Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Hui Qv"'
Publikováno v:
IEEE Access, Vol 10, Pp 39533-39544 (2022)
This paper introduces a novel and efficient Graph Convolutional Network (GCN) and Spatial Supporting Modification (SSM) method for classifying Hyperspectral Images (HSI), called Spatial First (SPA-F). The proposed method can utilize spatial informati
Externí odkaz:
https://doaj.org/article/f1add7386f3a424eb08f82ec174351b7
Publikováno v:
Pattern Recognition. 129:108745
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::819946afae7799088658446cf7a9b4f4
https://doi.org/10.1016/j.patcog.2022.108745
https://doi.org/10.1016/j.patcog.2022.108745
Publikováno v:
ICIP
In this paper, to alleviate the demand for enormous labeled data in the classification task, an Attention-weighted Graph Convolutional Networks (AwGCN) model for hyperspectral image (HSI) few-shot classification is proposed, which aims to explore the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f9b525d8a25f8853980a2b5421f89c1
https://doi.org/10.1109/icip40778.2020.9190752
https://doi.org/10.1109/icip40778.2020.9190752
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 56:7257-7271
A novel band discrimination analysis framework for hyperspectral image (HSI) supervised classification is proposed based on dual density (DD). Different from the popular supervised band selection (BS) approaches which measure the discrimination among
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5cbda474760b31c12183cc0ad436accf
https://doi.org/10.1109/tgrs.2018.2849881
https://doi.org/10.1109/tgrs.2018.2849881
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 55:3982-3996
A novel segment-oriented dictionary learning (SeODL) framework for hyperspectral image (HSI) classification is proposed. Differing from existing HSI classification methods which directly process the original whole spectral curves of pixels, our work
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e33b264402d8d4b6de0c665522343ef4
https://doi.org/10.1109/tgrs.2017.2686444
https://doi.org/10.1109/tgrs.2017.2686444
Publikováno v:
IGARSS
A global self-labeled distribution analysis (GSLDA) for hyperspectral image (HSI) band selection is proposed in this paper, which focuses on an unsupervised method to ascertain the band discrimination. In order to generate the band labels for further
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f26a32d65cdf5f7d65d4c45935b272d7
https://doi.org/10.1109/igarss.2019.8899035
https://doi.org/10.1109/igarss.2019.8899035
Publikováno v:
Pattern Recognition. 103:107265
Clustering is a research problem based on the data's proximity relationship which is not made full use of by all the existing algorithms. In this paper, we present a novel two-stage LG framework consisting of the proposed Local Energy Gradient Oppres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1cfb2c4f314d78bfa9993673e1116c29
https://doi.org/10.1016/j.patcog.2020.107265
https://doi.org/10.1016/j.patcog.2020.107265
Publikováno v:
IGARSS
Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this pap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad06bd44db49c45eec03671a6d7af5d3
https://doi.org/10.1109/igarss.2017.8127819
https://doi.org/10.1109/igarss.2017.8127819
Publikováno v:
WHISPERS
A Conjugated and Augmented Dictionaries (CAD) learning method based on Sparse Auto-Encoder (SAE) is proposed for hyperspectral image classification. The CAD originates from the intention to combine the synthesis model and analysis model. These two mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::44622a2b249aa805369e5654553313f1
https://doi.org/10.1109/whispers.2016.8071810
https://doi.org/10.1109/whispers.2016.8071810
Publikováno v:
IGARSS
Spectral unmixing is an important technique to exploit mineral distribution through remote sensing image. In this paper, we propose an unmixing algorithm combining clustering-aware method with the sparsity-constrained nonnegative matrix factorization
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41c7ee5079bdd5819a91cb6c74608249
https://doi.org/10.1109/igarss.2016.7730536
https://doi.org/10.1109/igarss.2016.7730536