Yi Wang

Yi Wang
China University of Geosciences · Department of Geophysics and Geomatics

Professor

About

67
Publications
24,730
Reads
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2,012
Citations
Introduction
Ph.D., State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University (2002.9 - 2007.7) . Visiting Scholar at Vrije Universiteit Brussel (VUB), Brussels (2013.7-2014.7). Professor, PhD Tutor, Dean of Geoinformatics, China University of Geosciences, Wuhan (2016.12 - present). My current project is “Landslide detection using remote sensing images” and “Natural disaster susceptibility mapping”.
Additional affiliations
December 2016 - September 2017
China University of Geosciences
Position
  • Managing Director
December 2016 - November 2018
China University of Geosciences
Position
  • Managing Director
July 2013 - July 2014
Vrije Universiteit Brussel
Position
  • Researcher
Education
September 2002 - June 2007
Wuhan University
Field of study
  • Photogrammetry and Remote Sensing
September 1998 - June 2002
Wuhan University
Field of study
  • Printing Engineering

Publications

Publications (67)
Article
Assessments of landslide disasters are becoming increasingly urgent. The aim of this study is to investigate a convolutional neural network (CNN) framework for landslide susceptibility mapping (LSM) in Yanshan County, China. The two primary contributions of this study are summarized as follows. First, to the best of our knowledge, this report descr...
Article
In this study, an effective kernel-based learning framework for landslide susceptibility mapping (LSM) is presented through an implementation of support vector machines (SVMs) with different composite kernels. Kernel-based classification methods are very popular in statistical classification and regression analysis because they can effectively addr...
Article
This study introduces four heterogeneous ensemble-learning techniques , that is, stacking, blending, simple averaging, and weighted averaging, to predict landslide susceptibility in Yanshan County, China. These techniques combine several state-of-the-art classifiers of convolutional neural network, recurrent neural network, support vector machine,...
Article
Landslides are regarded as one of the most common geological hazards in a wide range of geo-environment. The aim of this study is to assess landslide susceptibility by integrating convolutional neural network (CNN) with three conventional machine learning classifiers of support vector machine (SVM), random forest (RF) and logistic regression (LR) i...
Article
Due to the fast development of imaging sensors used in remote sensing, high-resolution images can increasingly exhibit fine-grained information on the Earth’s surface, which makes detecting real-world small-scale, weak-feature-response geospatial targets possible. Detecting these small weak targets is of great significance in some applications. Alt...
Preprint
Full-text available
Portraying spatiotemporal variations in landslide susceptibility patterns is crucial for landslide prevention and management. In this study, we implement a space-time modeling approach to predict the landslide susceptibility on a yearly basis across the main island of Taiwan, from 2004 to 2018. We use a Bayesian version of a binomial generalized ad...
Article
Full-text available
Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based...
Article
Full-text available
Landslide susceptibility prediction is a key step in preventing and managing landslide hazards. As a classical supervised non-parametric machine learning model, support vector machine (SVM) has been widely used in landslide susceptibility prediction in recent years. However, most studies focus on the application of general SVM methods, or compare S...
Research Proposal
Full-text available
The aim of this Special Issue is to collect the most recent research on remote sensing applications in earth sciences. In particular, this Special Issue is dedicated to satellite, aerial and terrestrial contactless devices for observation and evaluation of earth surface changes and deformations caused by earthquake and landslide, and new processing...
Article
Landslides have caused tremendous damage to human lives and property safety. However, the complex environment of mountain landslides and the vegetation coverage around landslides make it difficult to identify landslides quickly and efficiently using high-resolution images. To address this challenge, this article presents a feature-based constraint...
Article
Full-text available
Many studies consider landslide susceptibility prediction as a binary classification problem when using machine learning methods, which requires both landslide and non-landslide samples for modelling. Nevertheless, there are only landslide and unlabeled areas in the real world, and directly considering unlabeled areas as non-landslide areas may cau...
Article
Episodes of frequent flooding continue to increase, often causing serious damage and tools to identify areas affected by such disasters have become indispensable in today's society. Using the latest techniques can make very accurate flood predictions. In this study, we introduce four effective methods to evaluate the flood susceptibility of Poyang...
Article
Full-text available
A hybrid framework by integrating stacking ensemble with two deep learning methods of convolutional neural network (CNN) and recurrent neural network (RNN) is introduced in this paper for landslide spatial prediction in the Three Gorges Reservoir area, China. The proposed framework is summarized in following steps. First, a spatial database consist...
Article
Full-text available
Identifying floods and producing flood susceptibility maps are crucial steps for decision-makers to prevent and manage disasters. Plenty of studies have used machine learning models to produce reliable susceptibility maps. Nevertheless, most studies ignore the importance of developing appropriate feature engineering methods. In this study, we propo...
Article
Full-text available
This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning...
Article
Full-text available
Landslide early warning systems (EWSs) have been widely used to reduce disaster losses. The effectiveness of a landslide EWS depends highly on the prediction methods, and it is difficult to correctly predict landslides in a timely manner. In this paper, we propose a real-time prediction method to provide real-time early warning of landslides by com...
Article
Full-text available
Ensemble learning methods have been widely used due to their remarkable generalized performance, but their potential in landslide spatial prediction application is not fully studied. To take full advantage of ensemble learning techniques, the classification and regression tree classifier and four tree-based ensemble classifiers of random forest, ex...
Article
Flood is a very destructive natural disaster in the world, which seriously threatens the safety of human life and property. In this paper, the most popular convolutional neural network (CNN) is introduced to assess flood susceptibility in Shangyou County, China. The main contributions of this study are summarized as follows. First, the CNN techniqu...
Article
In this study, we present a hyperspectral image classification method by combining spectral, spatial, and semantic information. The main steps of the proposed method are summarized as follows: First, principal component analysis transform is conducted on an original image to produce its extended morphological profile, Gabor features, and superpixel...
Article
The detection of zones exposed to flash-flood and also the torrential valleys on which flash-floods are propagated, represents a crucial measure intended to eliminate the issues generated by these phenomena. In this paper, in order to locate the regions prone to runoff occurrence, a number of 4 hybrid models were employed: Naïve Bayes – Certainty F...
Article
Full-text available
The most dangerous landslide disaster always causes serious economic losses and people’s death on humanity. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey...
Article
Flooding is a very common natural hazard that causes catastrophic effects worldwide. Recently, ensemble-based techniques have become popular in flood susceptibility modelling due to their greater strength and efficiency in the prediction of flood locations. Thus, the aim of this study was to employ machine learning-based Reduced-error pruning trees...
Article
In this work, we present a novel spectral-spatial classification framework of hyperspectral images (HSIs) by integrating the techniques of algebraic multigrid (AMG), hierarchical segmentation (HSEG) and Markov random field (MRF). The proposed framework manifests two main contributions. First, an effective HSI segmen-tation method is developed by co...
Article
Full-text available
Floods are considered one of the most disastrous hazards all over the world and cause serious casualties and property damage. Therefore, the assessment and regionalization of flood disasters are becoming increasingly important and urgent. To predict the probability of a flood, an essential step is to map flood susceptibility. The main objective of...
Article
Full-text available
The present study is aimed at producing landslide susceptibility map of a landslide-prone area (Anfu County, China) by using evidential belief function (EBF), frequency ratio (FR) and Mahalanobis distance (MD) models. To this aim, 302 landslides were mapped based on earlier reports and aerial photographs, as well as, carrying out several field surv...
Article
In this paper, we introduce a novel classification framework for hyperspectral images (HSIs) by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, the spectral kernel is constructed through e...
Article
Full-text available
In this work, an effective framework for landslide susceptibility mapping (LSM) is presented by integrating information theory, K-means cluster analysis and statistical models. In general, landslides are triggered by many causative factors at a local scale, and the impact of these factors is closely related to geographic locations and spatial neigh...
Article
Full-text available
Due to the relatively low temporal resolutions of high spatial resolution (HR) remotely sensed images, land-cover change detection (LCCD) may have to use multi-temporal images with different resolutions. The low spatial resolution (LR) images often have high temporal repetition rates, but they contain a large number of mixed pixels, which may serio...
Article
Anisotropic diffusion can provide better compromise between noise reduction and edge preservation. In multispectral images, there exist different spatial local structures in the same band. Therefore, the levels of smoothing of anisotropic diffusion process should conform to both of image spectral and spatial features. In this paper, we present an e...
Article
Classification of hyperspectral images always suffers from high dimensionality and very limited labeled samples. Recently, the spectral-spatial classification has attracted considerable attention and can achieve higher classification accuracy and smoother classification maps. In this paper, a novel spectral-spatial classification method for hypersp...
Article
Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In this method, a novel technique for labeling regions...
Article
In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction r...
Article
Full-text available
Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this pa...
Article
Full-text available
The algebraic multigrid (AMG) method is used to solve linear systems of equations on a series of progressively coarser grids and has recently attracted significant attention for image segmentation due to its high efficiency and robustness. In this paper, a novel spectral-spatial classification method for hyperspectral images based on the AMG method...
Article
To improve accuracy and efficiency of object detection and classification with hyperspectral imagery (HSI), we propose a novel smoothing algorithm by coupling of a Laplacian-based reaction term to a classical divergence-based anisotropic diffusion partial differential equation (PDE). In addition, an adaptive parameter is introduced to regularize th...
Article
Due to the lack of support for a high-resolution image in a short time, land cover change detection is always applied on the multitemporal remote-sensed images with different resolutions. The coarse-resolution image contains a large number of mixed pixels, which can seriously limit the utility of the change detection. Soft classification (SC) can b...
Article
Full-text available
It is well known that soil erosion at the watershed scale is the result of interactions between various factors. Among these environmental factors, vegetation is the most important and plays a major role in the soil erosion process. The impact of fractional vegetation cover change (FVCC) on soil erosion in non-contributing areas is a heavily discus...
Article
Full-text available
Landslides are one of the most destructive phenomena in nature and damage both property and lives every year. In this paper, a logistic regression model with datasets developed via a geographic information system and remotely sensed data was used to create a landslide spatial susceptibility map for the Three Gorges Project reservoir region on the Y...
Article
This article proposes a spatial–temporal expansion method for remote-sensing reflectance by blending observations from sensors with different spatial and temporal characteristics. Compared with the methods used in the past, the main characteristic of the proposed method is consideration of sensor observation differences between different cover type...
Article
Full-text available
The urban fringe is the transition zone between urban land use and rural land use. It represents the most active part of the urban expansion process. Change detection using multi-temporal imagery is proven to be an efficient way to monitor land-use/land-cover change caused by urban expansion. In this study, we propose a new multi-temporal classific...
Article
Full-text available
In this paper, percentage of vegetation cover in different districts of Wuhan were calculated based on remote sensing images from 1988 to 2002 by employing NDVI method for dimidiate pixel model. Then the vegetation coverage maps in different periods were generated to analyze the temporal change of vegetation coverage of Wuhan. The results showed th...
Article
Full-text available
This work presents a scale-based forward-and-backward diffusion (SFABD) scheme. The main idea of this scheme is to perform local adaptive diffusion using local scale information. To this end, we propose a diffusivity function based on the Minimum Reliable Scale (MRS) of Elder and Zucker (IEEE Trans. Pattern Anal. Mach. Intell. 20(7), 699-716, 1998)...
Article
Hyperspectral imagery contains a large number of mixed pixels, which limits its utility. Super-resolution mapping is a potential solution to this problem, designed to use the proportion of land covers to obtain a sharpened thematic map with higher resolution. Endmember is a fundamental variable in the process, which is a critical issue for decompos...
Article
A novel region-based adaptive anisotropic diffusion (RAAD) is presented for image enhancement and denoising. The main idea of this algorithm is to perform the region-based adaptive segmentation. To this end, we use the eigenvalue difference of the structure tensor of each pixel to classify an image into homogeneous detail, and edge regions. Accordi...
Article
Full-text available
Soil conservation planning often requires estimates of the spatial distribution of soil erosion at a catchment or regional scale. This paper applied the Revised Universal Soil Loss Equation (RUSLE) to investigate the spatial distribution of annual soil loss over the upper basin of Miyun reservoir in China. Among the soil erosion factors, which are...
Article
The performance of remote sensing images in some applications is often affected by the existence of noise, blurring, stripes and corrupted pixels, as well as the hardware limits of the sensor with respect to spatial resolution. This paper presents a universal reconstruction method that can be used to improve the image quality by performing image de...
Article
In order to improve signal-to-noise ratio (SNR) and contrast-to-noise ratio, we introduces a novel tunable forward-and-backward (TFAB) diffusion approach for image restoration and edge enhancement. In the TFAB algorithm, an alternative forward-and-backward (FAB) diffusion process is presented, where it is possible to better modulate all aspects of...
Article
Among all enhancement techniques being developed over the past two decades, anisotropic diffusion has received much attention and experienced significant developments, with promising results and applications in various specific domains. The elegant property of the technique is that it can enhance images by reducing undesirable intensity variability...
Article
The key point of research on anisotropic diffusion is to build the adaptive and stable diffusion model. However, there are still two problems in the existing anisotropic diffusion models-the determination of the gradient threshold and the iterative stopping time. In this paper, we propose a time-dependent robust anisotropic diffusion method. In the...
Article
Full-text available
In remote sensed images, mixed pixels will always be present. Sub-pixel mapping is a technique to farther make sure the spatial distribution of all classes. The present sub-pixel mapping techniques always have a limit to the accuracy since they are based only on the soft-classified proportion data at the pixel level. In fact, supplementary informat...
Article
This paper proposes a novel multiscale graph cut based analysis framework for the supervised classification of hyperspectral imagery. This framework is aimed at obtaining accurate and reliable maps by properly considering the spatial-context information. It is made up of two main blocks: 1) a feature-extraction block exploits an object-oriented ana...
Article
In order to improve signal-to-noise ratio (SNR) and image quality, this paper introduces a wavelet-based multiscale anisotropic diffusion algorithm to remove speckle noise and enhance edges. In our algorithm, we use the tool of wavelet to construct a linear scale-space for the speckle image. Due to the smoothing functionality of the scaling functio...
Conference Paper
Anisotropic diffusion has received a lot of attention and has experienced significant developments, with promising results and applications in several specific domains. In this paper, a general flexible class of hyperspectral forward-and-backward (FAB) diffusion process will be proposed, which can achieve the main requirements for edge-preserving r...
Article
Super resolution (SR) image reconstruction technique has the performance to produce a high resolution image from several low resolution images. Therefore, it has been a hot topic in the field of image processing. The basic principle of SR reconstruction is introduced, and the relations between it and other image processing techniques are described....
Article
Objective To analyze the clinical value of the treatment by ultrasound-guided percutaneous puncture in liver abscess. Methods The percutaneous puncture was operated in 29 cases of liver abscess under the ultrasound-guidance. Results The body temperature returned to normal within three days after percutaneous liver puncture or catheter drainage. The...
Article
Among all enhancement techniques being developed over the past two decades, anisotropic diffusion has received a lot of attention and has experienced significant developments, with promising results and applications in several specific domains. The elegant property of the technique is that it can enhance images by reducing undesirable intensity var...
Article
Full-text available
This paper presents a universal maximum a posteriori (MAP) based reconstruction method which can be used for destriping, inpainting (the removal of dead pixels) and super resolution reconstruction (the recovery of a high resolution image from several low resolution images). In the MAP framework, the likelihood probability density function (PDF) is...
Article
Recently, many researchers have shown interests in anisotropic diffusion methods in image processing. There are two key problems on image restoration techniques based on anisotropic diffusion: one is to build stable diffusion coefficients; the other is to select the optimal stopping time for iterative diffusion process. In this paper, a time-depend...
Article
In order to improve signal-to-noise ratio (SNR) and contrast-to-noise ratio, this paper introduces a local variance-controlled forward-and-backward (LVCFAB) diffusion algorithm for edge enhancement and noise reduction. In our algorithm, an alternative FAB diffusion algorithm is proposed. The results for the alternative FAB algorithm show better alg...
Article
In order to effectively preserve SAR image edges while filtering, the authors propose an anisotropic diffusion filtering algorithm for speckle noise based on speckle reduction anisotropic diffusion (SRAD) model. On one hand, it can be proved that the diffusion coefficient in the proposed algorithm theoretically satisfies the conditions for the desi...
Article
Digital rights management (DRM) provides digital content creators and owners with a range of controls over how their information resources may be used. It is a fairly young discipline yet is becoming increasingly important as digital content can be copied and distributed so easily that the piracy of them is growing critical. In addition, with the r...
Conference Paper
Summary form only given. Anisotropic diffusion (AD) has been employed as an effective approach for image filtering. A great deal of work has been dedicated to the improvements of this technique. However, these efforts still left some difficulties unsolved. The first one is to estimate accurately image gradients in noisy images for nonlinear diffusi...

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Projects (2)
Project
In this project, we use new techniques of machine learning, data mining, and deep learning to map the susceptibility of natural disas