Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Haijiang Sun"'
Publikováno v:
IEEE Access, Vol 12, Pp 12308-12322 (2024)
In this paper, we propose an unstructured road-free space detection method that integrates distance imaging information in the Transformer framework. The proposed network is FNS-Swin, which innovatively supplements features through data fusion to red
Externí odkaz:
https://doaj.org/article/7c4c0b69b041465cbd90c7e17f884b2f
Publikováno v:
Sensors, Vol 24, Iss 22, p 7386 (2024)
For binocular stereo matching techniques, the most advanced method currently is using an iterative structure based on GRUs. Methods in this class have shown high performance on both high-resolution images and standard benchmarks. However, simply repl
Externí odkaz:
https://doaj.org/article/b9988ca399a94a9a989abce378acdf9f
Autor:
Hongyu Wu, Guanzhou Chen, Yang Bai, Ying Peng, Qianqian Ba, Shuai Huang, Xing Zhong, Haijiang Sun, Lei Zhang, Fuyu Feng
Publikováno v:
Remote Sensing, Vol 16, Iss 20, p 3893 (2024)
On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satel
Externí odkaz:
https://doaj.org/article/43cadafaae61490e9181afd427e138bf
Publikováno v:
Remote Sensing, Vol 16, Iss 17, p 3347 (2024)
Multi-object tracking in satellite videos (SV-MOT) is an important task with many applications, such as traffic monitoring and disaster response. However, the widely studied multi-object tracking (MOT) approaches for general images can rarely be dire
Externí odkaz:
https://doaj.org/article/5d9f0321aeeb42d388e342b3b2201fc6
Publikováno v:
Remote Sensing, Vol 16, Iss 14, p 2645 (2024)
With the continuous development of space remote sensing technology, the spatial resolution of visible remote sensing images has been continuously improved, which has promoted the progress of remote sensing target detection. However, due to the limita
Externí odkaz:
https://doaj.org/article/27bf190b6bbf4f9f981a74e30bac6e03
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2327 (2024)
Since Neural Radiation Field (NeRF) was first proposed, a large number of studies dedicated to them have emerged. These fields achieved very good results in their respective contexts, but they are not sufficiently practical for our project. If we wan
Externí odkaz:
https://doaj.org/article/84904404b7e24e79957a3bcbb6d7b8c0
Publikováno v:
Sensors, Vol 24, Iss 7, p 2064 (2024)
It is important to achieve the 3D reconstruction of UAV remote sensing images in deep learning-based multi-view stereo (MVS) vision. The lack of obvious texture features and detailed edges in UAV remote sensing images leads to inaccurate feature poin
Externí odkaz:
https://doaj.org/article/20826cbe41eb417fa4dcddbf72d2f8a0
Publikováno v:
Sensors, Vol 23, Iss 20, p 8424 (2023)
In the context of non-uniformity correction (NUC) within infrared imaging systems, current methods frequently concentrate solely on high-frequency stripe non-uniformity noise, neglecting the impact of global low-frequency non-uniformity on image qual
Externí odkaz:
https://doaj.org/article/7bb15f356f2c4f518fa5d18bcbf69d15
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4497 (2023)
Small object detection in remote sensing enables the identification and analysis of unapparent but important information, playing a crucial role in various ground monitoring tasks. Due to the small size, the available feature information contained in
Externí odkaz:
https://doaj.org/article/3c1db7cb4ceb4440bde26f261bbec259
Autor:
Xiangdong Xu, Jiarong Wang, Ming Zhu, Haijiang Sun, Zhenyuan Wu, Yao Wang, Shenyi Cao, Sanzai Liu
Publikováno v:
Remote Sensing, Vol 15, Iss 15, p 3736 (2023)
In recent years, the development of deep learning has brought great convenience to the work of target detection, semantic segmentation, and object recognition. In the field of infrared weak small target detection (e.g., surveillance and reconnaissanc
Externí odkaz:
https://doaj.org/article/2bbddf56167643f2b425bd932371df9f