Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Xiaodi Shang"'
Publikováno v:
IET Image Processing, Vol 18, Iss 8, Pp 2053-2063 (2024)
Abstract As one of the dimensionality reduction techniques of hyperspectral image (HSI), band selection (BS) does not change the spectral characteristics and physical meaning of HSIs, which is beneficial to the identification and analysis of surface
Externí odkaz:
https://doaj.org/article/68ac85ef7af649c982ab6ccd3d25fafe
Autor:
Houfu Liu, Ge Jin, Ruoxuan Wang, Zhengyi Lian, Xiucai Hu, Zhang Luo, Aijun Lv, Lei Jia, Xiaodi Shang
Publikováno v:
Water Biology and Security, Vol 3, Iss 3, Pp 100277- (2024)
Half-smooth tongue sole (Cynoglossus semilaevis) is regarded as a significant commercial marine fish species in China, and frequent outbreaks of vibriosis has led to substantial economic losses. In this study, we investigated the molecular mechanisms
Externí odkaz:
https://doaj.org/article/533f28b046734fb8bf0dc7488c563123
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14050-14063 (2024)
In recent years, subspace clustering has become increasingly popular and achieved great success in band selection (BS) of hyperspectral imagery. However, current subspace clustering approaches are mostly insufficient in capturing the fine spatial str
Externí odkaz:
https://doaj.org/article/c3399001b4bf4795bac23408178ebf77
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 12490-12504 (2024)
In recent years, sparse self-representation has achieved remarkable success in hyperspectral band selection. However, the traditional sparse self-representation-based band selection methods tend to neglect the spatial distribution differences and spe
Externí odkaz:
https://doaj.org/article/a59d5007c8b5463eb6d62007d0c4030e
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 10032-10050 (2024)
Band selection (BS) is a crucial concept within the realm of remote sensing, involving the selection of the most suitable bands to accurately capture features of landforms and surfaces. Despite the promising results achieved by many existing methods,
Externí odkaz:
https://doaj.org/article/61aad92887a14cae8cf8ea0234f6c184
Publikováno v:
Remote Sensing, Vol 16, Iss 15, p 2848 (2024)
Band selection (BS) aims to reduce redundancy in hyperspectral imagery (HSI). Existing BS approaches typically model HSI only in a single dimension, either spectral or spatial, without exploring the interactions between different dimensions. To this
Externí odkaz:
https://doaj.org/article/2380040253b34f058fbde34b0f0e8065
Publikováno v:
Remote Sensing, Vol 16, Iss 11, p 2029 (2024)
With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and produci
Externí odkaz:
https://doaj.org/article/09ac2253bece43b4b7cfcccc376ff9e4
Autor:
Jiajie Wang, Xiaopeng Wang, Jiahua Zhang, Xiaodi Shang, Yuyi Chen, Yiping Feng, Bingbing Tian
Publikováno v:
Remote Sensing, Vol 16, Iss 9, p 1565 (2024)
Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into
Externí odkaz:
https://doaj.org/article/c9a59cf30d3a49b0a7717d19299a8cc1
Publikováno v:
IET Image Processing, Vol 16, Iss 13, Pp 3557-3566 (2022)
Abstract Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors. Therefore, for the application of hyperspectral im
Externí odkaz:
https://doaj.org/article/bf7e10c77ee1421ca9b488fd83e601d8
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 553-566 (2021)
This article proposed a novel spectral-spatial classification framework for hyperspectral image (HSI) through combining collaborative representation (CR) and maximum margin projection (MMP). First, class-dependent CR classifier (CDCRC) is used on HSI
Externí odkaz:
https://doaj.org/article/6ddd380b81444f8895aa72a570c7cd12