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Spectral Profile Partial Least-Squares (SP-PLS): Local multivariate pansharpening on spectral profiles

The compatibility of multispectral (MS) pansharpening algorithms with hyperspectral (HS) data is limited. With the recent development in HS satellites, there is a need for methods that can provide high spatial and spectral fidelity in both HS and MS scenarios.
The present article presents a fast pansharpening method, based on the division of similar hyperspectral data in spectral subgroups using k-means clustering and Spectral Angle Mapper (SAM) profiling. Local Partial Least-Square (PLS) models are calibrated for each spectral subgroup against the respective pixels of the panchromatic image. The models are inverted to retrieve high-resolution pansharpened images. The method is tested against different methods that are able to handle both MS and HS pansharpening and assessed using reduced- and full-resolution evaluation methodologies. Based on a statistical multivariate approach, the proposed method is able to render uncertainty maps for spectral or spatial fidelity - functionality not reported in any other pansharpening study.

Ketersediaan
46621.3678Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
ISPRS Open Journal of Photogrammetry and Remote Sensing
No. Panggil
621.3678
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
12 hlm PDF, 9.538 KB
Bahasa
Inggris
ISBN/ISSN
1872-8235
Klasifikasi
621.3678
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.10, December 2023
Subjek
Pansharpening
Hyperspectral Imaging (HSI)
Spectral data fusion
Partial Least-Squares (PLS)
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Spectral Profile Partial Least-Squares (SP-PLS): Local multivariate pansharpening on spectral profiles
    The compatibility of multispectral (MS) pansharpening algorithms with hyperspectral (HS) data is limited. With the recent development in HS satellites, there is a need for methods that can provide high spatial and spectral fidelity in both HS and MS scenarios. The present article presents a fast pansharpening method, based on the division of similar hyperspectral data in spectral subgroups using k-means clustering and Spectral Angle Mapper (SAM) profiling. Local Partial Least-Square (PLS) models are calibrated for each spectral subgroup against the respective pixels of the panchromatic image. The models are inverted to retrieve high-resolution pansharpened images. The method is tested against different methods that are able to handle both MS and HS pansharpening and assessed using reduced- and full-resolution evaluation methodologies. Based on a statistical multivariate approach, the proposed method is able to render uncertainty maps for spectral or spatial fidelity - functionality not reported in any other pansharpening study.
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