Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Meiping Song"'
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 130, Iss , Pp 103901- (2024)
Various improved nonnegative matrix factorization (NMF) methods have been widely used in spectral unmixing (SU), including nonlinear versions to counter for the lower spatial resolution and interaction between materials. But the obtained abundances a
Externí odkaz:
https://doaj.org/article/e154f05c80734e39a8e50008a7c96961
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17278-17291 (2024)
Hyperspectral image (HSI) has garnered increasing attention due to its capacity for capturing extensive spectral information. However, the acquisition of high spatial resolution HSIs is often restricted by current imaging hardware limitations. A cost
Externí odkaz:
https://doaj.org/article/e971e5f70ddc41b1aade1f568d97010b
Autor:
Shihui Liu, Meiping Song
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14219-14236 (2024)
Real-time local anomaly detection needs to be performed simultaneously with hyperspectral image acquisition. However, the discussion on the scope of using existing data, mainly focuses on line-by-line data acquisition methods and does not address pix
Externí odkaz:
https://doaj.org/article/cb1a31604d0946cd895283b972ec1424
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 10674-10689 (2024)
Deep learning methodologies have demonstrated considerable effectiveness in hyperspectral anomaly detection (HAD). However, the practicality of deep learning-based HAD in real-world applications is impeded by challenges arising from limited labeled d
Externí odkaz:
https://doaj.org/article/45e1fe0ba2c24fec95c01948b6e4a6f6
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9053-9068 (2024)
Hyperspectral target detection plays a pivotal role in various civil and military applications. Although recent advancements in deep learning have largely embraced supervised learning approaches, they often hindered by the limited availability of lab
Externí odkaz:
https://doaj.org/article/4aab831a9ebd4816aee9b40f565c987e
Publikováno v:
Remote Sensing, Vol 16, Iss 2, p 224 (2024)
As one of the most important techniques for hyperspectral image dimensionality reduction, band selection has received considerable attention, whereas self-representation subspace clustering-based band selection algorithms have received quite a lot of
Externí odkaz:
https://doaj.org/article/263fafe0774444b899e925990d08f2a8
Publikováno v:
Remote Sensing, Vol 16, Iss 1, p 153 (2023)
Solar power generation has great development potential as an abundant and clean energy source. However, many factors affect the efficiency of the photovoltaic (PV) module; among these factors, outdoor PV modules are inevitably affected by stains, thu
Externí odkaz:
https://doaj.org/article/eb461c5a906d4f6481389629a870b868
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
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4915-4932 (2021)
Orthogonal subspace projection (OSP) is a versatile hyperspectral imaging technique which has shown great potential in dimensionality reduction, target detection, spectral unmixing, etc. However, due to its inherent requirement of prior target knowle
Externí odkaz:
https://doaj.org/article/aeede8949c9148c68e4c23640f20983c
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4999-5014 (2021)
Target extraction can provide a prior knowledge for spectral unmixing, unsupervised hyperspectral image classification, and unsupervised target detection tasks, which is of great practice. Considering that the traditional endmember extraction algorit
Externí odkaz:
https://doaj.org/article/5f72ef52959545c0bc75a4ecfb9b3697