.png){kind=icon}
Return
Adaptive Hypergraph Regularized Multilayer Sparse Tensor Factorization for Hyperspectral Unmixing
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2023.3241115.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Hyperspectral unmixing with tensor models has received great attention in recent years. A tensor-based decomposition method can effectively represent the structural feature of hyperspectral images; however, the obtained results may be physically uninterpretable. To overcome this limitation, a novel adaptive hypergraph regularized multilayer sparse tensor factorization (AHGMLSTF) algorithm is proposed. First, a modified hypergraph is incorporated into tensor factorization, and the modified hypergraph uses spectral angle distance (SAD) instead of Euclidean distance to construct hyperedges to better represent the joint spatial and spectral information. Then, the hypergraph is constructed adaptively by hyperedges of k neighborhoods. Second, the concept of multilayer decomposition is introduced to explore the hierarchical features of hyperspectral images, and a sparse constraint is imposed on each layer to make the unmixing results more consistent with the physical mechanism of mixed spectral pixels. With these constraints, the proposed method established a spectral-spatial joint tensor decomposition model that represents not only the local neighborhood similarity but also the heterogeneity of adjacent edges. Experiments on simulated data and real hyperspectral data demonstrate the effectiveness of the proposed method.
Keywords:
Hypergraph
hyperspectral image
multilayer
sparse unmixing
tensor factorization
Journal
IF:
8.6
Papers:
2.2W
Citations:
10.7W
Organization
Cited Papers
Hyperspectral Unmixing via Nonnegative Matrix Factorization With Handcrafted and Learned Priors
.png){kind=icon}
PREAI
ShareSave
ShareSave
ShareSaveShareSave
View more