FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4297192968> ?p ?o ?g. }

• W4297192968 endingPage "273" @default.
• W4297192968 startingPage "256" @default.
• W4297192968 abstract "Hyperspectral images (HSIs) contain hundreds of continuous spectral bands and are rich in spectral-spatial information. In terms of HSIs’ classification, traditional convolutional neural networks (CNNs) extract features based on HSI's spectral-spatial information through 2D convolution. However, 2D convolution extracts features in 2D plane without considering the relationships between spectral bands, which inevitably leads to insufficient feature extraction. 3D convolutional neural networks (3DCNNs) take account of the correlations among spectral bands and outperform 2D convolutional networks in feature extraction, but the computational cost is rather expensive. To address the above problem, a light-weight three-layer 3D convolutional network Module (3D-M) for HSIs’ spectral-spatial feature extraction is proposed. Another challenge is that neither 2D convolution nor 3D convolution utilizes the structural information inherent in the data. Graph convolution networks (GCNs) can model and utilize such information through the similarity matrix, also known as adjacency matrix. However, traditional GCNs cannot handle large-scale data because they construct adjacency matrix on all data, which results in high computational complexity and large storage requirement. To conquer this challenge, this article proposes a batch-graph strategy on which a scalable GCN is developed. Finally, a hybrid network model (HNM) based on the proposed light-weight 3D-M and scalable GCN is presented. HNM extracts spectral-spatial features of HSIs with low computational complexity through the light-weight 3D convolution network and leverages the structural information in data via the scalable GCN. The experimental results on three public datasets with different sizes demonstrate that the proposed HNM produces better classification results than other state-of-the-art hyperspectral images classification models in terms of overall accuracy (OA), average accuracy (AA) and kappa coefficient (Kappa)." @default.
• W4297192968 created "2022-09-27" @default.
• W4297192968 creator A5002894905 @default.
• W4297192968 creator A5014128597 @default.
• W4297192968 date "2022-09-25" @default.
• W4297192968 modified "2023-10-16" @default.
• W4297192968 title "Hybrid network model based on 3D convolutional neural network and scalable graph convolutional network for hyperspectral image classification" @default.
• W4297192968 cites W1521436688 @default.
• W4297192968 cites W1576462183 @default.
• W4297192968 cites W1983364832 @default.
• W4297192968 cites W2037328426 @default.
• W4297192968 cites W2069231830 @default.
• W4297192968 cites W2158787690 @default.
• W4297192968 cites W2500751094 @default.
• W4297192968 cites W2508760196 @default.
• W4297192968 cites W2558748708 @default.
• W4297192968 cites W2572303978 @default.
• W4297192968 cites W2579874689 @default.
• W4297192968 cites W2595650414 @default.
• W4297192968 cites W2601564443 @default.
• W4297192968 cites W2775507709 @default.
• W4297192968 cites W2789643644 @default.
• W4297192968 cites W2793941577 @default.
• W4297192968 cites W2804902458 @default.
• W4297192968 cites W2892621946 @default.
• W4297192968 cites W2896826789 @default.
• W4297192968 cites W2914331134 @default.
• W4297192968 cites W2915459603 @default.
• W4297192968 cites W2943849814 @default.
• W4297192968 cites W2946817927 @default.
• W4297192968 cites W2963790755 @default.
• W4297192968 cites W2991494819 @default.
• W4297192968 cites W3007080761 @default.
• W4297192968 cites W3028306149 @default.
• W4297192968 cites W3047443805 @default.
• W4297192968 cites W3049655825 @default.
• W4297192968 cites W3106439716 @default.
• W4297192968 cites W3132173102 @default.
• W4297192968 cites W3199740234 @default.
• W4297192968 cites W4210257598 @default.
• W4297192968 doi "https://doi.org/10.1049/ipr2.12632" @default.
• W4297192968 hasPublicationYear "2022" @default.
• W4297192968 type Work @default.
• W4297192968 citedByCount "3" @default.
• W4297192968 countsByYear W42971929682023 @default.
• W4297192968 crossrefType "journal-article" @default.
• W4297192968 hasAuthorship W4297192968A5002894905 @default.
• W4297192968 hasAuthorship W4297192968A5014128597 @default.
• W4297192968 hasBestOaLocation W42971929681 @default.
• W4297192968 hasConcept C110484373 @default.
• W4297192968 hasConcept C11413529 @default.
• W4297192968 hasConcept C132525143 @default.
• W4297192968 hasConcept C138885662 @default.
• W4297192968 hasConcept C153180895 @default.
• W4297192968 hasConcept C154945302 @default.
• W4297192968 hasConcept C159078339 @default.
• W4297192968 hasConcept C179799912 @default.
• W4297192968 hasConcept C180356752 @default.
• W4297192968 hasConcept C2776401178 @default.
• W4297192968 hasConcept C41008148 @default.
• W4297192968 hasConcept C41895202 @default.
• W4297192968 hasConcept C45347329 @default.
• W4297192968 hasConcept C48044578 @default.
• W4297192968 hasConcept C50644808 @default.
• W4297192968 hasConcept C52622490 @default.
• W4297192968 hasConcept C77088390 @default.
• W4297192968 hasConcept C80444323 @default.
• W4297192968 hasConcept C81363708 @default.
• W4297192968 hasConceptScore W4297192968C110484373 @default.
• W4297192968 hasConceptScore W4297192968C11413529 @default.
• W4297192968 hasConceptScore W4297192968C132525143 @default.
• W4297192968 hasConceptScore W4297192968C138885662 @default.
• W4297192968 hasConceptScore W4297192968C153180895 @default.
• W4297192968 hasConceptScore W4297192968C154945302 @default.
• W4297192968 hasConceptScore W4297192968C159078339 @default.
• W4297192968 hasConceptScore W4297192968C179799912 @default.
• W4297192968 hasConceptScore W4297192968C180356752 @default.
• W4297192968 hasConceptScore W4297192968C2776401178 @default.
• W4297192968 hasConceptScore W4297192968C41008148 @default.
• W4297192968 hasConceptScore W4297192968C41895202 @default.
• W4297192968 hasConceptScore W4297192968C45347329 @default.
• W4297192968 hasConceptScore W4297192968C48044578 @default.
• W4297192968 hasConceptScore W4297192968C50644808 @default.
• W4297192968 hasConceptScore W4297192968C52622490 @default.
• W4297192968 hasConceptScore W4297192968C77088390 @default.
• W4297192968 hasConceptScore W4297192968C80444323 @default.
• W4297192968 hasConceptScore W4297192968C81363708 @default.
• W4297192968 hasIssue "1" @default.
• W4297192968 hasLocation W42971929681 @default.
• W4297192968 hasOpenAccess W4297192968 @default.
• W4297192968 hasPrimaryLocation W42971929681 @default.
• W4297192968 hasRelatedWork W1993337810 @default.
• W4297192968 hasRelatedWork W1997912302 @default.
• W4297192968 hasRelatedWork W2028628118 @default.
• W4297192968 hasRelatedWork W2295021132 @default.
• W4297192968 hasRelatedWork W2546942002 @default.
• W4297192968 hasRelatedWork W2781623059 @default.
• W4297192968 hasRelatedWork W3173596272 @default.

• next