Zobrazeno 1 - 10
of 3 548
pro vyhledávání: '"hyperspectral image (HSI)"'
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
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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14726-14741 (2024)
Although many scholars have realized that deep learning methods have great advantages in hyperspectral lithology classification now, most of them use simple convolutional neural networks for discussion, which are difficult to effectively extract spec
Externí odkaz:
https://doaj.org/article/604d781a88b74b9ebacfb008d6a944ea
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15393-15406 (2024)
In recent years, deep learning algorithms, particularly convolutional neural networks, have significantly improved the performance of the hyperspectral image (HSI) classification. However, due to the high dimensionality of HSI and limited training sa
Externí odkaz:
https://doaj.org/article/6dc29576d5e44f1e892cda65acf58d37
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15122-15135 (2024)
The fusion of low-spatial-resolution hyperspectral image and high-spatial-resolution multispectral image provides an effective method to obtain high-spatial-resolution hyperspectral image. However, existing hybrid fusion architectures combining convo
Externí odkaz:
https://doaj.org/article/36f5f31e349542f68a17f881517c4ab8
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14486-14501 (2024)
Deep learning is an effective method for hyperspectral image (HSI) classification, where CNN-based and Transformer-based methods have achieved excellent performance. However, there are some drawbacks to the existing CNN-based and Transformer-based HS
Externí odkaz:
https://doaj.org/article/922577d4c4144006b93ac0f55dcb429f
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14521-14542 (2024)
Convolutional neural network (CNN) and transformer-based models have been widely used in hyperspectral image (HSI) classification due to their excellent local and global modeling capabilities. In addition, attention mechanism is widely embedded in th
Externí odkaz:
https://doaj.org/article/5e0126c779c1402a8521276a503d5888
Autor:
Javad Mahmoodi, Dariush Abbasi-Moghadam, Alireza Sharifi, Hossein Nezamabadi-Pour, Mohammad Esmaeili, Alireza Vafaeinejad
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14588-14613 (2024)
Recent advancements in remote sensing technology have significantly expanded the exploration of natural resources and enabled the detection of materials in inaccessible areas. Hyperspectral images (HSIs) are a valuable data source due to their distin
Externí odkaz:
https://doaj.org/article/a443848c297649bc8c9204dde0659302
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14064-14079 (2024)
With a spurt of progress in deep learning techniques, convolutional neural network-based and transformer-based methods have yielded impressive performance on the hyperspectral image (HSI) classification tasks. However, pixel-level manual annotation i
Externí odkaz:
https://doaj.org/article/bbc213521ab94b9b9670fdbfcaa39546