FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3148263657> ?p ?o ?g. }

• W3148263657 endingPage "4114" @default.
• W3148263657 startingPage "4101" @default.
• W3148263657 abstract "Convolutional neural networks (CNNs) can automatically learn features from the hyperspectral image (HSI) data, avoiding the difficulty of manually extracting features. However, the number of training samples for the classification of HSIs is always limited, making it difficult for CNN to obtain effective features and resulting in low classification accuracy. To solve this problem, a pixel cluster CNN and spectral-spatial fusion (SSF) algorithm for hyperspectral image classification with small-size training samples is proposed in this article. First, spatial information is extracted by the gray level co-occurrence matrix. Then, spatial information and spectral information are fused by means of bands superposition, forming spectral-spatial features. To expand the number of training samples, the pixels after SSF are combined into pixel clusters according to a certain rule. Finally, a CNN framework is utilized to extract effective features from the pixel clusters. Experiments based on three standard HSIs demonstrate that the proposed algorithm can get better performance than the conventional CNN and also outperforms other studied algorithms in the case of small training set." @default.
• W3148263657 created "2021-04-13" @default.
• W3148263657 creator A5008256662 @default.
• W3148263657 creator A5021408538 @default.
• W3148263657 creator A5028522401 @default.
• W3148263657 creator A5044570096 @default.
• W3148263657 creator A5066378186 @default.
• W3148263657 creator A5068336830 @default.
• W3148263657 date "2021-01-01" @default.
• W3148263657 modified "2023-10-15" @default.
• W3148263657 title "A Pixel Cluster CNN and Spectral-Spatial Fusion Algorithm for Hyperspectral Image Classification With Small-Size Training Samples" @default.
• W3148263657 cites W1516724916 @default.
• W3148263657 cites W1966580635 @default.
• W3148263657 cites W2031510368 @default.
• W3148263657 cites W2044465660 @default.
• W3148263657 cites W2076777984 @default.
• W3148263657 cites W2095443887 @default.
• W3148263657 cites W2097092275 @default.
• W3148263657 cites W2113003778 @default.
• W3148263657 cites W2131864940 @default.
• W3148263657 cites W2165049595 @default.
• W3148263657 cites W2169500530 @default.
• W3148263657 cites W2548791488 @default.
• W3148263657 cites W2767805377 @default.
• W3148263657 cites W2768537477 @default.
• W3148263657 cites W2782517840 @default.
• W3148263657 cites W2943270518 @default.
• W3148263657 cites W2945989246 @default.
• W3148263657 cites W2946655868 @default.
• W3148263657 cites W2946747211 @default.
• W3148263657 cites W2953926847 @default.
• W3148263657 cites W2956367483 @default.
• W3148263657 cites W2957718075 @default.
• W3148263657 cites W2984140474 @default.
• W3148263657 cites W2985701536 @default.
• W3148263657 cites W2989871747 @default.
• W3148263657 cites W2992919850 @default.
• W3148263657 cites W3005414792 @default.
• W3148263657 cites W3006226691 @default.
• W3148263657 cites W3010420609 @default.
• W3148263657 cites W3014465379 @default.
• W3148263657 cites W3023351371 @default.
• W3148263657 cites W3032070687 @default.
• W3148263657 cites W3034953390 @default.
• W3148263657 cites W3041591456 @default.
• W3148263657 cites W3043713335 @default.
• W3148263657 cites W3046007115 @default.
• W3148263657 cites W3046340269 @default.
• W3148263657 cites W3047443805 @default.
• W3148263657 cites W3048203336 @default.
• W3148263657 cites W3081287597 @default.
• W3148263657 cites W3088464175 @default.
• W3148263657 cites W3090232286 @default.
• W3148263657 cites W3092171629 @default.
• W3148263657 cites W3092372960 @default.
• W3148263657 cites W3093562283 @default.
• W3148263657 cites W3104795559 @default.
• W3148263657 doi "https://doi.org/10.1109/jstars.2021.3068864" @default.
• W3148263657 hasPublicationYear "2021" @default.
• W3148263657 type Work @default.
• W3148263657 sameAs 3148263657 @default.
• W3148263657 citedByCount "20" @default.
• W3148263657 countsByYear W31482636572021 @default.
• W3148263657 countsByYear W31482636572022 @default.
• W3148263657 countsByYear W31482636572023 @default.
• W3148263657 crossrefType "journal-article" @default.
• W3148263657 hasAuthorship W3148263657A5008256662 @default.
• W3148263657 hasAuthorship W3148263657A5021408538 @default.
• W3148263657 hasAuthorship W3148263657A5028522401 @default.
• W3148263657 hasAuthorship W3148263657A5044570096 @default.
• W3148263657 hasAuthorship W3148263657A5066378186 @default.
• W3148263657 hasAuthorship W3148263657A5068336830 @default.
• W3148263657 hasBestOaLocation W31482636571 @default.
• W3148263657 hasConcept C105795698 @default.
• W3148263657 hasConcept C11413529 @default.
• W3148263657 hasConcept C115961682 @default.
• W3148263657 hasConcept C121332964 @default.
• W3148263657 hasConcept C153180895 @default.
• W3148263657 hasConcept C154945302 @default.
• W3148263657 hasConcept C159078339 @default.
• W3148263657 hasConcept C159620131 @default.
• W3148263657 hasConcept C160633673 @default.
• W3148263657 hasConcept C183852935 @default.
• W3148263657 hasConcept C33390570 @default.
• W3148263657 hasConcept C33923547 @default.
• W3148263657 hasConcept C41008148 @default.
• W3148263657 hasConcept C62520636 @default.
• W3148263657 hasConcept C75294576 @default.
• W3148263657 hasConcept C81363708 @default.
• W3148263657 hasConceptScore W3148263657C105795698 @default.
• W3148263657 hasConceptScore W3148263657C11413529 @default.
• W3148263657 hasConceptScore W3148263657C115961682 @default.
• W3148263657 hasConceptScore W3148263657C121332964 @default.
• W3148263657 hasConceptScore W3148263657C153180895 @default.
• W3148263657 hasConceptScore W3148263657C154945302 @default.
• W3148263657 hasConceptScore W3148263657C159078339 @default.
• W3148263657 hasConceptScore W3148263657C159620131 @default.
• W3148263657 hasConceptScore W3148263657C160633673 @default.

• next