Zobrazeno 1 - 10
of 3 794
pro vyhledávání: '"Hyperspectral image (HSI)"'
Autor:
Yuqi Liu, Enshuo Zhu, Qinghe Wang, Junhong Li, Shujun Liu, Yaowen Hu, Yuhang Han, Guoxiong Zhou, Renxiang Guan
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1139-1152 (2025)
Graph convolution subspace clustering has been widely used in the field of hyperspectral image (HSI) unsupervised classification due to its ability to aggregate neighborhood information. However, existing methods focus on using graph convolution tech
Externí odkaz:
https://doaj.org/article/6f45bcf7fd7f4826abe31e33b955c2b7
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1329-1344 (2025)
Domain adaptation has been proven effective for addressing cross-domain hyperspectral image (HSI) classification, especially when the target domain has no labeled samples. Current domain adaptation algorithms focus on finding domain-invariant subspac
Externí odkaz:
https://doaj.org/article/4a211016184347d196986592a375af8b
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1196-1211 (2025)
In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) are widely used due to their ability to leverage the rich spectral information across multiple bands. However, HSI classification still faces various challenges, includ
Externí odkaz:
https://doaj.org/article/b644dd905935470cacc3ab699c3e9197
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 112-131 (2025)
Hyperspectral image (HSI) classification is a challenging task in remote sensing applications, aiming to determine the category of each pixel by utilizing rich spectral and spatial information in HSI. Convolutional neural networks (CNNs) have been ef
Externí odkaz:
https://doaj.org/article/e87fcdd975e74ecbbcd27b68cfc077c1
Publikováno v:
Shipin gongye ke-ji, Vol 45, Iss 17, Pp 345-351 (2024)
To evaluate the effectiveness of a deep learning which is based intelligent assisted hyperspectral imaging system on the detection of pork freshness indicators, volatile basic nitrogen (TVB-N), total viable count (TVC), and 900~2500 nm near-infrared
Externí odkaz:
https://doaj.org/article/66807ee9346842db8598d254de9fc021
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 134, Iss , Pp 104200- (2024)
Recently, Autoencoders (AEs) have demonstrated remarkable performance in the field of hyperspectral anomaly detection, owing to their powerful capability in handling high-dimensional data. However, they often overlook the inherent global distribution
Externí odkaz:
https://doaj.org/article/17f9642dfb3a45859d3d6c08a5be8811
Autor:
Miaomiao Liang, Xianhao Zhang, Xiangchun Yu, Lingjuan Yu, Zhe Meng, Xiaohong Zhang, Licheng Jiao
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 131, Iss , Pp 103979- (2024)
The success of vision Transformers (ViTs) relies heavily on the self-attention mechanism, which requires support from appropriate patch tokenization. However, hyperspectral image (HSI) often suffer from significant noise distortions and spectral unce
Externí odkaz:
https://doaj.org/article/e9fa3cb5d50a4d39a90f1974270e10d0
Publikováno v:
مهندسی مخابرات جنوب, Vol 12, Iss 47, Pp 37-52 (2024)
Feature extraction has a valuable role in hyperspectral images processing. In recent years, various methods have been presented to extract efficient features of hyperspectral images. Recent studies have successfully used conventional singular spectru
Externí odkaz:
https://doaj.org/article/e584792b2f194c1682c45459c20ee24d
Publikováno v:
IEEE Access, Vol 12, Pp 173076-173090 (2024)
In hyperspectral image (HSI) classification, combining the strengths of convolutional neural networks (CNNs) and Transformers can significantly enhance classification performance and model robustness. However, neural networks that combine CNNs and Tr
Externí odkaz:
https://doaj.org/article/8e0407c34cd0480a889fa733151482ac
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 20080-20097 (2024)
Hyperspectral image (HSI) is pivotal in ground object classification, owing to its rich spatial and spectral information. Recently, convolutional neural networks and graph neural networks have become hotspots in HSI classification. Although various m
Externí odkaz:
https://doaj.org/article/d9c7ff73277f40b388337d487adb12e6