Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Sirui Tian"'
Publikováno v:
Sensors, Vol 24, Iss 14, p 4574 (2024)
Recently, the low-rank representation (LRR) model has been widely used in the field of remote sensing image denoising due to its excellent noise suppression capability. However, those low-rank-based methods always discard important edge details as re
Externí odkaz:
https://doaj.org/article/a8ff4069d1ce4224a9289d119b1c6b3a
Publikováno v:
Remote Sensing, Vol 15, Iss 14, p 3697 (2023)
Robust unsupervised feature learning is a critical yet tough task for synthetic aperture radar (SAR) automatic target recognition (ATR) with limited labeled data. The developing contrastive self-supervised learning (CSL) method, which learns informat
Externí odkaz:
https://doaj.org/article/f7639c6cdb1947a7bc89f2ed0e73bf88
Autor:
Lingping Cai, Haonan Qian, Linger Xing, Yang Zou, Linkang Qiu, Zihan Liu, Sirui Tian, Hongtao Li
Publikováno v:
Remote Sensing, Vol 15, Iss 13, p 3371 (2023)
Low-altitude slow-moving small (LSS) targets are defined as flying at altitudes less than 1000 m with speeds less than 55 m/s and a radar crossing-section (RCS) less than 2 m2. The detection performance of ground-based radar using the LSS target dete
Externí odkaz:
https://doaj.org/article/e93c0068c7e849db8254e16d196d041b
Autor:
Xiaolin Feng, Sirui Tian, Stanley Ebhohimhen Abhadiomhen, Zhiyong Xu, Xiangjun Shen, Jing Wang, Xinming Zhang, Wenyun Gao, Hong Zhang, Chao Wang
Publikováno v:
Remote Sensing, Vol 15, Iss 9, p 2318 (2023)
The low-rank models have gained remarkable performance in the field of remote sensing image denoising. Nonetheless, the existing low-rank-based methods view residues as noise and simply discard them. This causes denoised results to lose many importan
Externí odkaz:
https://doaj.org/article/80f7c05e3a65453190c83b6064eea9f4
Publikováno v:
Sensors, Vol 22, Iss 19, p 7278 (2022)
In radar detection, in order to make the beam have variable directivity, a Capon beamformer is usually used. Although this traditional beamformer enjoys both high resolution and good interference suppression, it usually leads to high sidelobe and is
Externí odkaz:
https://doaj.org/article/5d1569088dc348f89a29be942471d882
Publikováno v:
Remote Sensing, Vol 14, Iss 17, p 4182 (2022)
Built-up area (BA) extraction using synthetic aperture radar (SAR) data has emerged as a potential method in urban research. Currently, typical deep-learning-based BA extractors show high false-alarm rates in the layover areas and subsurface bedrock,
Externí odkaz:
https://doaj.org/article/9b70c175e2c842ae9ef07a95557f8cfa
Publikováno v:
Sensors, Vol 20, Iss 5, p 1533 (2020)
Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored dist
Externí odkaz:
https://doaj.org/article/af44b33caa164420b3b0ec22aaea27f9
Publikováno v:
Remote Sensing, Vol 8, Iss 9, p 751 (2016)
A few previous studies have illustrated the potentials of compact polarimetric Synthetic Aperture Radar (CP SAR) in ship detection. In this paper, we design a ship detection algorithm of CP SAR from the perspective of computer vision. A ship detectio
Externí odkaz:
https://doaj.org/article/aa27812c2cdf427db4e2bccabe190c44
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 14:1-24
Manifold learning is a widely used technique for dimensionality reduction as it can reveal the intrinsic geometric structure of data. However, its performance decreases drastically when data samples are contaminated by heavy noise or occlusions, whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02813446196104bc3bff7c66a2354b8f
https://doi.org/10.1145/3582698
https://doi.org/10.1145/3582698
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 61:1-17
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa0c912158ca9e84f49ccf1a6a1432f2
https://doi.org/10.1109/tgrs.2023.3261502
https://doi.org/10.1109/tgrs.2023.3261502