Matches in SemOpenAlex for { <https://semopenalex.org/work/W2022361862> ?p ?o ?g. }
- W2022361862 endingPage "5163" @default.
- W2022361862 startingPage "5150" @default.
- W2022361862 abstract "Recently, many researchers have attempted to exploit spectral–spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral–spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral–spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples." @default.
- W2022361862 created "2016-06-24" @default.
- W2022361862 creator A5009123701 @default.
- W2022361862 creator A5009129294 @default.
- W2022361862 creator A5028584961 @default.
- W2022361862 creator A5030863883 @default.
- W2022361862 date "2014-08-01" @default.
- W2022361862 modified "2023-10-18" @default.
- W2022361862 title "Kernel Sparse Multitask Learning for Hyperspectral Image Classification With Empirical Mode Decomposition and Morphological Wavelet-Based Features" @default.
- W2022361862 cites W1974981350 @default.
- W2022361862 cites W1979078563 @default.
- W2022361862 cites W1992961908 @default.
- W2022361862 cites W1997565609 @default.
- W2022361862 cites W1999418380 @default.
- W2022361862 cites W2001298023 @default.
- W2022361862 cites W2005106632 @default.
- W2022361862 cites W2007221293 @default.
- W2022361862 cites W2013793344 @default.
- W2022361862 cites W2021576853 @default.
- W2022361862 cites W2043116665 @default.
- W2022361862 cites W2043665634 @default.
- W2022361862 cites W2044439250 @default.
- W2022361862 cites W2045095960 @default.
- W2022361862 cites W2061572659 @default.
- W2022361862 cites W2064604707 @default.
- W2022361862 cites W2081584380 @default.
- W2022361862 cites W2087347434 @default.
- W2022361862 cites W2097915756 @default.
- W2022361862 cites W2102674365 @default.
- W2022361862 cites W2112589365 @default.
- W2022361862 cites W2113513024 @default.
- W2022361862 cites W2126796976 @default.
- W2022361862 cites W2127271355 @default.
- W2022361862 cites W2128550928 @default.
- W2022361862 cites W2129638195 @default.
- W2022361862 cites W2129686634 @default.
- W2022361862 cites W2130630043 @default.
- W2022361862 cites W2131725398 @default.
- W2022361862 cites W2131864940 @default.
- W2022361862 cites W2135562430 @default.
- W2022361862 cites W2135758523 @default.
- W2022361862 cites W2144151128 @default.
- W2022361862 cites W2145096794 @default.
- W2022361862 cites W2145649962 @default.
- W2022361862 cites W2154881938 @default.
- W2022361862 cites W2156909104 @default.
- W2022361862 cites W2159070926 @default.
- W2022361862 cites W2160979406 @default.
- W2022361862 cites W2162698522 @default.
- W2022361862 cites W2164330327 @default.
- W2022361862 cites W2170407643 @default.
- W2022361862 cites W2171566342 @default.
- W2022361862 cites W4256117910 @default.
- W2022361862 doi "https://doi.org/10.1109/tgrs.2013.2287022" @default.
- W2022361862 hasPublicationYear "2014" @default.
- W2022361862 type Work @default.
- W2022361862 sameAs 2022361862 @default.
- W2022361862 citedByCount "38" @default.
- W2022361862 countsByYear W20223618622014 @default.
- W2022361862 countsByYear W20223618622015 @default.
- W2022361862 countsByYear W20223618622016 @default.
- W2022361862 countsByYear W20223618622017 @default.
- W2022361862 countsByYear W20223618622018 @default.
- W2022361862 countsByYear W20223618622019 @default.
- W2022361862 countsByYear W20223618622020 @default.
- W2022361862 countsByYear W20223618622021 @default.
- W2022361862 countsByYear W20223618622022 @default.
- W2022361862 countsByYear W20223618622023 @default.
- W2022361862 crossrefType "journal-article" @default.
- W2022361862 hasAuthorship W2022361862A5009123701 @default.
- W2022361862 hasAuthorship W2022361862A5009129294 @default.
- W2022361862 hasAuthorship W2022361862A5028584961 @default.
- W2022361862 hasAuthorship W2022361862A5030863883 @default.
- W2022361862 hasConcept C106131492 @default.
- W2022361862 hasConcept C114614502 @default.
- W2022361862 hasConcept C115961682 @default.
- W2022361862 hasConcept C124066611 @default.
- W2022361862 hasConcept C13280743 @default.
- W2022361862 hasConcept C138885662 @default.
- W2022361862 hasConcept C153180895 @default.
- W2022361862 hasConcept C154945302 @default.
- W2022361862 hasConcept C159078339 @default.
- W2022361862 hasConcept C185798385 @default.
- W2022361862 hasConcept C196216189 @default.
- W2022361862 hasConcept C205649164 @default.
- W2022361862 hasConcept C25570617 @default.
- W2022361862 hasConcept C2776401178 @default.
- W2022361862 hasConcept C31972630 @default.
- W2022361862 hasConcept C33923547 @default.
- W2022361862 hasConcept C41008148 @default.
- W2022361862 hasConcept C41895202 @default.
- W2022361862 hasConcept C47432892 @default.
- W2022361862 hasConcept C52622490 @default.
- W2022361862 hasConcept C74193536 @default.
- W2022361862 hasConcept C75294576 @default.
- W2022361862 hasConceptScore W2022361862C106131492 @default.
- W2022361862 hasConceptScore W2022361862C114614502 @default.
- W2022361862 hasConceptScore W2022361862C115961682 @default.