Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Yujun, Quan"'
In this study, we present a large-scale earth surface reconstruction pipeline for linear-array charge-coupled device (CCD) satellite imagery. While mainstream satellite image-based reconstruction approaches perform exceptionally well, the rational fu
Externí odkaz:
http://arxiv.org/abs/2310.20117
Stereo matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, such as KITTI and Scene Flow. UAVs (Unmanned Aerial Vehicles) are commonly utilized for surface
Externí odkaz:
http://arxiv.org/abs/2302.10082
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 16577-16591 (2024)
Building extraction is a challenging task in remote sensing images (RSI) interpretation. Fusing RSI from different sources, such as high-resolution RSI and LiDAR, is a common strategy to improve the building extraction accuracy. However, the acquisit
Externí odkaz:
https://doaj.org/article/c442f302645643bebf75c85a63b6bba0
Autor:
Xiaoqiong Wei, You Lu, Liangguang Leo Lin, Chengxin Zhang, Xinxin Chen, Siwen Wang, Shuangcheng Alivia Wu, Zexin Jason Li, Yujun Quan, Shengyi Sun, Ling Qi
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-16 (2024)
Abstract Endoplasmic reticulum-associated degradation (ERAD) plays indispensable roles in many physiological processes; however, the nature of endogenous substrates remains largely elusive. Here we report a proteomics strategy based on the intrinsic
Externí odkaz:
https://doaj.org/article/9af861f4006a4bc58054198a6fb20fa2
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 2942-2953 (2023)
Stereo matching is a fundamental task in 3-D scene reconstruction. Recently, deep learning-based methods have proven effective on some benchmark datasets, such as KITTI and SceneFlow. Unmanned aerial vehicles (UAVs) are commonly used for surface obse
Externí odkaz:
https://doaj.org/article/ef0db842c3234ac3bd09a30c27532743
Publikováno v:
Remote Sensing, Vol 16, Iss 2, p 431 (2024)
Synthetic aperture radar (SAR) and optical images provide highly complementary ground information. The fusion of SAR and optical data can significantly enhance semantic segmentation inference results. However, the fusion methods for multimodal data r
Externí odkaz:
https://doaj.org/article/bd2c4a33ceea4eea9cbbe363b4f38c70
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 120, Iss , Pp 103346- (2023)
Building change detection (CD) using remote sensing images plays a vital role in urban development, and deep learning models attracted attention for their potential to accomplish CD tasks automatically. However, most methods are still facing challeng
Externí odkaz:
https://doaj.org/article/ff4d0de5440e4865a5bfacb49ffe64dd
Autor:
Anzhu Yu, Yujun Quan, Ru Yu, Wenyue Guo, Xin Wang, Danyang Hong, Haodi Zhang, Junming Chen, Qingfeng Hu, Peipei He
Publikováno v:
Remote Sensing, Vol 15, Iss 20, p 4987 (2023)
The annotations used during the training process are crucial for the inference results of remote sensing images (RSIs) based on a deep learning framework. Unlabeled RSIs can be obtained relatively easily. However, pixel-level annotation is a process
Externí odkaz:
https://doaj.org/article/7fa89b47e65c40289b8d3f23157548ff
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 6559-6570 (2022)
Building extraction from aerial and satellite remote sensing images is a basic component of social development. Compared to traditional feature extraction strategies, deep convolutional neural networks (CNNs) have the advantage of extracting deep hig
Externí odkaz:
https://doaj.org/article/6eee730637724c458e5aa146089054c7
Publikováno v:
Applied Sciences, Vol 13, Iss 2, p 1037 (2023)
Building extraction (BE) and change detection (CD) from remote sensing (RS) imagery are significant yet highly challenging tasks with substantial application potential in urban management. Learning representative multi-scale features from RS images i
Externí odkaz:
https://doaj.org/article/1fe04de7079e43de880f1ab634f2e6b8