• Home
  • Wuhan University
  • State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing
  • Yanfei Zhong
Yanfei Zhong

Yanfei Zhong
Wuhan University | WHU · State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing

Doctor of Philosophy

About

296
Publications
61,780
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
8,331
Citations
Additional affiliations
July 2007 - present
Wuhan University
Position
  • Professor

Publications

Publications (296)
Article
With the acceleration of urban expansion, urban change detection (UCD), as a significant and effective approach, can provide the change information with respect to geospatial objects for dynamic urban analysis. In recent years, through the use of machine learning and artificial intelligence, change detection methods have gradually developed from th...
Conference Paper
Full-text available
Efficient and accurate landslide detection is of great significance for an emergency response to geological disasters. However, detecting landslides from remote sensing images faces two challenges: small objects and class imbalance, and distribution inconsistency. In this paper, the progressive label refinement-based distribution adaptation for the...
Preprint
Full-text available
The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence (IARAI) are presented here. The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally. The 2022 L4S aims to foster...
Article
Pine wilt disease (PWD) poses a serious threat to the worldwide pine forest resources. Unmanned aerial vehicle (UAV) remote sensing has been widely used for PWD control, due to its flexibility and efficiency. Although pixel-level detection can obtain fine detection boundaries, there have been few related works in complex scenes because of the diffi...
Conference Paper
Deep learning based remote sensing scene classification methods have become a research hotspot, but they can not fully mine the image information due to the architecture comes directly from natural image. The automatic search method-based network architecture has then attracted a lot of attention benefits by its ability to independently learn the n...
Article
Full-text available
Planning a practical three-dimensional (3-D) flight path for unmanned aerial vehicles (UAVs) is a key challenge for the follow-up management and decision making in disaster emergency response. The ideal flight path is expected to balance the total flight path length and the terrain threat, to shorten the flight time and reduce the possibility of co...
Preprint
Full-text available
Land-cover classification has long been a hot and difficult challenge in remote sensing community. With massive High-resolution Remote Sensing (HRS) images available, manually and automatically designed Convolutional Neural Networks (CNNs) have already shown their great latent capacity on HRS land-cover classification in recent years. Especially, t...
Article
Full-text available
Urban land-cover information is essential for resource allocation and sustainable urban development. Recently, deep learning algorithms have shown promising results in land-cover mapping with high spatial resolution (HSR) imagery. However, the limitation of the annotation and the divergence of the multi-sensor images always challenge the transferab...
Article
Full-text available
Some invasive tree species threaten biodiversity and cause irreversible damage to global ecosystems. The key to controlling and monitoring the propagation of invasive tree species is to detect their occurrence as early as possible. In this regard, one-class classification (OCC) shows potential in forest areas with abundant species richness since it...
Article
Accurate urban land-use maps, which reflect the complicated land-use pattern implied in the function and distribution of land-cover types, play an important role in urban analysis. In recent years, data-driven deep learning-based land-use mapping methods have made great breakthroughs due to their strong feature extraction ability. Meanwhile, multis...
Article
Pollution from tailings areas often introduces serious animal- and plant-associated ecological disasters and can even endanger human health. Communication-navigation-remote sensing (CNR)-integrated monitoring is expected to play a key role in the assessment of ecological environments in tailings areas, but CNR integration has long been a challenge...
Article
Global land cover mapping activities are of great important for retrieving the environment we are living in. However, the trade-off between spatial and temporal resolution makes it difficult to obtain the continuous fine-scale land cover product for detail and frequent land surface analysis. To overcome the difficulty, this study proposed a modifie...
Article
Due to the abundant features of high spatial resolution (HSR) remote sensing images, change detection of these images is crucial to understanding the land-use and land-cover (LULC) changes. However, previous works mostly focus on traditional binary change detection without considering the semantic information of the change classes. The latest progr...
Article
Full-text available
Contrast to the global forest, few trees live in cities but contribute significantly to urban environment and human health. However, the classical satellite-derived land cover/forest cover products with limited resolution are not fine enough for the identification of urban tree, which is usually appeared in small size and intersected with infrastru...
Article
High spatial and spectral resolution (H²) imagery collected by unmanned aerial vehicle (UAV) systems is an important data source for precise crop classification. Although this data source can provide us with abundant information about the crops of interest, it also introduces new challenges for the image processing. Specifically, the spectral simil...
Article
Full-text available
Multi-temporal high spatial resolution earth observation makes it possible to detect complex urban land surface changes, which is a significant and challenging task in remote sensing communities. Previous works mainly focus on binary change detection (BCD) based on modern technologies, e.g., deep fully convolutional network (FCN), whereas the deep...
Article
Remote sensing image scene classification is a challenging task. With the development of deep learning, methods based on convolutional neural networks (CNNs) have made great achievements in remote sensing image scene classification. Since the training of a CNN requires a large number of labeled samples, a generative adversarial network (GAN) for sa...
Article
Convolutional neural network (CNN)-based methods are widely used in remote sensing image scene classification and can obtain excellent performances. However, the stacked receptive fields in the CNN-based methods have limitations in modeling the long-range dependencies of local features. The vision transformer (ViT) model provides a good solution as...
Article
Hyperspectral anomaly detection is aimed at detecting observations that differ from their surroundings. To achieve this goal, low-rank models and autoencoders (AEs) have attracted a lot of attention. Although the low-rank model is self-explainable, a low-rank prior may not completely match real data. In contrast, AEs can automatically learn the dis...
Article
Full-text available
Compared with the common land surface temperature and emissivity (LST&LSE) retrieval from single- or multi-spectral thermal infrared (TIR) data, TIR hyperspectral imagery (HSI) has the advantage of obtaining accurate LST and LSE through automatic temperature and emissivity separation (TES) method. However, the existing TES algorithms have barely be...
Article
Conventional subpixel mapping (SPM) is performed based on the abundance maps obtained by spectral unmixing (SU), to interpret the mixed pixels and improve the mapping resolution for hyperspectral remote sensing imagery. However, the SU and SPM tasks are separately conducted, so that the unmixing error is propagated to the SPM, and the mapping resul...
Article
Full-text available
Large-scale crop mapping is an important task in agricultural resource monitoring, but it does usually require the ground-truth labels of all the land-cover types in the remotely sensed imagery. However, labeling each land-cover type is time-consuming and labor-intensive. One-class classification, which only needs samples of the class of interest,...
Article
Target tracking has received increased attention in the past few decades. However, most of the target tracking algorithms are based on RGB video data, and few are based on hyperspectral video data. With the development of the new "snapshot" hyperspectral sensors, hyperspectral videos can now be easily obtained. However, hyperspectral video target t...
Article
Full-text available
Emissivity information derived from thermal infrared (TIR) hyperspectral imagery has the advantages of both high spatial and spectral resolutions, which facilitate the detection and identification of the subtle spectral features of ground targets. Despite the emergence of several different TIR hyperspectral imagers, there are still no universal spe...
Article
Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using bitemporal high spatial resolution (HSR) remote sensing images can quickly and safely provide us with spatial distribution information and statistics of the damage degree to assist with humanit...
Article
Full-text available
Greenhouses have revolutionized farming all over the world. To estimate vegetable yields, greenhouse mapping using high spatial resolution (HSR) remote sensing imagery is very important. Although automatic greenhouse mapping methods have been proposed, they are often applied in limited small-scale areas (i.e. a parcel, a city, or a province). Large...
Article
Hyperspectral unmixing (HU) has been one of the hot spots in hyperspectral remote sensing research and has great potential in many applications. In recent years, the employment of the probabilistic topic model to mine latent topics in hyperspectral images has been an effective way for spectral unmixing. However, these methods fail to fully exploit...
Article
Full-text available
Oil spills have caused serious harm to the marine environment. Remote sensing technology is one of the important tools for marine environment monitoring. Synthetic aperture radar (SAR) has become an important technology for detecting marine pollution. Identifying dark spots is essential for oil spill detection based on SAR images. Dark spots' detec...
Article
Satellite video is an emerging data source for dynamic Earth observation, which provides us with a new means for large-scale moving vehicle detection and traffic monitoring. However, the ratio of the foreground to background in satellite video is severely uneven. Furthermore, due to the high altitude and spatial resolution of video satellites, the...
Article
Full-text available
Road detection based on convolutional neural networks (CNNs) has achieved remarkable performances for very high resolution (VHR) remote sensing images. However, this approach relies on massive annotated samples, and the problem of limited generalization for unseen images still remains. The manual pixel-level labeling process is also extremely time-...
Article
Scene classification is a means to interpret high-resolution remote sensing (HRS) imagery, to obtain the high-level semantic information, which can provide a reliable reference for urban planning and monitoring. The traditional scene classification methods based on HRS imagery take uniform grid cells as the scene units, thereby missing the geograph...
Article
With the continuous development of high-spatial-resolution ground observation technology, it is now becoming possible to obtain more and more high-resolution images, which provide us with the possibility to understand remote sensing images at the semantic level. Compared with traditional pixel- and object-oriented methods of change detection, scene...
Article
With the acceleration of urbanization, it is essential to carry out change detection (CD) and obtain surface change information in urban areas. In the early stages, the spectral information of remote sensing images was used as a change index to capture the spectral and texture changes of ground objects in a two-dimensional plane. However, due to th...
Preprint
Full-text available
For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images. However, it is very expensive and time-consuming to pairwise label large-scale bitemporal HSR remote sensing images. In this paper, we propose single-temporal supervised learning (S...
Article
Sub-pixel mapping (SPM) has been widely adopted to alleviate the mixed pixel problem in hyperspectral image, as an extension of spectral unmixing (SU), providing a way to observe the spatial location of the endmember within mixed pixel. However, most of the SPM methods are unmixing-then-mapping (UTM), i.e., SPM process relies on the abundance image...
Article
Hyperspectral anomaly detection, which is aimed at locating anomaly, has received widespread attention. In this article, a new anomaly detector, named local spatial constraint and total variation (LSC-TV), is proposed for hyperspectral imagery. In anomaly detection methods based on low-rank representation, background pixels are usually considered t...
Article
Full-text available
Thermal infrared (TIR) remote-sensing imagery can allow objects to be imaged clearly at night through the long-wave infrared, so that the fusion of thermal infrared and visible (VIS) imagery is a way to improve the remote-sensing interpretation ability. However, due to the large radiation difference between the two kinds of images, it is very diffi...
Article
Full-text available
Reliable urban road vector maps are essential for urban analysis because the spatial distribution of road networks reflects urban development under the combined effects of nature and socio-economics. Diverse very high resolution (VHR) remote sensing images are now available, enabling explicit extraction of urban road vector maps over wide areas. Ur...
Article
Full-text available
The small object semantic segmentation task is aimed at automatically extracting key objects from high-resolution remote sensing (HRS) imagery. Compared with the large-scale coverage areas for remote sensing imagery, the key objects such as cars, ships, etc. in HRS imagery often contain only a few pixels. In this paper, to tackle this problem, the...
Article
Crop mapping is essential for agricultural management, economic development planning, and ecological conservation. Remote sensing with a large field of view provides us with a potential technique for large-scale crop mapping. However, most of the previous studies have focused on multi-temporal crop mapping, requiring multiple imaging over a period...
Preprint
Full-text available
Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success. However, the deep neural network model has a large parameter space and requires a large number of labeled data. Deep learning methods for HSI classification usually follow a patchwise learning framework. Recently, a fast pa...
Article
Full-text available
Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success. However, the deep neural network model has a large parameter space and requires a large number of labeled data. Deep learning methods for HSI classification usually follow a patchwise learning framework. Recently, a fast pa...
Article
The complementarity of synthetic aperture radar (SAR) and optical images allows remote sensing observations to "see" unprecedented discoveries. Image matching plays a fundamental role in the fusion and application of SAR and optical images. However, both the geometric imaging pattern and the physical radiation mechanism of these two sensors are sig...
Article
Due to the unavoidable influence of sparse and Gaussian noise during the process of data acquisition, the quality of hyperspectral images (HSIs) is degraded and their applications are greatly limited. It is therefore necessary to restore clean HSIs. In the traditional methods, low-rank and sparse matrix decomposition methods are usually applied to...
Article
Full-text available
Road extraction is to automatically label the pixels of roads in satellite imagery with specific semantic categories based on the extraction of the topographical meaningful features. For governments, timely and accurate road mapping is crucial to plan infrastructure development and mobilize relief around the world. Recent advances in deep learning...