Matches in SemOpenAlex for { <https://semopenalex.org/work/W2976820585> ?p ?o ?g. }
- W2976820585 endingPage "2220" @default.
- W2976820585 startingPage "2220" @default.
- W2976820585 abstract "Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straightforward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification." @default.
- W2976820585 created "2019-10-03" @default.
- W2976820585 creator A5020896207 @default.
- W2976820585 creator A5031003824 @default.
- W2976820585 creator A5036065638 @default.
- W2976820585 creator A5066378186 @default.
- W2976820585 creator A5084316677 @default.
- W2976820585 creator A5089575397 @default.
- W2976820585 date "2019-09-23" @default.
- W2976820585 modified "2023-10-15" @default.
- W2976820585 title "Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification" @default.
- W2976820585 cites W2052160904 @default.
- W2976820585 cites W2132525626 @default.
- W2976820585 cites W2313466110 @default.
- W2976820585 cites W2321627895 @default.
- W2976820585 cites W2412782625 @default.
- W2976820585 cites W2502502269 @default.
- W2976820585 cites W2548791488 @default.
- W2976820585 cites W2577238056 @default.
- W2976820585 cites W2586793539 @default.
- W2976820585 cites W2587790406 @default.
- W2976820585 cites W2592224809 @default.
- W2976820585 cites W2732412926 @default.
- W2976820585 cites W2761781479 @default.
- W2976820585 cites W2763731268 @default.
- W2976820585 cites W2764205729 @default.
- W2976820585 cites W2764276316 @default.
- W2976820585 cites W2767805377 @default.
- W2976820585 cites W2768038330 @default.
- W2976820585 cites W2770315464 @default.
- W2976820585 cites W2775507709 @default.
- W2976820585 cites W2777427437 @default.
- W2976820585 cites W2782051899 @default.
- W2976820585 cites W2782772130 @default.
- W2976820585 cites W2804132729 @default.
- W2976820585 cites W2805177060 @default.
- W2976820585 cites W2887785636 @default.
- W2976820585 cites W2890945279 @default.
- W2976820585 cites W2896826789 @default.
- W2976820585 cites W2907100627 @default.
- W2976820585 cites W2908833896 @default.
- W2976820585 cites W2908955282 @default.
- W2976820585 cites W2909158354 @default.
- W2976820585 cites W2912371366 @default.
- W2976820585 cites W2914677397 @default.
- W2976820585 cites W2919509228 @default.
- W2976820585 cites W2928260953 @default.
- W2976820585 cites W2937675449 @default.
- W2976820585 cites W2940678725 @default.
- W2976820585 cites W2941387379 @default.
- W2976820585 cites W2942170965 @default.
- W2976820585 cites W2943270518 @default.
- W2976820585 cites W2944653015 @default.
- W2976820585 cites W2944896556 @default.
- W2976820585 cites W2945989246 @default.
- W2976820585 cites W3101640299 @default.
- W2976820585 cites W3103753223 @default.
- W2976820585 cites W3105357426 @default.
- W2976820585 cites W4240485910 @default.
- W2976820585 doi "https://doi.org/10.3390/rs11192220" @default.
- W2976820585 hasPublicationYear "2019" @default.
- W2976820585 type Work @default.
- W2976820585 sameAs 2976820585 @default.
- W2976820585 citedByCount "26" @default.
- W2976820585 countsByYear W29768205852020 @default.
- W2976820585 countsByYear W29768205852021 @default.
- W2976820585 countsByYear W29768205852022 @default.
- W2976820585 countsByYear W29768205852023 @default.
- W2976820585 crossrefType "journal-article" @default.
- W2976820585 hasAuthorship W2976820585A5020896207 @default.
- W2976820585 hasAuthorship W2976820585A5031003824 @default.
- W2976820585 hasAuthorship W2976820585A5036065638 @default.
- W2976820585 hasAuthorship W2976820585A5066378186 @default.
- W2976820585 hasAuthorship W2976820585A5084316677 @default.
- W2976820585 hasAuthorship W2976820585A5089575397 @default.
- W2976820585 hasBestOaLocation W29768205851 @default.
- W2976820585 hasConcept C114614502 @default.
- W2976820585 hasConcept C115961682 @default.
- W2976820585 hasConcept C127313418 @default.
- W2976820585 hasConcept C138885662 @default.
- W2976820585 hasConcept C153180895 @default.
- W2976820585 hasConcept C154945302 @default.
- W2976820585 hasConcept C159078339 @default.
- W2976820585 hasConcept C159620131 @default.
- W2976820585 hasConcept C2524010 @default.
- W2976820585 hasConcept C2776401178 @default.
- W2976820585 hasConcept C2777210771 @default.
- W2976820585 hasConcept C2780757906 @default.
- W2976820585 hasConcept C33923547 @default.
- W2976820585 hasConcept C41008148 @default.
- W2976820585 hasConcept C41895202 @default.
- W2976820585 hasConcept C62649853 @default.
- W2976820585 hasConcept C75294576 @default.
- W2976820585 hasConcept C81363708 @default.
- W2976820585 hasConcept C95623464 @default.
- W2976820585 hasConceptScore W2976820585C114614502 @default.
- W2976820585 hasConceptScore W2976820585C115961682 @default.
- W2976820585 hasConceptScore W2976820585C127313418 @default.