Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Kenneth-Yeonkong Ma"'
Publikováno v:
Remote Sensing, Vol 16, Iss 6, p 942 (2024)
Band clustering has been widely used for hyperspectral band selection (BS). However, selecting an appropriate band to represent a band cluster is a key issue. Density peak clustering (DPC) provides an effective means for this purpose, referred to as
Externí odkaz:
https://doaj.org/article/b8d30c7c2155471989d8f987a74b7281
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 3986-4007 (2020)
Hyperspectral image classification (HSIC) has generated considerable interests over the past years. However, one of challenging issues arising in HSIC is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecti
Externí odkaz:
https://doaj.org/article/d207e658693747a3822fcb5915c71b27
Autor:
Chein-I Chang, Kenneth Yeonkong Ma
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-21
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c96172dd787c1f754b545c113677a32
https://doi.org/10.1109/tgrs.2022.3146803
https://doi.org/10.1109/tgrs.2022.3146803
Autor:
Kenneth Yeonkong Ma, Chein-I Chang
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-23
One fundamental task of hyperspectral imaging is spectral unmixing. In this case, the conventional pure pixel-based hyperspectral image classification (HSIC) may not work effectively for mixed pixels. This article presents a kernel-based approach to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20708847e6da4be93dade6cacda3d206
https://doi.org/10.1109/tgrs.2021.3085801
https://doi.org/10.1109/tgrs.2021.3085801
Autor:
Chein-I Chang, Kenneth Yeonkong Ma
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 59:8672-8692
Training sample selection is a great challenge for hyperspectral image classification (HSIC), specifically when only a very limited number of labeled data samples are available for training. Two recently developed concepts for training sample selecti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a86e6f4ea8c8d8d54fc33c992383a64
https://doi.org/10.1109/tgrs.2021.3053204
https://doi.org/10.1109/tgrs.2021.3053204
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 59:5979-5997
Due to significant inter-band correlation resulting from the use of hundreds of contiguous spectral bands, band selection (BS) is commonly used to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effectiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23ca79150ddbe6a253061983ea2107b2
https://doi.org/10.1109/tgrs.2020.3024602
https://doi.org/10.1109/tgrs.2020.3024602
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 3986-4007 (2020)
Hyperspectral image classification (HSIC) has generated considerable interests over the past years. However, one of challenging issues arising in HSIC is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98ac432bf667533d6d08bdf293b1675c
https://ieeexplore.ieee.org/document/9137652/
https://ieeexplore.ieee.org/document/9137652/
Publikováno v:
IGARSS
Hyperspectral image classification (HSIC) has received considerable interest in recent years. In particular, spectral-spatial classification methods are proposed to jointly consider spectral and spatial together. However, one of challenging issues in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::faaa90b4736fec8e219cb1858df46aef
https://doi.org/10.1109/igarss.2019.8898826
https://doi.org/10.1109/igarss.2019.8898826
Improving pesticide residues detection using band prioritization and constrained energy minimization
Publikováno v:
IGARSS
This paper presents an emerging method to detect pesticide residues on fruit. In order to enhance pesticide signature intensity and make the detection rate of pesticide better, we applied band weighting process and band selection (BS) process base on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f351827c9fcf072100e3f0ad75ce924
https://doi.org/10.1109/igarss.2017.8128076
https://doi.org/10.1109/igarss.2017.8128076
Autor:
Chang, Chein-I1,2,3 (AUTHOR) ccgsh10923@gmail.com, Kuo, Yi-Mei2 (AUTHOR) a0721988@gmail.com, Ma, Kenneth Yeonkong2 (AUTHOR)
Publikováno v:
Remote Sensing. Mar2024, Vol. 16 Issue 6, p942. 29p.