Min Xia

• PhD
• Professor (Full) at Nanjing University of Information Science and Technology

Cloud detection is a critical preprocessing step for optical remote sensing imagery. However, traditional CNN-based methods have limitations in global feature modeling, while Transformer models, despite their strong global modeling capability, struggle to capture fine-grained local details effectively. To tackle these challenges, this study introdu...

The purpose of change detection is to recognize changed areas from a pair of two remote sensing images. However, since change areas often include multiple terrain features, this demands enhanced feature extraction capability from the model. This paper proposes a frozen-parameter Transformer self-attention change detection network (ZAQNet). The netw...

Style transfer is a technique that learns style features from different domains and applies these features to other images. It can not only play a role in the field of artistic creation but also has important significance in image processing, video processing, and other fields. However, at present, style transfer still faces some challenges, such a...

The combined use of synthetic aperture radar (SAR) and optical images for surface observation is gaining increasing attention. Optical images, with their distinct edge features, can accurately classify different objects, while SAR images reveal deeper internal variations. To address the challenge of differing feature distributions in multi-source i...

Change detection (CD) aims to explore surface changes in co-aligned image pairs. However, many existing networks primarily focus on learning deep features, without considering the impact of attention and fusion strategies on detection performance. Therefore, a new Frequency-Temporal-Aware Network (FTA-Net) is proposed, it recognizes changes by mean...

Land cover change detection (LCCD) is a crucial research topic for rational planning of land use and facilitation of sustainable land resource growth. However, due to the complexity of LCCD tasks, integrating global and local features and fusing contextual information from remote sensing features are essential. Recently, with the advent of Mamba, w...

Unit commitment (UC) is a critical component for the power system dispatching departments. Current methodologies for solving UC problems predominantly rely on mixed-integer linear programming and are supplemented by data-driven approaches. These methodologies have two primary limitations: first, as the scale of the power grid expands, the complexit...

Traditional medical image sensors face multiple challenges. First, these sensors typically rely on large amounts of labeled data, which are time-consuming and costly to obtain. Second, when the data volume and image size are large, traditional sensors have limited computational power, making it difficult to effectively train and infer models. Addit...

In the context of Vertical Federated Learning (VFL), agents utilize multimodal data on their edge devices to corporately train and inference with the deep learning models. However, in classical VFL, there exists three problems from the perspective of embeddings. Firstly, the utilization of oversimplified embedding fusion mechanism may result in sub...

Carbon dioxide is one of the most influential greenhouse gases affecting human life. CO2 data can be obtained through three methods: ground-based, airborne, and satellite-based observations. However, ground-based monitoring is typically composed of sparsely distributed stations, while airborne monitoring has limited coverage and spatial resolution;...

Semantic segmentation is primarily employed to generate accurate prediction labels for each pixel of the input image, and then classify the images according to the generated labels. Semantic segmentation of building and water in remote sensing images helps us to conduct reasonable land planning for a city. However, many current mature networks face...

Accurate forecasting of multienergy loads is essential for designing, operating, scheduling, and managing integrated energy systems (IESs). Recent research suggests that transformer models have the potential to improve long-sequence predictions. However, existing transformer models often emphasize capturing temporal dependencies while neglecting cr...

Parameter identification of transmission lines plays a crucial role in power systems, and many deep learning methods have been continuously applied to this domain. However, these methods are highly sensitive to data corruption, and as the scale of the power grid continues to expand, the model’s solving accuracy deteriorates. In response to these ch...

Federated learning has received a great deal of research attention recently,with privacy protection becoming a key factor in the development of artificial intelligence. Federated learning is a special kind of distributed learning framework, which allows multiple users to participate in model training while ensuring that their privacy is not comprom...

Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms...

Remote sensing image change detection (CD) is an important means in remote sensing data analysis tasks, which can help us understand the surface changes in high-resolution (HR) remote sensing images. Traditional pixel-based and object-based methods are only suitable for low- and medium-resolution images, and are still challenging for complex textur...

In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection syst...

Change detection is widely used in the field of building monitoring. In recent years, the progress of remote sensing image technology has provided high-resolution data. However, unlike other tasks, change detection focuses on the difference between dual-input images, so the interaction between bi-temporal features is crucial. However, the existing...

Cloud and snow in remote sensing images typically block the underlying surface information and interfere with the extraction of available information, so detecting cloud and snow becomes a critical problem in remotely sensed image processing. The current methods for detecting clouds and snow are susceptible to interference from complex background,...

Temporal Action Detection (TAD) aims to accurately capture each action interval in an untrimmed video and to understand human actions. This paper comprehensively surveys the state-of-the-art techniques and models used for TAD task. Firstly, it conducts comprehensive research on this field through Citespace and comprehensively introduce relevant dat...

The severity of the global warming issue emphasizes the critical importance of utilizing carbon satellite data to estimate groundlevel carbon dioxide emissions. However, existing reviews have not kept pace with the latest research developments. Therefore, this paper provides an overview of relevant work in the global carbon emissions field to addre...

Remote sensing image change detection plays an important role in urban planning and environmental monitoring. However, the existing change detection algorithms have limited ability in feature extraction, feature relationship understanding, and capture of small target features and edge detail features, which leads to the loss of some edge detail inf...

Semantic segmentation of cloud and shadow is an important task in remote sensing and atmospheric science. However, the complexity of cloud/shadow shapes, and noise disturbances (such as snow and ice, buildings, complex backgrounds, and atmospheric optics) make this task challenging. The traditional deep network has good details and generalization d...

The segmentation task of cloud and cloud shadow has always been one of the important tasks in remote sensing image processing. At present, cloud detection based on deep learning methods lacks generalization, which is easy to cause the loss of space and detail information, and missed detection and false detection occur from time to time. Aiming at t...

Driven by economic incentives, illegal electricity consumers pose significant threats to the economic and security aspects of the power system by illicitly accessing or manipulating electrical resources. With the widespread adoption of Advanced Metering Infrastructure (AMI), researchers have turned to leveraging smart meter data for electricity the...

With the advancement of remote sensing (RS) image technology, the availability of very high-resolution (VHR) image data has brought new challenges to change detection (CD). Currently, deep learning-based CD methods commonly employ bitemporal interaction networks using convolutional neural networks (CNNs) or transformers. Yet, these models overly em...

The ground is typically hidden by cloud and snow in satellite images, which have a similar visible spectrum and complex spatial distribution characteristics. The detection of cloud and snow is important for increasing image availability and studying climate change. To address the issues of the low classification accuracy and poor generalization eff...

In action recognition, obtaining skeleton data from human poses is valuable. This process can help eliminate negative effects of environmental noise, including changes in background and lighting conditions. Although GCN can learn unique action features, it fails to fully utilize the prior knowledge of human body structure and the coordination relat...

Cloud and cloud shadow segmentation is one of the most critical challenges in remote sensing image processing. Because of susceptibility to factors such as disturbance from terrain features and noise, as well as a poor capacity to generalize, conventional deep learning networks, when directly used to cloud and cloud shade detection and division, ha...

With the rapid development of electric power enterprises and the Internet, a large amount of data has generated in marketing activities. Marketing inspection is an important means to ensure the power market, however, traditional marketing inspection methods have many limitations in dealing with massive data, facing problems such as low screening ef...

A transmission line parameter identification method based on graph neural network (MNGAN) is proposed. The self-supervised graph attention network (SuperGAT) is used to learn the branch characteristics of the power grid. After selecting the appropriate attention form, the network can dynamically learn the relationship between the current node and o...

Recently, deep learning-based change detection methods for bitemporal remote sensing images have achieved promising results based on fully convolutional neural networks. However, due to the inherent characteristics of convolutional neural networks, if the previous block fails to correctly segment the entire target, erroneous predictions might accum...

Among the most difficult difficulties in contemporary satellite image-processing subjects is cloud and cloud shade segmentation. Due to substantial background noise interference, existing cloud and cloud shadow segmentation techniques would result in false detection and missing detection. We propose a Location Pooling Multi-Scale Network (LPMSNet)...

Change detection is an important component in the field of remote sensing. At present, deep-learning-based change-detection methods have acquired many breakthrough results. However, current algorithms still present issues such as target misdetection, false alarms, and blurry edges. To alleviate these problems, this work proposes a network based on...

Existing underwater image enhancement algorithms rely on paired datasets, which enhance underwater images by learning the mapping relationship between low-quality and high-quality data. However, currently, high-quality data (which are called real data) are artificially selected by the dataset builders from the results of previous algorithms, and th...

Change detection is important in remote sensing image analysis. In recent years, significant breakthroughs have been made in change detection algorithms based on deep learning. However, due to continuous downsampling, the detection results of these algorithms still have serious detection errors, detection omissions and edge blurring. Aiming at thes...

Cloud and cloud shadow detection are essential in remote sensing imagery applications. Few semantic segmentation models were designed specifically for clouds and their shadows. Based on the visual and distribution characteristics of clouds and their shadows in remote sensing imagery, this paper provides a multi-supervised feature fusion attention n...

In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises. At present, traditional machine learning methods (such as SVM, random forest, Markov mode...

The analysis of land cover types is helpful for detecting changes in land use categories and evaluating land resources. It is of great significance in environmental monitoring, land management, land planning, and mapping. At present, remote sensing imagery obtained by remote sensing is widely employed in the classification of land types. However, m...

Federated Learning (FL) is an algorithm for the encrypted exchange of model parameters while ensuring the independence of participants. Classic federated learning does not take into account the correlation between features, nor does it take into account the data differences caused by the reasonable personalization of each client. Therefore, this pa...

At present, 3D reconstruction technology is being gradually applied to underwater scenes and has become a hot research direction that is vital to human ocean exploration and development. Due to the rapid development of computer vision in recent years, optical image 3D reconstruction has become the mainstream method. Therefore, this paper focuses on...

The change-detection task is essentially a binary semantic segmentation task of changing and invariant regions. However, this is much more difficult than simple binary tasks, as the changing areas typically include multiple terrains such as factories, farmland, roads, buildings, and mining areas. This requires the ability of the network to extract...

Graph Convolutional Networks (GCNs) have become the standard skeleton-based human action recognition research paradigm. As a core component in graph convolutional networks, the construction of graph topology often significantly impacts the accuracy of classification. Considering that the fixed physical graph topology cannot capture the non-physical...

Extracting mask information of buildings and water areas from high resolution remote sensing images is beneficial to monitoring and management of urban development. However, due to different times, different geographical locations and different remote sensing acquisition angles, water areas and buildings will feed back different spectral informatio...

Cloud detection is a critical task in remote sensing image tasks. Due to the influence of ground objects and other noises, the traditional detection methods are prone to miss or false detection and rough edge segmentation in the detection process. To avoid the defects of traditional methods, Cloud and Cloud Shadow Refinement Segmentation Networks a...

The segmentation algorithm for buildings and waters is extremely important for the efficient planning and utilization of land resources. The temporal and space range of remote sensing pictures is growing. Due to the generic convolutional neural network’s (CNN) insensitivity to the spatial position information in remote sensing images, certain locat...

Understanding surface changes requires the ability to identify changes in high resolution remote sensing images. Because current deep learning-based change detection algorithms are not able to accurately discriminate between altered and unmodified areas, which leads to the problem of edge uncertainty and small target missing in the detection proces...

The non-intrusive load decomposition method helps users understand the current situation of electricity consumption and reduce energy consumption. Traditional methods based on deep learning are difficult to identify low usage appliances, and are prone to model degradation leading to insufficient classification capacity. To solve this problem, this...

Because clouds and snow block the underlying surface and interfere with the information extracted from an image, the accurate segmentation of cloud/snow regions is essential for imagery preprocessing for remote sensing. Nearly all remote sensing images have a high resolution and contain complex and diverse content, which makes the task of cloud/sno...

Extracting buildings and water bodies from high-resolution remote sensing images is of great significance for urban development planning. However, when studying buildings and water bodies through high-resolution remote sensing images, water bodies are very easy to be confused with the spectra of dark objects such as building shadows, asphalt roads...

The systematicness of banks is an important driver of financial crisis. Overlapping portfolios and assets correlation of banks’ investment are important reasons for systemic risk contagion. The existing systemic risk models are all analyzed from one aspect and cannot reflect the real situation of the banking system. In the present paper, considerin...

In Remote Sensing(RS) data analysis, Remote Sensing Change Detection(CD) is an important technology. The existing Remote Sensing Change Detection(RS-CD) methods do not fully consider the advantages and disadvantages of Convolution and Transformer in feature extraction, which will restrict the overall performance of the network to a certain extent....

Architectural image segmentation refers to the extraction of architectural objects from remote sensing images. At present, most neural networks ignore the relationship between feature information, and there are problems such as model overfitting and gradient explosion. Thus, this paper proposes an improved UNet based on ResNet34 and Attention Modul...

Traditional building and water segmentation methods are vulnerable to noise interference, and hence they could not avoid missed and false detections in the detection process. Excessive deep learning downsampling would lead to significant loss of feature map information, and image location information offset, and the overall effect of falling apart....

Cloud and cloud shadow segmentation is one of the most important problems in remote sensing image processing. Due to the vulnerability to ground object interference, noise interference and other factors, and the lack of generalization ability, traditional deep learning network would inevitably lose details and spatial information, resulting in impr...

With the introduction of earth observation satellites, the classification technology through high-definition remote sensing images appeared. After decades of evolution, the land cover classification method in high-definition satellite maps has been gradually improved. Recently, high-definition remote sensing maps have been applied to land cover cla...

Accurately predicting multi-energy loads is essential for optimizing the dispatch and economic operation of integrated energy systems (IES). However, existing multi-energy load forecasting methods have two main limitations: (1) they fail to consider the complex correlations between multi-energy loads and auxiliary features; (2) single time-scale fe...

With increasingly rapid development of Convolutional Neural Networks (CNNs), the field of remote sensing has experienced a significant revitalization. However, understanding and detecting surface changes, which necessitate the identification of high-resolution remote sensing images, remain substantial challenges in achieving precise change detectio...

Although the vision transformer-based methods (ViTs) exhibit excellent performance than convolutional neural networks (CNNs) for image recognition tasks, their pixel-level semantic segmentation ability is limited due to the lack of explicit utilization of local biases. Recently, a variety of hybrid structures of ViT and CNN have been proposed, but...

Land cover semantic segmentation is an important technique in land. It is very practical in land resource protection planning, geographical classification, surveying and mapping analysis. Deep learning shows excellent performance in picture segmentation in recent years, but there are few semantic segmentation algorithms for land cover. When dealing...

Financial distress prediction aims at providing an early warning solution of financial distress to help business participants, investors, and regulators to achieve better profit growth and financial risk management. Extreme gradient boosting (XGBoost), has been recognized as a favorable competitor compared with machine learning-based individual cla...

Cloud detection is an important prerequisite for remote sensing image application. Any remote sensing image from which the information of ground object could be obtained will inevitably be preprocessed on cloud occlusion. In the traditional method, the segmentation of cloud and its shadow will be affected by the complex background. In the detection...

In recent years, the resolution of remote sensing images, especially aerial images, has become higher and higher, and the spans of time and space have become larger and larger. The phenomenon in which one class of objects can produce several kinds of spectra may lead to more errors in detection methods that are based on spectra. For different convo...

Load decomposition technology is an important aspect of power intelligence. At present, there are mainly machine learning methods based on artificial features and deep learning methods for load decomposition. The method based on artificial features has a difficult time obtaining effective load features, leading to low accuracy. The method based on...

Human action recognition (HAR) is the foundation of human behavior comprehension. It is of great significance and can be used in many real-world applications. From the point of view of human kinematics, the coordination of limbs is an important intrinsic factor of motion and contains a great deal of information. In addition, for different movements...

Cloud and snow recognition technology is of great significance in the field of meteorology, and is also widely used in remote sensing mapping, aerospace, and other fields. Based on the traditional method of manually labeling cloud-snow areas, a method of labeling cloud and snow areas using deep learning technology has been gradually developed to im...

Credit scoring is an effective tool for banks or lending institutions to identify potential bad lenders and creditworthy applicants. Boosting ensemble approaches have made appealing progress for credit scoring. However, classical boosting ensemble models realize credit scoring to minimize the misclassification error of credit datasets, which implie...

With the continuous improvement of the segmentation effect for natural datasets, some studies have gradually been applied to high-resolution remote sensing images (HRRSIs). Due to a large amount of ground object information contained, even objects of the same type present the diversity and complexity of features in different periods or locations. T...

In the parameter identification of power transmission system, deep learning methods stand out because of its effectiveness and robustness. However, deep learning methods usually suffers from two limitations: (1) The power grid topology structure data belongs to non-Euclidean data, so the traditional deep learning methods can’t deal with this data,...

Remote sensing image change detection is an important technology in remote sensing data analysis. Existing mainstream solutions are divided into supervised and unsupervised solutions. Among supervised methods, most remote sensing image change methods based on deep learning are related to semantic segmentation. However, these methods only use deep l...

Cloud and cloud shadow detection is a crucial issue in remote sensing image processing. The backgrounds of clouds and cloud shadows are mostly complex in actual remote sensing images. Traditional methods are easily affected by ground object interference, noise interference and other factors, and problems such as missing detection and false detectio...

The use of remote sensing images for land cover analysis has broad prospects. At present, the resolution of aerial remote sensing images is getting higher and higher, and the span of time and space is getting larger and larger, therefore segmenting target objects enconter great difficulties. Convolutional neural networks are widely used in many ima...

Federated Learning (FL) can combine multiple clients for training and keep client data local, which is a good way to protect data privacy. There are many excellent FL algorithms. However, most of these can only process data with regular structures, such as images and videos. They cannot process non-Euclidean spatial data, that is, irregular data. T...

Credit scoring is an important process for banks and financial institutions to manage credit risk. Tree-based ensemble algorithms have made promising progress in credit scoring. However, tree-based ensemble algorithms lack representation learning, making them cannot well express the potential distribution of loan data. In this study, we propose a m...

Designing a lightweight and robust real-time land cover segmentation algorithm is an important task for land resource applications. In recent years, with the development of edge computing and the increasing resolution of remote sensing images, the huge amount of calculations and parameters have restricted the efficiency of real-time semantic segmen...

In the task of human motion recognition, the overall action span is changeable, and there may be an inclusion relationship between action semantics. This paper proposes a novel multi-scale time sampling module and a deep spatiotemporal feature extraction module, which strengthens the receptive field of the feature map and strengthens the extraction...

In the application of remote sensing, cloud blocking brings trouble to the analysis of surface parameters and atmospheric parameters. Due to the complexity of the background, the influence of some cloud-like interferences (such as ice, snow, buildings, etc.) and the complexity of the cloud shape, the traditional deep learning method is difficult to...