Zobrazeno 1 - 10
of 843
pro vyhledávání: '"Cloud removal"'
Publikováno v:
Geo-spatial Information Science, Vol 27, Iss 4, Pp 1326-1347 (2024)
Cloud coverage has become a significant factor affecting the availability of remote-sensing images in many applications. To mitigate the adverse impact of cloud coverage and recover ground information obscured by clouds, this paper presents a curvatu
Externí odkaz:
https://doaj.org/article/53f72b532a9e47dc93abffa8564ffb34
Autor:
Qiang Bie, Xiaojie Su
Publikováno v:
IEEE Access, Vol 12, Pp 181303-181315 (2024)
Optical remote sensing imagery is often contaminated by clouds and cloud shadows, leading to the loss of ground information and limiting the application of optical images in fields such as change detection and object classification. Therefore, the re
Externí odkaz:
https://doaj.org/article/f41b462e2dc14bf2ae225f6ce759416a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11741-11749 (2024)
Satellite-based Earth observation activities, such as urban and agricultural land monitoring, change detection, and disaster management, are constrained by adequate spatial and temporal ground observations. The presence of aerosols and clouds usually
Externí odkaz:
https://doaj.org/article/bd933af5065440328a7e1d72c47271e7
Autor:
Thomas Rosberg, Michael Schmitt
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 7748-7758 (2024)
Gaps in normalized difference vegetation index (NDVI) time series resulting from frequent cloud cover pose significant challenges in remote sensing for various applications, such as agricultural monitoring or forest disturbance detection. This study
Externí odkaz:
https://doaj.org/article/21d87d2ef6194d7596d8d787e637709d
Publikováno v:
Sensors, Vol 24, Iss 23, p 7848 (2024)
Optical remote sensing images have a wide range of applications but are often affected by cloud cover, which interferes with subsequent analysis. Therefore, cloud removal has become indispensable in remote sensing data processing. The Tibetan Plateau
Externí odkaz:
https://doaj.org/article/a44cbc14fdbd4619ace727d8be6d9cc9
Publikováno v:
Remote Sensing, Vol 16, Iss 19, p 3665 (2024)
Cloud is a serious problem that affects the quality of remote-sensing (RS) images. Existing cloud removal techniques suffer from notable limitations, such as being specific to certain data types, cloud conditions, and spatial extents, as well as requ
Externí odkaz:
https://doaj.org/article/76f9fee3903649b3b346106598ef5014
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 130, Iss , Pp 103909- (2024)
Traditional CNNs struggle with SAR and optical image fusion cloud removal due to SAR image noise, feature space differences and random cloud distribution. This often leads to blurred results with less texture information. This paper proposes a synerg
Externí odkaz:
https://doaj.org/article/755327f77ed24c029548ab53b001a89d
Publikováno v:
Remote Sensing, Vol 16, Iss 19, p 3658 (2024)
Thin clouds in Remote Sensing (RS) imagery can negatively impact subsequent applications. Current Deep Learning (DL) approaches often prioritize information recovery in cloud-covered areas but may not adequately preserve information in cloud-free reg
Externí odkaz:
https://doaj.org/article/c1aa785b37b54416989e5a7689e61bc9
Publikováno v:
Remote Sensing, Vol 16, Iss 17, p 3134 (2024)
Reconstructing cloud-covered regions in remote sensing (RS) images holds great promise for continuous ground object monitoring. A novel lightweight machine-learning method for cloud removal constrained by conditional information (SMLP-CR) is proposed
Externí odkaz:
https://doaj.org/article/5a7ef8daa5b349759533d874c6efc358
Publikováno v:
Remote Sensing, Vol 16, Iss 15, p 2867 (2024)
Cloud contamination significantly impairs optical remote sensing images (RSIs), reducing their utility for Earth observation. The traditional cloud removal techniques, often reliant on deep learning, generally aim for holistic image reconstruction, w
Externí odkaz:
https://doaj.org/article/c3f526c718c44209816e16fa5986994d