Zobrazeno 1 - 10
of 1 088
pro vyhledávání: '"Super-resolution (SR)"'
Autor:
Michal Kawulok, Pawel Kowaleczko, Maciej Ziaja, Jakub Nalepa, Daniel Kostrzewa, Daniele Latini, Davide De Santis, Giorgia Salvucci, Ilaria Petracca, Valeria La Pegna, Zoltan Bartalis, Fabio Del Frate
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 18949-18966 (2024)
The need for enhancing image spatial resolution has motivated the researchers to propose numerous super-resolution (SR) techniques, including those developed specifically for hyperspectral data. Despite significant advancements in this field attribut
Externí odkaz:
https://doaj.org/article/ee0f5d274a35448ba269f7145323d5bc
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 18502-18516 (2024)
Hyperspectral images (HSIs) contain abundant spectral information, while the spatial resolution is usually limited. To obtain high-spatial-resolution HSIs, various HSI super-resolution (SR) methods are proposed. Currently, deep-learning-based SR reco
Externí odkaz:
https://doaj.org/article/c32d4f4db9434d588ea02bb366495637
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15721-15734 (2024)
Small objects are widely distributed on remote sensing images (RSIs), and most of them are achieved by super-resolution (SR) reconstruction followed by detection. However, due to the independent training of the SR network and the detection network, t
Externí odkaz:
https://doaj.org/article/37212381079a4ec7b7435f6a2e5b05b6
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13669-13677 (2024)
Remote sensing image analysis plays a vital role in achieving intelligent agricultural monitoring. However, the acquisition of high-resolution agricultural remote sensing data can be resource-intensive, resulting in an imbalance between training samp
Externí odkaz:
https://doaj.org/article/8b13ba540e1742a7b01973da2e0ef14a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11117-11133 (2024)
Super-resolution reconstruction technology is a crucial approach to enhance the quality of remote sensing optical images. Currently, the mainstream reconstruction methods leverage convolutional neural networks (CNNs). However, they overlook the globa
Externí odkaz:
https://doaj.org/article/0634e07069e84ff9908d5c565a7723cb
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 10636-10647 (2024)
Digital surface model (DSM) is the fundamental data in various geoscience applications, such as city 3-D modeling and urban environment analysis. The freely available DSM often suffers from limited spatial resolution. Super-resolution (SR) is a promi
Externí odkaz:
https://doaj.org/article/3d8796d7f6bf4ff3b0ea813bb5d8a74d
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 8581-8593 (2024)
Smart satellites and unmanned aerial vehicles (UAVs) are typically equipped with visible light and infrared (IR) spectrum sensors. However, achieving real-time object detection utilizing these multimodal data on such resource-limited devices is a cha
Externí odkaz:
https://doaj.org/article/24320c0fba4e40f7a3c636be8871dd83
Autor:
The Van Le, Jin Young Lee
Publikováno v:
IEEE Access, Vol 12, Pp 40989-40999 (2024)
Video captioning is an automatic task that collects natural language to represent visual content. Recently, it has achieved lots of amazing progress thanks to deep learning techniques. Most techniques have mainly focused on a deep learning network ar
Externí odkaz:
https://doaj.org/article/2a715d9827164f4aab242d30993de2a5
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 6860-6874 (2024)
Super-resolution (SR) based on deep learning has been playing an important role in improving the spatial resolution of remote sensing images. Although convolutional neural networks (CNNs) dominate the research of remote sensing image SR, most of them
Externí odkaz:
https://doaj.org/article/cd8d955b438c4d278081e3a9300e02bd
Autor:
Valentino Constantinou, Mark Hoffmann, Matthew Paterson, Ali Mezher, Brian Pak, Alexander Pertica, Emily Milne
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 6354-6365 (2024)
The expansion of small satellite networks in earth's orbit has resulted in a plethora of earth optical imagery available to the civil, defense, and commercial sectors. Small satellites (less than 1000 kg in mass) and their constellations can be deliv
Externí odkaz:
https://doaj.org/article/cea2461c4c794073b8aa9eb1bf0a180d