Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Xikai Fu"'
Publikováno v:
Remote Sensing, Vol 16, Iss 23, p 4427 (2024)
High-performance neural networks for synthetic aperture radar (SAR) automatic target recognition (ATR) often encounter the challenge of data scarcity. The lack of sufficient labeled SAR image datasets leads to the consideration of using simulated dat
Externí odkaz:
https://doaj.org/article/d30365b393194d8c8b950f86cc3132ed
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 5561-5577 (2023)
In a high-resolution and wide-swath synthetic aperture radar (SAR) platform, the along-track position error reduces the accuracy of the phase error estimation, which will lead to the failure of aliased signal reconstruction. However, classical subspa
Externí odkaz:
https://doaj.org/article/31713a19828c4b29b7980213ebfccbd9
Publikováno v:
Remote Sensing, Vol 15, Iss 1, p 129 (2022)
Radargrammetry is a widely used methodology to generate the large-scale Digital Surface Model (DSM). Stereo matching is the most challenging step in radargrammetry due to the significant geometric differences and the inherent speckle noise. The speck
Externí odkaz:
https://doaj.org/article/48aa65af06684e9bbd380a685846991a
Publikováno v:
Remote Sensing, Vol 13, Iss 18, p 3625 (2021)
The multichannel synthetic aperture radar (SAR) system can effectively overcome the fundamental limitation between high-resolution and wide-swath. However, the unavoidable channel errors will result in a mismatch of the reconstruction filter and fals
Externí odkaz:
https://doaj.org/article/f3c89df3e5bc45d3a1ad6cfbd1a82048
Publikováno v:
Remote Sensing, Vol 13, Iss 16, p 3149 (2021)
Road detection from images has emerged as an important way to obtain road information, thereby gaining much attention in recent years. However, most existing methods only focus on extracting road information from single temporal intensity images, whi
Externí odkaz:
https://doaj.org/article/d47e49e0a2b4483d8e0407586efdaaec
Publikováno v:
Remote Sensing, Vol 13, Iss 3, p 471 (2021)
Building change detection using remote sensing images is essential for various applications such as urban management and marketing planning. However, most change detection approaches can only detect the intensity or type of change. The aim of this st
Externí odkaz:
https://doaj.org/article/c1e9cf468cff4f64b3f29a970b98dc1d
Publikováno v:
Remote Sensing, Vol 11, Iss 9, p 1033 (2019)
This paper investigates the potential of the time-frequency optimization on the basis of the sublook decomposition for forest height estimation. The optimization is deemed to be capable of extracting a relatively accurate volume contribution when P-b
Externí odkaz:
https://doaj.org/article/a7dda4e45c4542029e7edb68db45c792
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 19:1-5
This letter proposes a machine learning inversion scheme for P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR), which can achieve the single-baseline random volume over ground (RVoG) model inversion without the assumption of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a54cd40d0e2c115aef01d79cd6bb548f
https://doi.org/10.1109/lgrs.2021.3093484
https://doi.org/10.1109/lgrs.2021.3093484
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-17
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e66c07663e495b9ff932cd94c6481035
https://doi.org/10.1109/tgrs.2021.3138188
https://doi.org/10.1109/tgrs.2021.3138188
Publikováno v:
Remote Sensing, Vol 13, Iss 3625, p 3625 (2021)
Remote Sensing; Volume 13; Issue 18; Pages: 3625
Remote Sensing; Volume 13; Issue 18; Pages: 3625
The multichannel synthetic aperture radar (SAR) system can effectively overcome the fundamental limitation between high-resolution and wide-swath. However, the unavoidable channel errors will result in a mismatch of the reconstruction filter and fals
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c53101b14f3be94c335f4a95f5b03b9
https://www.mdpi.com/2072-4292/13/18/3625
https://www.mdpi.com/2072-4292/13/18/3625