Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Remote sensing image scene classification"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 4591-4606 (2023)
Few-shot learning, which aims to learn the concept of novel category from extremely limited labeled samples, has received intense interests in remote sensing image scene classification. Most of the existing methods inherit the philosophy of prototype
Externí odkaz:
https://doaj.org/article/be6f2cff2f624592bffb5cce1c7bca64
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 15, Iss 1, Pp 1-11 (2022)
Abstract With the availability of numerous high-resolution remote sensing images, remote sensing image scene classification has been widely used in various fields. Compared with the field of natural images, the insufficient number of labeled remote s
Externí odkaz:
https://doaj.org/article/6aac0ceda2ea409080ea8b77a9d7904d
Autor:
Weilong Guo, Shengyang Li, Jian Yang, Zhuang Zhou, Yunfei Liu, Junjie Lu, Longxuan Kou, Manqi Zhao
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 2546-2562 (2022)
Remote sensing image scene classification faces challenges, such as the difference in semantic granularity of different scene categories and the imbalance of the number of samples, which cause the wrong features learning for deep convolutional networ
Externí odkaz:
https://doaj.org/article/eaf78821fc3b41929f08295339ad4a07
Akademický článek
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Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 2508-2521 (2021)
With the development of supervised deep neural networks, classification performance on existing remote sensing scene datasets has been markedly improved. However, supervised learning methods rely heavily on large-scale tagged examples to obtain a bet
Externí odkaz:
https://doaj.org/article/eb7142200a5f43abbf7a6e806a600ab0
Akademický článek
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Publikováno v:
Remote Sensing, Vol 14, Iss 22, p 5838 (2022)
In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcra
Externí odkaz:
https://doaj.org/article/3ca5674cedaa451dad40a0ed2e26414b
Publikováno v:
Remote Sensing, Vol 14, Iss 21, p 5550 (2022)
Remote sensing image scene classification has drawn extensive attention for its wide application in various scenarios. Scene classification in many practical cases faces the challenge of few-shot conditions. The major difficulty of few-shot remote se
Externí odkaz:
https://doaj.org/article/8cf9dadc80bb4d8cab0fa1a4645b1060
Publikováno v:
Remote Sensing, Vol 14, Iss 18, p 4533 (2022)
Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classific
Externí odkaz:
https://doaj.org/article/0104136461c74c79abc83c9d5221cece
Publikováno v:
Remote Sensing, Vol 14, Iss 12, p 2794 (2022)
Remote sensing image scene classification (RSISC) plays a vital role in remote sensing applications. Recent methods based on convolutional neural networks (CNNs) have driven the development of RSISC. However, these approaches are not adequate conside
Externí odkaz:
https://doaj.org/article/095684806cf4482a9549c6cc08c205b0