Zobrazeno 1 - 10
of 239
pro vyhledávání: '"Yuntao Qian"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 19640-19667 (2024)
Fine-grained image recognition (FGIR), unlike traditional coarse-grained recognition, is centered on distinguishing fine-level subclasses within broader semantic categories. It holds significant scientific research value, particularly in remote sensi
Externí odkaz:
https://doaj.org/article/1cbc3dc6119840a1a24822c6b2883140
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 3366-3378 (2024)
Change detection in remote sensing images is a challenging task due to object appearance diversity and the interference of complex backgrounds. Self-attention- and spatial-attention-based solutions face limitations, such as high memory consumption an
Externí odkaz:
https://doaj.org/article/065355dd86214a3b95691bfc94be3e46
Publikováno v:
IET Computer Vision, Vol 17, Iss 7, Pp 739-749 (2023)
Abstract Small‐sample‐size problem is always a challenge for hyperspectral image (HSI) classification. Considering the co‐occurrence of land‐cover classes between similar scenes, transfer learning can be performed, and cross‐scene classific
Externí odkaz:
https://doaj.org/article/725f35de49db4112affe0f684ed8ebae
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 2473-2483 (2021)
Hyperspectral images (HSIs) include hundreds of spectral bands, which lead to Hughes phenomenon in classification task and decrease the classification accuracy. Feature selection can remove redundant and noisy features in the HSIs to overcome this ph
Externí odkaz:
https://doaj.org/article/0b3dc7860f794a77a6acae15c0610932
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 5932-5949 (2021)
In the classification of hyperspectral images (HSIs), too many spectral bands (features) cause feature redundancy, resulting in a reduction in classification accuracy. In order to solve this problem, it is a good method to use feature selection to se
Externí odkaz:
https://doaj.org/article/1e81458f9075457fa51d98e4d44bd2db
Publikováno v:
Alexandria Engineering Journal, Vol 59, Iss 3, Pp 1159-1169 (2020)
With the rapid advancement in technology, network systems are becoming prone to more sophisticated types of intrusions. However, machine learning (ML) based strategies are among the most efficient and popular methods to identify the network intrusion
Externí odkaz:
https://doaj.org/article/2672c43c80174049be05fb660fc14377
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 3164-3178 (2020)
Lack of labeled training samples is a big challenge for hyperspectral image (HSI) classification. In recent years, cross-scene classification has become a new research topic. In cross-scene classification, two closely related HSI scenes are considere
Externí odkaz:
https://doaj.org/article/05af1dc45243429b8cd45a32587fee91
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 6088-6100 (2020)
Hyperspectral unmixing is an important step to learn the material categories and corresponding distributions in a scene. Over the past decade, nonnegative matrix factorization (NMF) has been utilized for this task, thanks to its good physical interpr
Externí odkaz:
https://doaj.org/article/f20daaab4bcb49e2b781b3a2b6c8e936
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 5162-5179 (2020)
Spectral unmixing is an important technique for quantitatively analyzing hyperspectral remote sensing images. Recently, constrained nonnegative matrix factorization (NMF) has been demonstrated to be a powerful tool for spectral unmixing. However, acq
Externí odkaz:
https://doaj.org/article/23a09c44b65e47048f938f0a33f43396
Publikováno v:
IEEE Access, Vol 7, Pp 81407-81418 (2019)
Deep learning has been widely used for hyperspectral pixel classification due to its ability to generate deep feature representation. However, how to construct an efficient and powerful network suitable for hyperspectral data is still under explorati
Externí odkaz:
https://doaj.org/article/2f3d96bab47c40ebae41645e3a43b980