Matches in SemOpenAlex for { <https://semopenalex.org/work/W2985175950> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W2985175950 abstract "Gaussian mixture model (GMM) can estimate not only the abundances and distribution parameters but also distinct end-member set for each pixel. However, the traditional GMM unmixing model only has proper smoothness and sparsity prior constraints on the abundances and thus cannot excavate the local spatial information in hyperspectral image (HSI). Thus, we propose a new unmixing method with superpixel segmentation (SS) and low-rank representation (LRR) based on GMM called GMM-SS-LRR, which can consider the local spatial correlation of HSI. First, we adopt the principal component analysis (PCA) to obtain the first principal component of HSI, which contains the most information for the entire HSI. Then, we adopt the SS in the first principal component of HSI to obtain the homogeneous regions, and the abundances in each homogeneous region have the underlying low-rank property. Finally, we unmix the pixels in each homogeneous region of HSI depending on the low-rank property of abundances. Experiments on synthetic datasets and real H-SIs demonstrate that the proposed GMM-SS-LRR is efficient compared with other current popular methods." @default.
- W2985175950 created "2019-11-22" @default.
- W2985175950 creator A5002618865 @default.
- W2985175950 creator A5002872664 @default.
- W2985175950 creator A5019560977 @default.
- W2985175950 creator A5021381864 @default.
- W2985175950 creator A5030825527 @default.
- W2985175950 creator A5036746956 @default.
- W2985175950 creator A5080470968 @default.
- W2985175950 date "2019-07-01" @default.
- W2985175950 modified "2023-09-24" @default.
- W2985175950 title "Gaussian Mixture Model for Hyperspectral Unmixing with Low-Rank Representation" @default.
- W2985175950 cites W1561797649 @default.
- W2985175950 cites W1798575223 @default.
- W2985175950 cites W1965340435 @default.
- W2985175950 cites W1967639437 @default.
- W2985175950 cites W2069921544 @default.
- W2985175950 cites W2091397530 @default.
- W2985175950 cites W2097211423 @default.
- W2985175950 cites W2107219953 @default.
- W2985175950 cites W2239643428 @default.
- W2985175950 cites W2542152021 @default.
- W2985175950 cites W2591248827 @default.
- W2985175950 cites W2773583860 @default.
- W2985175950 cites W2800324071 @default.
- W2985175950 cites W3106244377 @default.
- W2985175950 doi "https://doi.org/10.1109/igarss.2019.8898410" @default.
- W2985175950 hasPublicationYear "2019" @default.
- W2985175950 type Work @default.
- W2985175950 sameAs 2985175950 @default.
- W2985175950 citedByCount "8" @default.
- W2985175950 countsByYear W29851759502020 @default.
- W2985175950 countsByYear W29851759502021 @default.
- W2985175950 countsByYear W29851759502022 @default.
- W2985175950 countsByYear W29851759502023 @default.
- W2985175950 crossrefType "proceedings-article" @default.
- W2985175950 hasAuthorship W2985175950A5002618865 @default.
- W2985175950 hasAuthorship W2985175950A5002872664 @default.
- W2985175950 hasAuthorship W2985175950A5019560977 @default.
- W2985175950 hasAuthorship W2985175950A5021381864 @default.
- W2985175950 hasAuthorship W2985175950A5030825527 @default.
- W2985175950 hasAuthorship W2985175950A5036746956 @default.
- W2985175950 hasAuthorship W2985175950A5080470968 @default.
- W2985175950 hasConcept C114614502 @default.
- W2985175950 hasConcept C121332964 @default.
- W2985175950 hasConcept C153180895 @default.
- W2985175950 hasConcept C154945302 @default.
- W2985175950 hasConcept C159078339 @default.
- W2985175950 hasConcept C163716315 @default.
- W2985175950 hasConcept C164226766 @default.
- W2985175950 hasConcept C17744445 @default.
- W2985175950 hasConcept C199539241 @default.
- W2985175950 hasConcept C2776359362 @default.
- W2985175950 hasConcept C33923547 @default.
- W2985175950 hasConcept C41008148 @default.
- W2985175950 hasConcept C61224824 @default.
- W2985175950 hasConcept C61326573 @default.
- W2985175950 hasConcept C62520636 @default.
- W2985175950 hasConcept C94625758 @default.
- W2985175950 hasConceptScore W2985175950C114614502 @default.
- W2985175950 hasConceptScore W2985175950C121332964 @default.
- W2985175950 hasConceptScore W2985175950C153180895 @default.
- W2985175950 hasConceptScore W2985175950C154945302 @default.
- W2985175950 hasConceptScore W2985175950C159078339 @default.
- W2985175950 hasConceptScore W2985175950C163716315 @default.
- W2985175950 hasConceptScore W2985175950C164226766 @default.
- W2985175950 hasConceptScore W2985175950C17744445 @default.
- W2985175950 hasConceptScore W2985175950C199539241 @default.
- W2985175950 hasConceptScore W2985175950C2776359362 @default.
- W2985175950 hasConceptScore W2985175950C33923547 @default.
- W2985175950 hasConceptScore W2985175950C41008148 @default.
- W2985175950 hasConceptScore W2985175950C61224824 @default.
- W2985175950 hasConceptScore W2985175950C61326573 @default.
- W2985175950 hasConceptScore W2985175950C62520636 @default.
- W2985175950 hasConceptScore W2985175950C94625758 @default.
- W2985175950 hasLocation W29851759501 @default.
- W2985175950 hasOpenAccess W2985175950 @default.
- W2985175950 hasPrimaryLocation W29851759501 @default.
- W2985175950 hasRelatedWork W1869808405 @default.
- W2985175950 hasRelatedWork W2028628118 @default.
- W2985175950 hasRelatedWork W2031007444 @default.
- W2985175950 hasRelatedWork W2107898784 @default.
- W2985175950 hasRelatedWork W2127813325 @default.
- W2985175950 hasRelatedWork W2439208816 @default.
- W2985175950 hasRelatedWork W2783789044 @default.
- W2985175950 hasRelatedWork W2891787551 @default.
- W2985175950 hasRelatedWork W3211035526 @default.
- W2985175950 hasRelatedWork W4291701050 @default.
- W2985175950 isParatext "false" @default.
- W2985175950 isRetracted "false" @default.
- W2985175950 magId "2985175950" @default.
- W2985175950 workType "article" @default.