based pansharpening methods reconstruct the desired high-resolution image from the encoded low-resolution (LR) representation. However, the encoded LR representation captures semantic information of the image and is inadequate in reconstructing fine details. How to effectively extract high-resolution and LR representations for high-resolution image reconstruction is the main objective of this article. In this article, we propose a feature pyramid fusion network (FPFNet) for pansharpening, which permits the network to extract multiresolution representations from PAN and HS images in two branches. The PAN branch starts from the high-resolution stream that maintains the spatial resolution of the PAN image and gradually adds LR streams in parallel. The structure of the HS branch remains highly consistent with that of the PAN branch, but starts with the LR stream and gradually adds high-resolution streams. The representations with corresponding resolutions of PAN and HS branches are fused and gradually upsampled in a coarse to fine manner to reconstruct the high-resolution HS image. Experimental results on three datasets demonstrate the significant superiority of the proposed FPFNet over the state-of-the-art methods in terms of both qualitative and quantitative comparisons.

Noise Prior Knowledge Informed Bayesian Inference Network for Hyperspectral Super-Resolution.

Well-known deep learning (DL) is widely used in fusion based hyperspectral image super-resolution (HS-SR). However, DL-based HS-SR models have been designed mostly using off-the-shelf components from current deep learning toolkits, which lead to two inherent challenges: i) they have largely ignored the prior information contained in the observed images, which may cause the output of the network to deviate from the general prior configuration; ii) they are not specifically designed for HS-SR, making it hard to intuitively understand its implementation mechanism and therefore uninterpretable. In this paper, we propose a noise prior knowledge informed Bayesian inference network for HS-SR. Instead of designing a "black-box" deep model, our proposed network, termed as BayeSR, reasonably embeds the Bayesian inference with the Gaussian noise prior assumption to the deep neural network. In particular, we first construct a Bayesian inference model with the Gaussian noise prior assumption that can be solved iteratively by the proximal gradient algorithm, and then convert each operator involved in the iterative algorithm into a specific form of network connection to construct an unfolding network. In the process of network unfolding, based on the characteristics of the noise matrix, we ingeniously convert the diagonal noise matrix operation which represents the noise variance of each band into the channel attention. As a result, the proposed BayeSR explicitly encodes the prior knowledge possessed by the observed images and considers the intrinsic generation mechanism of HS-SR through the whole network flow. Qualitative and quantitative experimental results demonstrate the superiority of the proposed BayeSR against some state-of-the-art methods.

Multifunctional composite films based on polyvinyl alcohol, quaternary ammonium salt modified cellulose nanofibers and tannic acid-iron ion coordination complexes for food packaging.

The development of sustainable and well-performing food packaging materials takes on critical significance, whereas it is still challenging. To overcome the shortcomings of polyvinyl alcohol (PVA) as a degradable packaging material, in this work, hydrophobic quaternary ammonium salt (QAS) modified cellulose nanofibers (CNF) and tannic acid­iron ion coordination complexes (TA-Fe) were adopted for the preparation of functional PVA films. The modified CNF (CNF-QAS) not only improved the mechanical properties and water resistance of PVA, but also endowed it with antibacterial ability. In addition, the synergistic antibacterial capability with CNF-QAS was achieved using TA-Fe with photothermal therapy. As a result, the modulus, elongation at break, tensile strength, and water contact angle of the prepared PVA films were examined as 88 MPa, 200 %, 11.7 MPa, and 94.8°, respectively. Furthermore, with the assistance of CNF-QAS and TA-Fe, the films inhibited the growth of E. coli and S. aureus by 99.8 % and 99.7 %, respectively, and they exhibited high cell viability of 90.5 % for L929 fibroblasts. Based on the above encouraging properties, the functional PVA films could significantly extend the shelf life of oranges for over two weeks, proving the excellent application prospects in the food packaging field.

Mother Phubbing and Adolescent Cyberbullying: The Mediating Role of Perceived Mother Acceptance and the Moderating Role of Emotional Stability.

Previous studies have found some risk factors of cyberbullying. However, little is known about how mother phubbing may influence adolescent cyberbullying, and the mediating and moderating mechanisms underlying this relationship. "Phubbing," which is a portmanteau of "phone" and "subbing," refers to snubbing other people and focus on smartphones in social interactions. This study examined whether mother phubbing, which refers to being phubbed by one's mother, would be positively related to adolescent cyberbullying, whether perceived mother acceptance would mediate the relationship between mother phubbing and adolescent cyberbullying, and whether emotional stability would moderate the pathways between mother phubbing and adolescent cyberbullying. The sample consisted of 4,213 Chinese senior high school students (mean age 16.41 years, SD = 0.77, 53% were female). Participants completed measurements regarding mother phubbing, cyberbullying, perceived mother acceptance, and emotional stability. The results indicated that mother phubbing was positively related to cyberbullying, which was mediated by perceived mother acceptance. Further, moderated mediation analyses showed that emotional stability moderated the direct path between mother phubbing and cyberbullying and the indirect path between mother phubbing and perceived mother acceptance. This study highlighted the harmful impact of mother phubbing on adolescents by showing a positive association between mother phubbing and adolescent cyberbullying, as well as the underlying mechanisms between mother phubbing and adolescent cyberbullying.

A Spatio-Spectral Fusion Method for Hyperspectral Images Using Residual Hyper-Dense Network.

Spatio-spectral fusion of panchromatic (PAN) and hyperspectral (HS) images is of great importance in improving spatial resolution of images acquired by many commercial HS sensors. DenseNets have recently achieved great success for image super-resolution because they facilitate gradient flow by concatenating all the feature outputs in a feedforward manner. In this article, we propose a residual hyper-dense network (RHDN) that extends the DenseNet to solve the spatio-spectral fusion problem. The overall structure of the proposed RHDN method is a two-branch network, which allows the network to capture the features of HS images within and outside the visible range separately. At each branch of the network, a two-stream strategy of feature extraction is designed to process PAN and HS images individually. A convolutional neural network (CNN) with cascade residual hyper-dense blocks (RHDBs), which allows direct connections between the pairs of layers within the same stream and those across different streams, is proposed to learn more complex combinations between the HS and PAN images. The residual learning is adopted to make the network efficient. Extensive benchmark evaluations well demonstrate that the proposed RHDN fusion method yields significant improvements over many widely accepted state-of-the-art approaches.

Growth differentiation factor 10 induces angiogenesis to promote wound healing in rats with diabetic foot ulcers by activating TGF-ß1/Smad3 signaling pathway.

Background: Diabetic foot ulcer (DFU) represents a highly-prevalent complication of diabetes mellitus (DM). Herein, the current study sought to identify the role of growth differentiation factor 10 (GDF-10) in wound healing in DFU via regulation of the transforming growth factor-beta 1 (TGF-ß1)/Smad3 pathway. Methods: DM- and DFU-related microarray datasets GSE29221 and GSE134431 were firstly retrieved, and weighted gene co-expression network analysis (WGCNA) was carried out to construct a co-expression network affecting wound healing in DFU, followed by differential analysis. A protein-protein interaction (PPI) network of the DFU-related genes was subsequently constructed, and the core genes and signaling pathways in DFU were screened with the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional analyses. A DFU rat model was constructed for mechanism verification of the effect of GDF-10 on wound healing in DFU. Results: WGCNA screened five co-expression modules, and the brown module was most closely-related to DM. Clustering analysis screened 4417 candidate genes, of which 175 differential genes were associated with wound healing, further involved in TGF-ß1/Smad3 signaling pathway regulation of wound healing in DFU. The PPI network analysis predicted that GDF-10 might regulate the TGF-ß1/Smad3 signaling pathway to participate in DFU development. Results of animal experimentation showed that the wound healing rates of NFU, DFU, DFU + GDF and GDF + SIS3 groups on the 22nd day were (87.66 ± 6.80)%, (56.31 ± 7.29)%, (71.64 ± 9.43)% and (55.09 ± 7.13)%, respectively. Besides, the expression of TGF-ß1 in NFU, DFU, DFU + GDF and GDF + SIS3 groups was 0.988 ± 0.086, 0.297 ± 0.036, 0.447 ± 0.044, and 0.240 ± 0.050, respectively, and that of Smad3 was 1.009 ± 0.137, 0.145 ± 0.017, 0.368 ± 0.048, and 0.200 ± 0.028, respectively. Specifically, GDF-10 exerted a significant diminishing effect on fasting blood glucose level, and promoted wound healing in DFU rats, in addition to up-regulation of VEGF, FGF, Ang-1, TGF-ß1, Smad3 and enhancement of IL-1b, IL-6, TNF-a and MMP-9, thereby promoting fibroblast proliferation, collagen deposition and angiogenesis. Conclusions: Our findings highlight that GDF-10 may promote angiogenesis by activating TGF-ß1/Smad3 signaling, thereby promoting wound healing in DFU rats.

Regulation of the Proliferation of Diabetic Vascular Endothelial Cells by Degrading Endothelial Cell Functional Genes with QKI-7.

Background: Diabetes has emerged as one of the most serious and common chronic diseases of our times, causing life-threatening, disabling and costly complications, and reducing life expectancy. Studies have shown that cardiovascular morbidity is 1-3 times higher in diabetic patients than in normal people. There are many clinical and experimental data that prove that most of the complications of diabetes are related to atherosclerosis, which suggests that chronic hyperglycemia may induce an imbalance in the proliferation of vascular endothelial cells. Purpose: This study aims to explore the relationship between QKI-7 and vascular endothelial cell dysfunction and lay a foundation for further clarifying the molecular mechanism of endothelial cell damage in the process of diabetes with atherosclerosis. Methods: We chose blood samples and pluripotent stem cells and vascular endothelial cells of hospitalized patients with diabetes and diabetes atherosclerosis as research subjects. The expression levels of endothelial cell proliferation and genes related to endothelial cell proliferation were analyzed by RT-qPCR and Western blot, to study the influence of QKi-7 on the physiological state of endothelial cells. Through gene knockdown experiment, the effects of QKi-7 knockdown on functional genes and physiological functions of endothelial cells were analyzed. Finally, RNA immunoprecipitation was used to test the mutual effect among QKI-7 and the transcription level of functional genes, and the mRNA attenuation experiment proved that QKI-7 participated in the degradation process of functional genes. Results: The findings of the RT-qPCR and Western blot tests revealed that QKI-7 was highly expressed in blood samples of diabetic patients and atherosclerosis as well as in endothelial cells induced by human pluripotent stem cells and human vascular endothelial cells after high-glucose treatment. Overexpression and high glucose of QKI-7 resulted in inhibiting expressed function genes CD144, NLGN1, and TSG6 and upregulation of inflammatory factors TNF-α, IL-1ß, and IFN-γ, leading to excessive proliferation of endothelial cells. After QKI-7 gene knockdown, the expression levels of CD144, NLGN1, and TSG6, inflammatory factors TNF-α, IL-1ß, and IFN-γ, and the cell proliferation rate all returned to normal levels. RNA immunoprecipitation showed that QKi-7 interacted with CD144, NLGN1, and TSG6 mRNAs and was involved in the transcriptional degradation of functional genes through their interactions. Conclusion: This research initially revealed the relevant molecular mechanism of QKI-7 leading to the excessive proliferation of endothelial cells in diabetic and atherosclerotic patients. In view of the role of QKI-7 in diabetic vascular complications, we provided a potential target for clinical diabetes treatment strategies in the future.

Generative Dual-Adversarial Network With Spectral Fidelity and Spatial Enhancement for Hyperspectral Pansharpening.

Hyperspectral (HS) pansharpening is of great importance in improving the spatial resolution of HS images for remote sensing tasks. HS image comprises abundant spectral contents, whereas panchromatic (PAN) image provides spatial information. HS pansharpening constitutes the possibility for providing the pansharpened image with both high spatial and spectral resolution. This article develops a specific pansharpening framework based on a generative dual-adversarial network (called PS-GDANet). Specifically, the pansharpening problem is formulated as a dual task that can be solved by a generative adversarial network (GAN) with two discriminators. The spatial discriminator forces the intensity component of the pansharpened image to be as consistent as possible with the PAN image, and the spectral discriminator helps to preserve spectral information of the original HS image. Instead of designing a deep network, PS-GDANet extends GANs to two discriminators and provides a high-resolution pansharpened image in a fraction of iterations. The experimental results demonstrate that PS-GDANet outperforms several widely accepted state-of-the-art pansharpening methods in terms of qualitative and quantitative assessment.

Evolution of Nanoporous Surface Layers on Gas-Atomized Ti60Cu39Au1 Powders during Dealloying.

Nanoporous golf ball-shaped powders with a surface porous layer consisting of fcc Cu and Cu3Au phases have been fabricated by selectively dissolving gas-atomized Ti60Cu39Au1 powders in 0.13 M HF solution. The distribution profiles of the Ti2Cu and TiCu intermetallic phases and powder size play an important role of the propagation of the selective corrosion frontiers. The final nanoporous structure has a bimodal characteristic with a finer nanoporous structure at the ridges, and rougher structure at the shallow pits. The powders with a size of 18⁻75 m dealloy faster due to their high crystallinity and larger powder size, and these with a powder size of smaller than 18 m tend to deepen uniformly. The formation of the Cu3Au intermetallic phases and the finer nanoporous structure at the ridges proves that minor Au addition inhibits the fast diffusion of Cu adatoms and decreases surface diffusion by more than two orders. The evolution of the surface nanoporous structure with negative tree-like structures is considered to be controlled by a percolation dissolution mechanism.