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Junjue Wang

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

PhD Candidate
http://junjuewang.top/

About

26
Publications
14,426
Reads
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579
Citations
Citations since 2017
26 Research Items
579 Citations
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2017201820192020202120222023050100150200250
2017201820192020202120222023050100150200250

Publications

Publications (26)
Article
Full-text available
Deep learning algorithms, especially convolutional neural networks (CNNs), have recently emerged as a dominant paradigm for high spatial resolution remote sensing (HRS) image recognition. A large amount of CNNs have already been successfully applied to various HRS recognition tasks, such as land-cover classification, scene classification, etc. Howe...
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
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...
Conference Paper
Full-text available
Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping. However, urban and rural scenes can show completely different geographical landscapes, and the inadequate generalizability of these algorithms hinders city-level or national-level mapping. Most of the existing HSR land-cover dat...
Article
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
Unsupervised domain adaptive (UDA) land-cover classification has recently gained more and more attention. UDA aimed to learn a model from the annotated source data and the unlabeled target data that can perform well on the target domain. The existing UDA frameworks based on adversarial training and self-training methods have boosted this field a lo...
Article
Full-text available
Remote sensing image scene classification methods based on deep learning have been widely studied and discussed. However, most of the network architectures are directly reliant on natural image processing methods and are fixed. A few studies have focused on automatic search mechanisms, but they cannot weigh the interpretation accuracy and the param...
Article
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...
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...
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...
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
Image captioning in remote sensing can help us understand the inner attributes of the objects and the outer relationships between different objects. However, the existing image captioning algorithms lack the ability of global representation and cannot obtain object relationships over long distances. In addition, these algorithmics generate captions...
Preprint
Full-text available
Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping. However, urban and rural scenes can show completely different geographical landscapes, and the inadequate generalizability of these algorithms hinders city-level or national-level mapping. Most of the existing HSR land-cover dat...
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-...
Conference Paper
Full-text available
With the development of remote sensing, a large amountof high-spatial-resolution (HSR) images is available, whichmakes refined land-cover mapping possible. However, thedetails of ground objects in HSR images are complex, espe-cially in edges, therefore brings new challenges in land-coverclassification. Existing deep learning method views it as asem...
Article
Full-text available
The scene classification approaches using deep learning have been the subject of much attention for remote sensing imagery. However, most deep learning networks have been constructed with a fixed architecture for natural image processing, and they are difficult to apply directly to remote sensing images, due to the more complex geometric structural...
Preprint
Full-text available
Geospatial object segmentation, as a particular semantic segmentation task, always faces with larger-scale variation, larger intra-class variance of background, and foreground-background imbalance in the high spatial resolution (HSR) remote sensing imagery. However, general semantic segmentation methods mainly focus on scale variation in the natura...
Conference Paper
Full-text available
Geospatial object segmentation, as a particular semantic segmentation task, always faces with larger-scale variation, larger intra-class variance of background, and foreground-background imbalance in the high spatial resolution (HSR) remote sensing imagery. However, general semantic segmentation methods mainly focus on scale variation in the natura...
Article
Full-text available
Detailed urban land use classification plays a highly important role in the development and management of cities and in the identification of human activities. The complexity of the urban system makes its functional zoning a challenge, which makes such maps underutilized. A detailed land use classification encompasses both the natural land features...

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