Shuyuan Yang

Xi'an Electronic Science and Technology University

In view of the good correlation measurement ability of correntropy, we propose a temporal local correntropy representation (TLCE) method based on the local correntropy matrix for fault diagnosis of machines. In TLCE, a sample is divided into several segments, and then the correlation between these segments is expressed by correntropy. Finally, the...

To analyze the clinical characteristics of laryngomalacia in Chinese children and explore the surgical efficacy and factors influencing severe laryngomalacia. Children (0–18 years) diagnosed with laryngomalacia in our hospital from January 2016 to January 2022 were enrolled in this study. Clinical data of patients, including general conditions, cli...

Recently, the excellent performance of transformer has attracted the attention of the visual community. Visual transformer models usually reshape images into sequence format and encode them sequentially. However, it is difficult to explicitly represent the relative relationship in distance and direction of visual data with typical 2-D spatial struc...

Internet of Things (IoT) networks are often subject to many malicious attacks in untrusted environments, and Automatic Modulation Classification (AMC) is an effective way to combat IoT physical-layer threats. However, most existing AMC methods assume sufficient labeled signals and invariant signal distribution, which is often impossible in untruste...

Domain generalization (DG) is one of the critical issues for deep learning in unknown domains. How to effectively represent domain-invariant context (DIC) is a difficult problem that DG needs to solve. Transformers have shown the potential to learn generalized features, since the powerful ability to learn global context. In this article, a novel me...

With the evolutionary development of modern communications technology, automatic modulation classification (AMC) has played an increasing role in the complex wireless communication environment. Existing AMC schemes based on deep learning use a neural network to extract features and calculate feature maps, then feed them into fully connected layers...

Radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum, signal waveforms, and so on. When the electromagn...

Objective The aim of this study was to describe a novel surgical technique of endoscopic percutaneous repair in pediatric patients with type 1, type 2 and type 3 laryngeal cleft (LC). Methods A retrospective study involving 12 patients with LC was performed at a tertiary pediatric hospital between February 2021 and June 2022. Endoscopic percutaneo...

The multispectral (MS) and the panchromatic (PAN) images belong to different modalities with specific advantageous properties. Therefore, there is a large representation gap between them. Moreover, the features extracted independently by the two branches belong to different feature spaces, which is not conducive to the subsequent collaborative clas...

Purpose To evaluate the feasibility and clarify the appropriate indications for extubation immediately after single-stage laryngotracheal reconstruction (SS-LTR) in pediatric subglottic stenosis (SGS). Methods A retrospective study was performed from July 2017 to July 2022. All patients underwent SS-LTR with anterior costal cartilage graft. Inform...

For complex data, high dimension and high noise are challenging problems, and deep matrix factorization shows great potential in data dimensionality reduction. In this article, a novel robust and effective deep matrix factorization framework is proposed. This method constructs a dual-angle feature for single-modal gene data to improve the effective...

Convolutional neural networks (CNNs) have superior feature learning capabilities with large numbers of labeled samples. The reality is that labeling these samples is costly in terms of human labor. Existing data augmentation methods alleviate the scarcity of labeled samples. However, these methods are not suitable for synthetic aperture radar (SAR)...

Multimodal Image fusion is becoming urgent in multi-sensor information utilization. However, existing end-to-end image fusion frameworks ignore a priori knowledge integration and long-distance dependencies across domains, which brings challenges to the network convergence and global image perception in complex scenes. In this paper, a conditional g...

Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Fir...

Deep convolutional neural networks (CNNs) are significant in remote sensing. Due to the strong local representation learning ability, CNNs have excellent performance in remote sensing scene classification. However, CNNs focus on location-sensitive representations in the spatial domain and lack contextual information mining capabilities. Meanwhile,...

Supervised remote sensing (RS) image segmentation has achieved remarkable success with large amounts of manually labeled data, which may be difficult to acquire in some practical application scenarios. Semisupervised RS image segmentation can efficiently utilize the knowledge embedded in unlabeled data to improve recognition performance, which is o...

Deep Learning-based (DL) methods have dominated the task of semantic segmentation of remote sensing images. However, the sizes of different objects vary widely, and there is a great deal of label-noise due to the inevitable shadows. Therefore, there is an urgent need for a method that can precisely handle complex ground data. In this paper, we prop...

Specific Emitter Identification (SEI) is crucial for attacking and defending Internet of Things (IoT) devices in untrusted scenarios or battlefield environments. However, existing SEI methods usually require annotation information, which is often unavailable in non-cooperative communications and untrusted scenarios. In this paper, we propose a sign...

Exploring semantic structure information contained in the hyperspectral image (HSI) is significant for accurate HSI classification (HSIC), especially when there are very few labeled samples. Besides, it is usually time-consuming to process HSI for many algorithms. In this paper, for a more accurate and efficient HSIC, a Semantic CorrEntropy Represe...

Transformer has shown excellent performance in remote sensing field with long-range modeling capabilities. Remote sensing video (RSV) moving object detection and tracking play indispensable roles in military activities as well as urban monitoring. However, transformers in these fields are still at the exploratory stage. In this survey, we comprehen...

Recently, Hyperspectral Image Classification (HIC) with noisy labels is attracting increasing interest. However, existing methods usually neglect to explore feature-dependent knowledge to reduce label noise, and thus perform poorly when the noise ratio is high or the clean samples are limited. In this paper, a novel Triple Contrastive Representatio...

Recently, multi-modal remote sensing image (MRSI) classification has attracted increasing attention of researchers. However, classification of MRSI with limited labeled instances is still a challenging task. In this paper, a novel self-supervised cross-modal contrastive learning method is proposed for MRSI classification. Joint intra- and cross-mod...

For remote sensing image segmentation, the boundaries of objects are difficult to distinguish, which is ignored by most methods. Therefore, it is challenging how to excavate and recover the boundaries of objects accurately. In this article, we propose a boundary-aware multi-scale network (BMNet) to solve this problem. The key components of BMNet in...

Automatic Modulation Classification (AMC) is a critical task in industrial cognitive communication systems. Existing state-of-the-art methods, typified by real-valued convolutional neural networks, have introduced innovative solutions for AMC. However, such models viewed the two constituent components of complex-valued modulated signals as discrete...

Real-world data typically follows a long-tailed distribution. When a small sample of tail classes does not cover the underlying distribution well, methods such as class re-balancing strategies and decoupled training are difficult to work, and additional knowledge needs to be introduced to recover the underlying distribution of the tail classes. In...

Inspired by the sparse and hierarchical features representation in the ventral stream of the human visual system, the biologically inspired multi-scale contourlet attention network (BMCAnet) is proposed to extract robust discriminative features. First, we constructed the multi-scale contourlet filter banks as a population of neurons in the primary...

The foundation model (FM) has garnered significant attention for its remarkable transfer performance in downstream tasks. Typically, it undergoes task-agnosticpre-training on a large dataset and can be efficiently adapted to various downstream applications through fine-tuning. While FMs have been extensively explored in language and other domains,...

With the maturity of big data and computing power, deep learning has provided an end-to-end efficient solution for fault diagnosis of rotating machinery. However, the diagnosis performance is commonly affected by complex working environment and limited labeled samples. While considering these undesirable effects and borrowing from multisource fusio...

A growing number of earth observation satellites are able to simultaneously gather multimodal images of the same area due to the expanding availability and resolution of satellite remote sensing data. This paper proposes a novel multimodal balanced self-learning interaction network (MBSI-Net) for the classification task. It involves a dual-branch t...

In recent years, the joint classification approach of hyperspectral image (HSI) and light detection and ranging (LiDAR) data based on deep learning (DL) has received increasing attention. However, existing methods either lack interaction between heterogeneous features during feature extraction or treat them equally during the interaction, inevitabl...

In the complex electromagnetic environment, radar signal deinterleaving (RSD) is a challenging task. In this paper, a deep contrastive clustering algorithm (DCCA) is advanced in a new self-supervised paradigm, for accurate RSD without any prior information of radar emitters. First, a contrastive self-supervised deep attention network (CSDAN) is con...

Most popular visual trackers for natural scenarios always adopt handcraft features or deep features to track the target in a video. However, they face with difficulties in discriminative feature representation and usually suffer from severe model drift for satellite videos, especially when encountering challenges of dim and small targets, low contr...

For accurate segmentation, effective feature extraction has always been a challenging problem, since the variability of appearance and the fuzziness of object boundaries. Convolutional neural networks have recently gained recognition in feature representation learning. However, it is only conducted in the spatial domain, and lacks effective represe...

Cross-domain classification of hyperspectral data is a critical challenge in remote sensing, especially when labels are unavailable in the target domain. Deep learning-based domain adaptation (DA) methods have been widely used in recent years. However, curren methods primarily focus on the global domain structure of the source and target domains wh...

Contrastive self-supervised learning (CSSL) is a promising method in extracting effective features from unlabeled data. It performs well in image-level tasks, such as image classification and retrieval. However, the existing CSSL methods are not suitable for pixel-level tasks, e.g., change detection (CD), since they ignore the correlation between l...

Deep representation learning has improved automatic remote change detection (RSCD) in recent years. Existing methods emphasize primarily convolutional neural networks (CNNs) or Transformer-based networks. However, most of them neither effectively combine CNNs and Transformer nor use prior geometric information to refine regions. In this paper, a no...

Connections between visual components are ubiquitous. Graphs, as a highly flexible data structure, not only allow imposing relational induction bias on data, but can provide a completely distinct learning perspective for regular image data. In this paper, we propose a hierarchical dynamic graph clustering network (HDGCN) for visual feature learning...

The feature representation learning process greatly determines the performance of networks in classification tasks. By combining multiscale geometric tools and networks, better representation and learning can be achieved. However, relatively fixed geometric features and multiscale structures are always used. In this article, we propose a more flexi...

Contrastive self-supervised learning (CSSL) has achieved promising results in extracting visual features from unlabeled data. Most of the current CSSL methods are used to learn global image features with low-resolution that are not suitable or efficient for pixel-level tasks. In this paper, we propose a coarse-to-fine CSSL framework based on a nove...

Aircraft identification has been a research hotspot in remote-sensing fields. However, due to the presence of clouds in satellite-borne optical imagery, it is difficult to identify aircraft using a single optical image. In this paper, a Multi-path Interactive Network (MIN) is proposed to fuse Optical and Synthetic Aperture Radar (SAR) images for ai...

Due to the scarcity and high cost of labeled hyperspectral image (HSI) samples, many deep learning methods driven by massive data cannot achieve the intended expectations. Semi-supervised and self-supervised algorithms have advantages in coping with this phenomenon. This paper primarily concentrates on applying self-supervised strategies to make st...

Most current deep learning (DL) based AMC methods often require a large number of labeled samples to drive itself optimization study, so as to achieve a more superior performance. However, for many AMC tasks with a small amount of data, the AMC method based on deep learning still has many challenges, which makes the deep neural network model traine...

This article proposes a practical end-to-end neural network framework to detect tiny moving vehicles in satellite videos with low imaging quality. Some instability factors, such as illumination changes, motion blurs, and low contrast to the cluttered background, make it difficult to distinguish true objects from noise and other point-shaped distrac...

Remote sensing object tracking (RSOT) is a novel and challenging problem due to the negative effects of weak features and background noise. In this article, from the perspective of attention-focus deep learning, we propose a joint Siamese attention-aware network (JSANet) for efficient remote sensing tracking which contains both the self-attention a...

Graph structure is a powerful mathematical abstraction, which can not only represent information about individuals, but also capture the interactions between individuals for reasoning. Geometric modeling and relational inference based on graph data is a long-standing topic of interest in the computer vision community. In this paper, we provide a sy...

In this letter, a new Quadruplet Convolution Neural Network (QCNN) is proposed to cooperatively and contrastively learn deep features of raw radio signals for accurate Automatic Modulation Classification (AMC), when very few labeled samples are available. Four convolutional channels with shared weights are constructed to deal with the In-phase (I)...

Global average pooling (GAP) plays an important role in traditional channel attention. However, there is the disadvantage of insufficient information to use the result of GAP as the channel scalar. At the same time, the existing spatial attention models focus on the areas of interest using average pooling or convolutional networks, but there is a l...

Autofocus plays a key role in synthetic aperture radar (SAR) imaging, especially for high-resolution imaging. In the literature, the minimum-entropy-based algorithms (MEA) have been proved to be robust and have been widely applied in SAR. However, this kind of method needs hundreds of iterations and is computationally expensive. In this paper, we p...

To maximize the complementary advantages of synergistic multimodal, a transfer representation learning fusion network (TRLF-Net) is proposed for multisource remote sensing images collaborative classification in this article. First, with respect to the feature encoding, we design a dual-branch attention sparse transfer module (DAST-Module), which co...

Semantic segmentation based on deep learning has achieved impressive results in recent years, but these results are supported by a large amount of labeled data, which requires intensive annotation at the pixel level, particularly for high-resolution remote sensing (RS) images. In this work, we propose a simple yet efficient semisupervised learning...

Convolutional neural networks (CNNs) can extract shift-invariant features and have been widely applied in the change detection task. However, common CNN lacks noise robustness and needs supervised data to alleviate these problems; in this article, we propose a novel deep shearlet convolutional neural network (ShearNet) for change detection in synth...

Recently, deep learning-based compressive synthetic aperture radar (SAR) imaging has received increasing interests. However, its performances rely heavily on the training data and could not well adapt to new observations. To solve it, in this article a robust compressive SAR imaging method is proposed under the paradigm of meta-learning. First, a D...

Feature representation has been widely used and developed recently. Multi-scale features have led to remarkable breakthroughs for representation learning process in many computer vision tasks. This paper aims to provide a comprehensive survey of the recent multi-scale representation learning achievements in classification tasks. Multi-scale represe...

Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. However, existing autofocus algorithms either have poor focusing quality or a slow focusing speed. In thi...

Existing learned-based image compression methods have shown impressive performance. However, they rely mostly on the consistent distribution between training and test images, which reduces the robustness of the training model. In this paper, we propose a novel compression method called sparse flow adversarial model (SFAM). SFAM employs a deep gener...

Due to the complementary properties of different types of sensors, change detection between heterogeneous images receives increasing attention from researchers. However, change detection cannot be handled by directly comparing two heterogeneous images since they demonstrate different image appearances and statistics. In this article, we propose a d...

Panchromatic (PAN) and multispectral (MS) images have coordinated and paired spatial spectral information, which can complement each other and make up for their shortcomings for image interpretation. In this article, a novel classification method called the deep group spatial-spectral attention fusion network is proposed for PAN and MS images. Firs...

Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used. In recent years, deep learning methods have rapidly become widespread in the field of video object detection, achieving excellent results compared with those of traditional methods. However, the presence of duplicate information and abundant spat...

Deep learning (DL) has become a hot topic in the research field of hyperspectral image (HSI) classification. However, with increasing depth and size of deep learning methods, its application in mobile and embedded vision applications has brought great challenges. In this article, we address a network architecture search (NAS)-guided lightweight spe...

Automated male pelvic multi-organ segmentation on CT images is highly desired for applications, including radiotherapy planning. To further improve the performance and efficiency of existing automated segmentation methods, in this study, we propose a multi-task edge-recalibrated network (MTER-Net), which aims to overcome the challenges, including b...

With the development of the imaging technology of various sensors, multisource image classification has become a key challenge in the field of image interpretation. In this article, a novel classification method, called the deep multiview union learning network (DMULN), is proposed to classify multisensor data. First, an associated feature extracto...

Extracting effective features is always a challenging problem for texture classification because of the uncertainty of scales and the clutter of textural patterns. For texture classification, spectral analysis is traditionally employed in the frequency domain. Recent studies have shown the potential of convolutional neural networks (CNNs) when deal...

Automated multi-organ segmentation on abdominal CT images may replace or complement manual segmentation for clinical applications including image-guided radiation therapy. However, the accuracy of auto-segmentation is challenged by low image contrast, large spatial and inter-patient anatomical variations. In this study, we propose an end-to-end seg...

Supervised change detection methods always face a big challenge that the current scene (target domain) is fully unlabeled. In remote sensing, it is common that we have sufficient labels in another scene (source domain) with a different but related data distribution. In this article, we try to detect changes in the target domain with the help of the...

In the last five years, deep learning has been introduced to tackle the hyperspectral image (HSI) classification and demonstrated good performance. In particular, the convolutional neural network (CNN)-based methods for HSI classification have made great progress. However, due to the high dimensionality of HSI and equal treatment of all bands, the...

Accurate segmentation of organs-at-risk (OARs) is necessary for adaptive head and neck (H&N) cancer treatment planning but manual delineation is tedious, slow, and inconsistent. A Self-Channel-and-Spatial-Attention neural network (SCSA-Net) is developed for H&N OARs segmentation on CT images. To simultaneously ease the training and improve the segm...

Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularied Band Selection via Learned Pairwise Agreement (RBS-LPA), was proposed. The process was formulated as a graph-regularized row-sparse constrained optimization problem, which...

Synthetic aperture radar (SAR) image classification is an important part in the understanding and interpretation of SAR images. Each patch in SAR images has a scene category, but usually contains multiple land-cover classes or latent properties, which can be represented by topics in the probabilistic topic model (PTM). The representation and select...

In recent years, with the diversification of acquisition methods of very high resolution panchromatic (PAN) and multispectral (MS) remote sensing images, multiresolution remote sensing classification has become a research hotspot. In this paper, from the perspective of data–driven deep learning, we design a dual–branch attention fusion deep network...

Type 2 innate lymphoid cells (ILC2s) are a newly identified group of innate immune cells. ILC2s promote features of allergic airway diseases through the secretion of Th2 type cytokines, including interleukin (IL)-4, IL-5 and IL-13. It remains unknown whether ILC2s aggregate in the peripheral blood. The present study examined the ILC2 levels in pedi...

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people’s life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection task...

The aim of the present study was to investigate the expression and role of the co‑stimulatory molecule T‑lymphocyte activation antigen CD86 (CD86) in dendritic cells (DCs) from the peripheral blood of patients with allergic rhinitis (AR) compared with those from healthy individuals. It was observed that mature DCs from the peripheral blood of patie...

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks,...

In order to obtain high classification accuracy and reduce time consumption for large-scale polarimetric synthetic-aperture radar (PolSAR) data. In this letter, we propose a fast semisupervised classification algorithm using histogram-based density estimation (called FSHDE). First, a noniterative collaborative training using our proposed Wishart-cl...

Supervised deep neural networks (DNNs) have been extensively used in diverse tasks. Generally, training such DNNs with superior performance requires a large amount of labeled data. However, it is time-consuming and expensive to manually label the data, especially for tasks in remote sensing, e.g., change detection. The situation motivates us to res...

Purpose: Image-guided radiotherapy provides images not only for patient positioning but also for online adaptive radiotherapy. Accurate delineation of organs-at-risk (OARs) on Head and Neck (H&N) CT and MR images is valuable to both initial treatment planning and adaptive planning, but manual contouring is laborious and inconsistent. A novel metho...

Nowadays, with the development of remote sensing technology, very-high-resolution (VHR) remote sensing image object detection technology attracts more and more attention. However, various challenges still exist in remote sensing image object detection field, such as the complex and varied appearances, the expensive manual annotation, and difficult...

In this paper, a high-quality depth-image-based-rendering-based free viewpoint generation model is proposed, which consists of three parts. First, a boundary-aware 3-D warping is introduced for projecting the side views from original view plane to target virtual view plane. Specifically, a high-pass filter is used to remove the ghost contours along...