FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2999446243> ?p ?o ?g. }

• W2999446243 endingPage "4250" @default.
• W2999446243 startingPage "4237" @default.
• W2999446243 abstract "In recent years, deep learning has presented a great advance in the hyperspectral image (HSI) classification. Particularly, long short-term memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs. However, the loss of spatial information makes it quite difficult to obtain better performance. In order to address this problem, two novel deep models are proposed to extract more discriminative spatial-spectral features by exploiting the convolutional LSTM (ConvLSTM). By taking the data patch in a local sliding window as the input of each memory cell band by band, the 2-D extended architecture of LSTM is considered for building the spatial-spectral ConvLSTM 2-D neural network (SSCL2DNN) to model long-range dependencies in the spectral domain. To better preserve the intrinsic structure information of the hyperspectral data, the spatial-spectral ConvLSTM 3-D neural network (SSCL3DNN) is proposed by extending LSTM to the 3-D version for further improving the classification performance. The experiments, conducted on three commonly used HSI data sets, demonstrate that the proposed deep models have certain competitive advantages and can provide better classification performance than the other state-of-the-art approaches." @default.
• W2999446243 created "2020-01-23" @default.
• W2999446243 creator A5000432967 @default.
• W2999446243 creator A5008770845 @default.
• W2999446243 creator A5015155189 @default.
• W2999446243 creator A5033017179 @default.
• W2999446243 creator A5067803447 @default.
• W2999446243 creator A5067999166 @default.
• W2999446243 date "2020-06-01" @default.
• W2999446243 modified "2023-10-16" @default.
• W2999446243 title "Spatial–Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification" @default.
• W2999446243 cites W1521436688 @default.
• W2999446243 cites W1948819007 @default.
• W2999446243 cites W2008847349 @default.
• W2999446243 cites W2029316659 @default.
• W2999446243 cites W2038386419 @default.
• W2999446243 cites W2064675550 @default.
• W2999446243 cites W2068067793 @default.
• W2999446243 cites W2069231830 @default.
• W2999446243 cites W2076961568 @default.
• W2999446243 cites W2087263574 @default.
• W2999446243 cites W2097915756 @default.
• W2999446243 cites W2105386417 @default.
• W2999446243 cites W2122585011 @default.
• W2999446243 cites W2129812935 @default.
• W2999446243 cites W2144348684 @default.
• W2999446243 cites W2153635508 @default.
• W2999446243 cites W2164330327 @default.
• W2999446243 cites W2290946060 @default.
• W2999446243 cites W2314785379 @default.
• W2999446243 cites W2465503420 @default.
• W2999446243 cites W2480631127 @default.
• W2999446243 cites W2500751094 @default.
• W2999446243 cites W2548791488 @default.
• W2999446243 cites W2572303978 @default.
• W2999446243 cites W2577238056 @default.
• W2999446243 cites W2592224809 @default.
• W2999446243 cites W2600746131 @default.
• W2999446243 cites W2602024454 @default.
• W2999446243 cites W2620547787 @default.
• W2999446243 cites W2738447277 @default.
• W2999446243 cites W2753248899 @default.
• W2999446243 cites W2764276316 @default.
• W2999446243 cites W2768537477 @default.
• W2999446243 cites W2771065697 @default.
• W2999446243 cites W2772452219 @default.
• W2999446243 cites W2792332881 @default.
• W2999446243 cites W2792827505 @default.
• W2999446243 cites W2811355488 @default.
• W2999446243 cites W2888715336 @default.
• W2999446243 cites W2892075618 @default.
• W2999446243 cites W2893756180 @default.
• W2999446243 cites W2900587135 @default.
• W2999446243 cites W2913594625 @default.
• W2999446243 cites W2922379874 @default.
• W2999446243 cites W2937615289 @default.
• W2999446243 cites W2963659353 @default.
• W2999446243 cites W3100011500 @default.
• W2999446243 cites W3101640299 @default.
• W2999446243 cites W3104839310 @default.
• W2999446243 cites W4240485910 @default.
• W2999446243 cites W639708223 @default.
• W2999446243 doi "https://doi.org/10.1109/tgrs.2019.2961947" @default.
• W2999446243 hasPublicationYear "2020" @default.
• W2999446243 type Work @default.
• W2999446243 sameAs 2999446243 @default.
• W2999446243 citedByCount "95" @default.
• W2999446243 countsByYear W29994462432019 @default.
• W2999446243 countsByYear W29994462432020 @default.
• W2999446243 countsByYear W29994462432021 @default.
• W2999446243 countsByYear W29994462432022 @default.
• W2999446243 countsByYear W29994462432023 @default.
• W2999446243 crossrefType "journal-article" @default.
• W2999446243 hasAuthorship W2999446243A5000432967 @default.
• W2999446243 hasAuthorship W2999446243A5008770845 @default.
• W2999446243 hasAuthorship W2999446243A5015155189 @default.
• W2999446243 hasAuthorship W2999446243A5033017179 @default.
• W2999446243 hasAuthorship W2999446243A5067803447 @default.
• W2999446243 hasAuthorship W2999446243A5067999166 @default.
• W2999446243 hasBestOaLocation W29994462432 @default.
• W2999446243 hasConcept C108583219 @default.
• W2999446243 hasConcept C127313418 @default.
• W2999446243 hasConcept C138885662 @default.
• W2999446243 hasConcept C153180895 @default.
• W2999446243 hasConcept C154945302 @default.
• W2999446243 hasConcept C159078339 @default.
• W2999446243 hasConcept C159620131 @default.
• W2999446243 hasConcept C2776401178 @default.
• W2999446243 hasConcept C41008148 @default.
• W2999446243 hasConcept C41895202 @default.
• W2999446243 hasConcept C50644808 @default.
• W2999446243 hasConcept C52622490 @default.
• W2999446243 hasConcept C59404180 @default.
• W2999446243 hasConcept C62649853 @default.
• W2999446243 hasConcept C81363708 @default.
• W2999446243 hasConcept C97931131 @default.
• W2999446243 hasConceptScore W2999446243C108583219 @default.
• W2999446243 hasConceptScore W2999446243C127313418 @default.

• next