Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Zhengzhou Li"'
Publikováno v:
Remote Sensing, Vol 16, Iss 7, p 1255 (2024)
The long-distance ship target turns into a small spot in an infrared image, which has the characteristics of small size, weak intensity, limited texture information, and is easily affected by noise. Moreover, the presence of heavy sea clutter, includ
Externí odkaz:
https://doaj.org/article/39f2868e39cb4fc394cff186d09d739e
Multiscale Context-Aware Feature Fusion Network for Land-Cover Classification of Urban Scene Imagery
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 8475-8491 (2023)
Recently, several land-cover classification models have achieved great success in terms of both accuracy and computational performance. However, it remains challenging due to interclass similarities, intraclass variations, scale-related inaccuracies,
Externí odkaz:
https://doaj.org/article/df74811d10424ca4a53137cc572f1241
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 7687-7699 (2022)
Small target detection is an arduous mission in the infrared search and tracking system, especially when the target signal is disturbed by high-intensity background clutters. In view of this situation, this article presents a robust target detection
Externí odkaz:
https://doaj.org/article/0c9aa0d12dc94db8920812f11bddc567
Publikováno v:
Remote Sensing, Vol 15, Iss 8, p 2106 (2023)
It is a big challenge to quickly and accurately recognize targets in a complex background. The mutual constraints between a wide field of vision (FOV) and high resolution affect the optical tracking and imaging ability in a wide area. In nature, rapt
Externí odkaz:
https://doaj.org/article/8ea3891397b149b79379b2b136298d21
Publikováno v:
Remote Sensing, Vol 15, Iss 5, p 1258 (2023)
It is a big challenge to detect and track small infrared marine targets in non-stationary and time-varying sea clutter because the signal is too strong to be estimated. Based on the phenomenon that sea clutter spreads not only in the temporal domain
Externí odkaz:
https://doaj.org/article/eac2ba600b12448a8e7d765f6c28a7e9
Publikováno v:
IEEE Access, Vol 8, Pp 44798-44820 (2020)
The existing infrared (IR) ship target segmentation methods may suffer serious performance degradation in the situation of diverse background clutters and ship targets. To cope with this problem, a novel ship target segmentation method is proposed in
Externí odkaz:
https://doaj.org/article/a2f7f16502fd464a8571c506ae7bff8d
Publikováno v:
IEEE Access, Vol 7, Pp 144691-144709 (2019)
Object detection in high-resolution remote sensing images has been attracted increasing attention in recent years owing to the successful applications of civil and military. However, there are many critical challenges deciding the performance of obje
Externí odkaz:
https://doaj.org/article/4a78eac07b4d4e7f8a126682a64109f2
Publikováno v:
Remote Sensing, Vol 14, Iss 9, p 2233 (2022)
The long-time coherent integration can effectively improve the detection ability of radar targets. However, this strategy usually shows poor effect in resisting the sea clutter, which produces difficulties for accurate estimation of sea clutter chara
Externí odkaz:
https://doaj.org/article/8e35ca75a62341518a444e824c423a7c
Publikováno v:
Remote Sensing, Vol 14, Iss 7, p 1683 (2022)
The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple targets in a single scenario. However, for GM-PHD, unknown target behavior, e.g., target birth or target intersection, produces difficulties in terms o
Externí odkaz:
https://doaj.org/article/61c19b6026e343ab916f62dafee89d6c
Publikováno v:
IEEE Access, Vol 6, Pp 45961-45973 (2018)
With the increasing use of distributed generations and distribution automation technologies in smart grid, the distribution system operator can alleviate the loss-of-load and improve the reliability of supply by forming intentional islands when emerg
Externí odkaz:
https://doaj.org/article/0a5eb54ce99649bc9cb30fba0c154d5e