Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Xuming Ge"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11069-11085 (2024)
Reconstruction and expansion, as well as asset management, of highways necessitate the development of a current and highly precise 3D pavement model. Current inverse modeling methods with point clouds are laborious, time-consuming, and limited in pre
Externí odkaz:
https://doaj.org/article/ba1792a448c142d682fc7304e7498634
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 5077-5088 (2023)
Point-based and voxel-based methods can learn the local features of point clouds. However, although point-based methods are geometrically precise, the discrete nature of point clouds negatively affects feature learning performance. Moreover, although
Externí odkaz:
https://doaj.org/article/055f8b19670e451d8c795b4c9a62b82a
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 117, Iss , Pp 103209- (2023)
Many methods have been proposed to extract line segment (LS) correspondences for images with viewpoint variations. However, the matching performance on images with viewpoint variations is still limited due to the influence of discontinuous parallax a
Externí odkaz:
https://doaj.org/article/0ac3d896da894d7dbae89f71a41959f5
Publikováno v:
Remote Sensing, Vol 15, Iss 22, p 5383 (2023)
Recent studies have introduced transformer modules into convolutional neural networks (CNNs) to solve the inherent limitations of CNNs in global modeling and have achieved impressive performance. However, some challenges have yet to be addressed: fir
Externí odkaz:
https://doaj.org/article/3ef2302dd2394fad9145d034c8064be0
Publikováno v:
Remote Sensing, Vol 15, Iss 17, p 4311 (2023)
Image dense matching plays a crucial role in the reconstruction of three-dimensional models of buildings. However, large variations in target heights and serious occlusion lead to obvious mismatches in areas with discontinuous depths, such as buildin
Externí odkaz:
https://doaj.org/article/cfde2f6568d24b199d6ea768ac3bbf34
Autor:
Li Chen, Yulin Ding, Saeid Pirasteh, Han Hu, Qing Zhu, Xuming Ge, Haowei Zeng, Haojia Yu, Qisen Shang, Yongfei Song
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 110, Iss , Pp 102807- (2022)
Predicting a landslide susceptibility map (LSM) is essential for risk recognition and disaster prevention. Despite the successful application of data-driven approaches for LSM prediction, most methods generally apply a single global model to predict
Externí odkaz:
https://doaj.org/article/52d789b8f6a546e7a545d8030db7a14b
Publikováno v:
Remote Sensing, Vol 15, Iss 4, p 1085 (2023)
Landslides are geological disasters that can cause serious severe damage to properties and lead to the loss of human lives. The application of deep learning technology to optical remote sensing images can help in the detection of landslide areas. Tra
Externí odkaz:
https://doaj.org/article/243d0ca5e5c548f9961132a6a8373a8e
Autor:
Shuming Si, Han Hu, Yulin Ding, Xuekun Yuan, Ying Jiang, Yigao Jin, Xuming Ge, Yeting Zhang, Jie Chen, Xiaocui Guo
Publikováno v:
Remote Sensing, Vol 15, Iss 1, p 269 (2023)
Compared with the existing modes of LiDAR, single-photon LiDAR (SPL) can acquire terrain data more efficiently. However, influenced by the photon-sensitive detectors, the collected point cloud data contain a large number of noisy points. Most of the
Externí odkaz:
https://doaj.org/article/7ca05f542df54daa83d611b20a25a7e3
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 11, Iss 4, p 247 (2022)
This paper proposes an efficient approach for the plane segmentation of indoor and corridor scenes. Specifically, the proposed method first uses voxels to pre-segment the scene and establishes the topological relationship between neighboring voxels.
Externí odkaz:
https://doaj.org/article/21e289fe7c0947cba2572d8c98f385d4
Publikováno v:
Remote Sensing, Vol 14, Iss 9, p 2024 (2022)
This work proposes the use of a robust geometrical segmentation algorithm to detect inherent shapes from dense point clouds. The points are first divided into voxels based on their connectivity and normal consistency. Then, the voxels are classified
Externí odkaz:
https://doaj.org/article/c05d9855ac83415cbf260a3230a31638