Wenhui Diao - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Wenhui Diao

Also published under: Wen-Hui Diao

Publications

88

Citations

1,497

Publications by Year

20162025

Co-Authors:

Jiahao BaoHanbo BiZhiying CaoHao ChangZhonghan Chang

Show All Co-Authors (115)

Wenhui Diao

Also published under: Wen-Hui Diao

Affiliation

Aerospace Information Research Institute

Chinese Academy of Sciences

Beijing, China

Key Laboratory of Network Information System Technology (NIST)

Aerospace Information Research Institute

Chinese Academy of Sciences, Beijing, China

Publication Topics

Object Detection,
Remote Sensing,
Remote Sensing Images,
Synthetic Aperture Radar,
Feature Maps,
Synthetic Aperture Radar Images,
Bounding Box,
Semantic Segmentation,
Semantic Information,
Training Set,
Feature Fusion,
Intersection Over Union

Biography

Wenhui Diao (Member, IEEE) received the B.Sc. degree from Xidian University, Xi’an, China, in 2011, and the M.Sc. and Ph.D. degrees from the Institute of Electronics, Chinese Academy of Sciences, Beijing, China, in 2016, all in electronic information engineering.,He is currently an Associate Professor with the Aerospace Information Research Institute, Chinese Academy of Sciences. His research interests include computer vision and remote sensing image analysis.(Based on document published on 24 August 2021).

Publications

88

Citations

1,497

Publications by Year

20162025

Co-Authors:

Jiahao Bao
Hanbo Bi
Zhiying Cao
Hao Chang
Zhonghan Chang

Show All Co-Authors (115)

Author's Published Works

Search History

Showing 1-15 of 15 results

Conferences (12)

Journals (3)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Tao Xu(15)
Xinxin Li(5)
Pengcheng Xu(5)
Wenhui Diao(4)
Haitao Yu(4)
Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China(3)
Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China(3)
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China(3)
State Key Lab of Transducer Technology, Chinese Academy of Sciences, Shanghai, CHINA(2)
University of Chinese Academy of Sciences, Beijing, China(2)
2016 IEEE International Conference on Information and Automation (ICIA)(3)
IEEE Transactions on Geoscience and Remote Sensing(3)
2017 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS)(2)
2017 IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS)(2)
2012 IEEE International Conference on Multimedia and Expo(1)
IEEE(15)
Ningbo, China(3)
Kaohsiung, Taiwan(2)
Las Vegas, NV, USA(2)
Melbourne, VIC, Australia(2)
Kuala Lumpur, Malaysia(1)
Metal–organic Frameworks(4)
Cantilever(3)
Deionized Water(3)
Feature Maps(3)
Stock Solution(3)

Select All on Page

Sort By

Results

Soft Weighted Ordinal Classification for Monocular Height Estimation in Remote Sensing Image

Yingchao Feng;Xian Sun;Wenhui Diao;Jihao Li;Tao Xu;Xin Gao;Kun Fu

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Year: 2022 | Conference Paper |

Cited by: Papers (2)

HTML

Estimating height information from a single remote sensing image is a critical component for 3D perception. Recent methods formulate it as a dense height prediction task based on regression loss functions. However, the regression accuracy is limited by the infinite continuous solution space. In this paper, we propose the soft weighted ordinal (SWO) classification loss for height prediction model t...Show More

Soft Weighted Ordinal Classification for Monocular Height Estimation in Remote Sensing Image

Yingchao Feng;Xian Sun;Wenhui Diao;Jihao Li;Tao Xu;Xin Gao;Kun Fu

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Year: 2022 | Conference Paper |

BSNet: Dynamic Hybrid Gradient Convolution Based Boundary-Sensitive Network for Remote Sensing Image Segmentation

Jianlong Hou;Zhi Guo;Youming Wu;Wenhui Diao;Tao Xu

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (23)

HTML

Boundary information is essential for the semantic segmentation of remote sensing images. However, most existing methods were designed to establish strong contextual information while losing detailed information, making it challenging to extract and recover boundaries accurately. In this article, a boundary-sensitive network (BSNet) is proposed to address this problem via dynamic hybrid gradient c...Show More

BSNet: Dynamic Hybrid Gradient Convolution Based Boundary-Sensitive Network for Remote Sensing Image Segmentation

Jianlong Hou;Zhi Guo;Youming Wu;Wenhui Diao;Tao Xu

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

FADA: Feature Aligned Domain Adaptive Object Detection in Remote Sensing Imagery

Tao Xu;Xian Sun;Wenhui Diao;Liangjin Zhao;Kun Fu;Hongqi Wang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (19)

HTML

Deep learning-based object detectors have been widely adopted in the field of remote sensing imagery interpretation. These detectors heavily depend on the expensive large-scale labeled datasets, while the scarce remote sensing datasets limit the performance. The domain adaptive object detection can alleviate this problem. However, it struggles with the confusing feature’s alignment, damaging the d...Show More

FADA: Feature Aligned Domain Adaptive Object Detection in Remote Sensing Imagery

Tao Xu;Xian Sun;Wenhui Diao;Liangjin Zhao;Kun Fu;Hongqi Wang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

ASSD: Feature Aligned Single-Shot Detection for Multiscale Objects in Aerial Imagery

Tao Xu;Xian Sun;Wenhui Diao;Liangjin Zhao;Kun Fu;Hongqi Wang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (44)

HTML

Object detection is a fundamental part of the interpretation of remote sensing imagery. The one-stage object detector has been adopted into this field because of its high computational efficiency. However, this detector suffers from the misalignment among predefined anchor, object, and feature extracted by standard convolution kernel both in spatial and scale. It limits the further improvement of ...Show More

ASSD: Feature Aligned Single-Shot Detection for Multiscale Objects in Aerial Imagery

Tao Xu;Xian Sun;Wenhui Diao;Liangjin Zhao;Kun Fu;Hongqi Wang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Ni2(dobdc) MOF (metal-organic framework) nanocrystals for ultra-sensitive detection of ppb-level co with resonant-cantilever

Yangqing Lv;Tao Xu;Pengcheng Xu;Haitao Yu;Xinxin Li

2017 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS)

Year: 2017 | Conference Paper |

HTML

MOF (metal-organic framework) material of Ni2(dobdc) (where dobdc = 1,4-dioxido-2,5-benzenedicarboxylate) nanocrystals is, for the first time, explored as resonant-gravimetric sensing material for CO detection. Due to the ultra-high surface-area and the active metal center-sites, the MOF of Ni2(dobdc) exhibits adsorbing induced high-performance sensing to CO gas. By loading the MOF material onto r...Show More

Ni2(dobdc) MOF (metal-organic framework) nanocrystals for ultra-sensitive detection of ppb-level co with resonant-cantilever

Yangqing Lv;Tao Xu;Pengcheng Xu;Haitao Yu;Xinxin Li

2017 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS)

Year: 2017 | Conference Paper |

Multi-dimensional multi-level sensing nanostructure for high-performance detection to trace-level dopamine molecules

Pengcheng Xu;Manyi Liu;Xinxin Li;Tao Xu;Yuan Zhang

2017 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS)

Year: 2017 | Conference Paper |

Cited by: Papers (2)

HTML

A novel multi-dimensional nanostructure constructed with three types of nano-building-block is used as electrochemical sensing material to detect trace dopamine which plays as neurotransmitter in body-fluids. For the multi-dimensional multi-level sensing nanostructure construction, nanosheet of graphene oxide (GO) is herein served as nano-substrate. By using hydrothermal method, ZnO nanowires are ...Show More

Multi-dimensional multi-level sensing nanostructure for high-performance detection to trace-level dopamine molecules

Pengcheng Xu;Manyi Liu;Xinxin Li;Tao Xu;Yuan Zhang

2017 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS)

Year: 2017 | Conference Paper |

MOF nano-crystals of ZIF-8 identified as ambient NO2 gas absorbent by using resonant micro-cantilever experiment

Pengcheng Xu;Haitao Yu;Tao Xu;Xinxin Li

2017 IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS)

Year: 2017 | Conference Paper |

HTML

Based on resonant micro-cantilever measurements, the metal-organic framework (MOF) nano-crystal material of ZIF-8 is, for the first time, identified as an excellent absorbent to capture/fix the automobile exhaust of NO2 in real atmospheric air. By using MEMS resonant cantilever as material evaluation tool, the adsorbing properties of ZIF-8 to NO2 gas have been experimentally examined not only for ...Show More

MOF nano-crystals of ZIF-8 identified as ambient NO2 gas absorbent by using resonant micro-cantilever experiment

Pengcheng Xu;Haitao Yu;Tao Xu;Xinxin Li

2017 IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS)

Year: 2017 | Conference Paper |

MOF (metal-organic framework) nanomaterial for 400ppb-concentration detectable xylene gas sensors

Pengcheng Xu;Tao Xu;Haitao Yu;Dan Zheng;Xinxin Li

2017 IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS)

Year: 2017 | Conference Paper |

Cited by: Papers (3)

HTML

In this paper, metal-organic framework (MOF) is explored as xylene sensing-material for resonant-cantilever gas sensor. Sensing experiment with the MOF nano-material of HKUST-1 demonstrates that 400 ppb xylene can be detected. This limit of detection (LOD) has been lower than the human olfactory threshold. The sensing mechanism is identified as the interaction between MOF and xylene. The specifici...Show More

MOF (metal-organic framework) nanomaterial for 400ppb-concentration detectable xylene gas sensors

Pengcheng Xu;Tao Xu;Haitao Yu;Dan Zheng;Xinxin Li

2017 IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS)

Year: 2017 | Conference Paper |

ProDigger: Towards Robust Automatic Network Protocol Fingerprint Learning via Byte Embedding

Yafei Sang;Shicong Li;Yongzheng Zhang;Tao Xu

2016 IEEE Trustcom/BigDataSE/ISPA

Year: 2016 | Conference Paper |

Cited by: Papers (3)

HTML

As a prerequisite technique, Deep Packet Inspection (DPI) plays a major role to contemporary network security and management. The key of DPI is a repository of protocol fingerprints. However, inferring and maintaining up-to-date fingerprints for various and new protocols is very difficult in order to adapt them to the continuous evolution of the protocols. In this paper, we propose ProDigger, a ro...Show More

ProDigger: Towards Robust Automatic Network Protocol Fingerprint Learning via Byte Embedding

Yafei Sang;Shicong Li;Yongzheng Zhang;Tao Xu

2016 IEEE Trustcom/BigDataSE/ISPA

Year: 2016 | Conference Paper |

A data processing method for chemical industry based on sensor data and process monitoring

Haodong Chen;Qing He;Guan Guan;Xian Wu;Tao Xu;Yanghong Zhang;Haibing Wang

2016 IEEE International Conference on Information and Automation (ICIA)

Year: 2016 | Conference Paper |

Cited by: Papers (2)

HTML

In order to recognizing the feature of abnormality in advance and fix the possible dirty data simultaneously, this article proposed a method to construct a proper mode for a production process producing specific product, a method to estimate the stability of a linear process and a group of data cleaning rule. Both the data processing procedure and algorithm are given detailedly with some samples a...Show More

A data processing method for chemical industry based on sensor data and process monitoring

Haodong Chen;Qing He;Guan Guan;Xian Wu;Tao Xu;Yanghong Zhang;Haibing Wang

2016 IEEE International Conference on Information and Automation (ICIA)

Year: 2016 | Conference Paper |

An improved TLD target tracking algorithm

Tao Xu;Chaobing Huang;Qing He;Guan Guan;Yanghong Zhang

2016 IEEE International Conference on Information and Automation (ICIA)

Year: 2016 | Conference Paper |

Cited by: Papers (6)

HTML

Target tracking has always been a hot research topic in the field of computer vision. Tracking-Learning-Detection (TLD) is a new algorithm for online learning tracking proposed by Zdenek Kalal. In the algorithm, the computation consuming of detection module is relatively large. To solve this problem and improve the algorithm, we proposed an online learning method to adaptively update the threshold...Show More

An improved TLD target tracking algorithm

Tao Xu;Chaobing Huang;Qing He;Guan Guan;Yanghong Zhang

2016 IEEE International Conference on Information and Automation (ICIA)

Year: 2016 | Conference Paper |

Background subtraction based on pixel clustering

Yanghong Zhang;Qing He;Haibin Wang;Guan Guan;Tao Xu;Haodong Chen

2016 IEEE International Conference on Information and Automation (ICIA)

Year: 2016 | Conference Paper |

Cited by: Papers (2)

HTML

Nowadays motion detection in video sequences is the basis for the high-level analytics and applications. After ViBe is proposed, the current mainstream detection algorithm is based on pixel level to construct the background model for the foreground / background segmentation. Influenced by this kind of algorithm, this paper proposes a spatiotemporal-based algorithm to construct an approximate preci...Show More

Background subtraction based on pixel clustering

Yanghong Zhang;Qing He;Haibin Wang;Guan Guan;Tao Xu;Haodong Chen

2016 IEEE International Conference on Information and Automation (ICIA)

Year: 2016 | Conference Paper |

Graphene-oxide (GO) nano-sheets: Lateral and vertical size-effects on chemical-gas sensitivity enhancement

Pengcheng Xu;Tao Xu;Haitao Yu;Xinxin Li

2016 IEEE 29th International Conference on Micro Electro Mechanical Systems (MEMS)

Year: 2016 | Conference Paper |

Cited by: Papers (3)

HTML

The paper reports a size-effect of graphene-oxide (GO) nano-sheets on sensitivity of chemical-gas detection. By using resonant-cantilevers to measure molecule-adsorbing induced mass, the size-effect is classified as in-plane dimension lateral-effect and vertical thickness-effect. Based on detection of both reducing-gas of NH3 and oxidizing-gas of NO2, this research clearly reveals that, lateral-di...Show More

Graphene-oxide (GO) nano-sheets: Lateral and vertical size-effects on chemical-gas sensitivity enhancement

Pengcheng Xu;Tao Xu;Haitao Yu;Xinxin Li

2016 IEEE 29th International Conference on Micro Electro Mechanical Systems (MEMS)

Year: 2016 | Conference Paper |

A Fast and Robust Pedestrian Detection Framework Based on Static and Dynamic Information

Tao Xu;Hong Liu;Yueliang Qian;Zhe Wang

2012 IEEE International Conference on Multimedia and Expo

Year: 2012 | Conference Paper |

Cited by: Papers (5)

HTML

With the powerful development of pedestrian detection technique based on sliding-window and machine-learning, detection-based tracking systems have become increasingly popular. Most of these systems rely on existing static pedestrian detectors only despite the obvious potential motion information for people detection. This paper proposes a novel pedestrian detection framework fusing static and dyn...Show More

A Fast and Robust Pedestrian Detection Framework Based on Static and Dynamic Information

Tao Xu;Hong Liu;Yueliang Qian;Zhe Wang

2012 IEEE International Conference on Multimedia and Expo

Year: 2012 | Conference Paper |

Crowd Density Estimation Based on Local Binary Pattern Co-Occurrence Matrix

Zhe Wang;Hong Liu;Yueliang Qian;Tao Xu

2012 IEEE International Conference on Multimedia and Expo Workshops

Year: 2012 | Conference Paper |

Cited by: Papers (30) | Patents (1)

HTML

Crowd density estimation is important for intelligent video surveillance. Many methods based on texture features have been proposed to solve this problem. Most of the existing algorithms only estimate crowd density on the whole image while ignore crowd density in local region. In this paper, we propose a novel texture descriptor based on Local Binary Pattern (LBP) Co-occurrence Matrix (LBPCM) for ...Show More

Crowd Density Estimation Based on Local Binary Pattern Co-Occurrence Matrix

Zhe Wang;Hong Liu;Yueliang Qian;Tao Xu

2012 IEEE International Conference on Multimedia and Expo Workshops

Year: 2012 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.