Zobrazeno 1 - 10
of 34 681
pro vyhledávání: '"spatial-spectral"'
Hyperspectral image (HSI) classification has garnered substantial attention in remote sensing fields. Recent Mamba architectures built upon the Selective State Space Models (S6) have demonstrated enormous potential in long-range sequence modeling. Ho
Externí odkaz:
http://arxiv.org/abs/2410.05100
Autor:
Chen, Jingwei1 (AUTHOR) chenjingweijc@163.com
Publikováno v:
Applied Artificial Intelligence. 2024, Vol. 38 Issue 1, p1-24. 24p.
Hyperspectral and multispectral image fusion aims to generate high spectral and spatial resolution hyperspectral images (HR-HSI) by fusing high-resolution multispectral images (HR-MSI) and low-resolution hyperspectral images (LR-HSI). However, existi
Externí odkaz:
http://arxiv.org/abs/2409.09670
Autor:
Feng, Jie, Zhang, Tianshu, Zhang, Junpeng, Shang, Ronghua, Dong, Weisheng, Shi, Guangming, Jiao, Licheng
Unsupervised domain adaptation techniques, extensively studied in hyperspectral image (HSI) classification, aim to use labeled source domain data and unlabeled target domain data to learn domain invariant features for cross-scene classification. Comp
Externí odkaz:
http://arxiv.org/abs/2408.15263
Autor:
Ahmad, Muhammad, Butt, Muhammad Hassaan Farooq, Khan, Adil Mehmood, Mazzara, Manuel, Distefano, Salvatore, Usama, Muhammad, Roy, Swalpa Kumar, Chanussot, Jocelyn, Hong, Danfeng
Recent advancements in transformers, specifically self-attention mechanisms, have significantly improved hyperspectral image (HSI) classification. However, these models often suffer from inefficiencies, as their computational complexity scales quadra
Externí odkaz:
http://arxiv.org/abs/2408.01372
Hyperspectral Imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications. Despite the advancements in Deep Learning (DL) and Transformer architectures for HSI classification, cha
Externí odkaz:
http://arxiv.org/abs/2408.01231
This paper studies the large-scale cell-free networks where dense distributed access points (APs) serve many users. As a promising next-generation network architecture, cell-free networks enable ultra-reliable connections and minimal fading/blockage,
Externí odkaz:
http://arxiv.org/abs/2407.11389
Motor imagery electroencephalogram (MI-EEG) decoding plays a crucial role in developing motor imagery brain-computer interfaces (MI-BCIs). However, decoding intentions from MI remains challenging due to the inherent complexity of EEG signals relative
Externí odkaz:
http://arxiv.org/abs/2407.03177
Classification of Hyperspectral Images of Explosive Fragments Based on Spatial–Spectral Combination.
Autor:
Zhao, Donge1,2 (AUTHOR) b20240511@st.nuc.edu.cn, Yu, Peiyun2 (AUTHOR) s2005063@st.nuc.edu.cn, Guo, Feng2 (AUTHOR) yxf768@nuc.edu.cn, Yang, Xuefeng2 (AUTHOR) mayayun@nuc.edu.cn, Ma, Yayun2 (AUTHOR) 20160080@nuc.edu.cn, Wang, Changli3 (AUTHOR) wangchangli@nint.ac.cn, Li, Kang3 (AUTHOR) likang@nint.ac.cn, Chu, Wenbo4 (AUTHOR) 20210008@nuc.edu.cn, Zhang, Bin2 (AUTHOR)
Publikováno v:
Sensors (14248220). Nov2024, Vol. 24 Issue 22, p7131. 25p.
Autor:
Ahmad, Muhammad, Butt, Muhammad Hassaan Farooq, Usama, Muhammad, Altuwaijri, Hamad Ahmed, Mazzara, Manuel, Distefano, Salvatore
Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations. However, traditional Mamba models overlook rich spectral information in HSIs and struggle with high dimensionalit
Externí odkaz:
http://arxiv.org/abs/2408.01224