Matches in SemOpenAlex for { <https://semopenalex.org/work/W2896834648> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W2896834648 abstract "Algorithms for automatic semantic segmentation of the satellite images provide an effective approach for the generation of vector maps. Convolutional neural networks (CNN) have achieved the state-of-the-art quality of the output segmentation on the satellite images-to-semantic labels task. However, the generalization ability of such methods is not sufficient to process the satellite images that were captured in the different area or during the different season. Recently, the Generative Adversarial Networks (GAN) were introduced that can overcome the overfitting using the adversarial loss. This paper is focused on the development of the new GAN model for effective semantic segmentation of multispectral satellite images. The pix2pix1 model is used as the starting point of the research. It is trained in the semi-supervised setting on the aligned pairs of images. The perceptual validation has demonstrated the high quality of the output labels. The evaluation on the independent test dataset has proved the robustness of GANs on the task of semantic segmentation of multispectral satellite images." @default.
- W2896834648 created "2018-10-26" @default.
- W2896834648 creator A5065934992 @default.
- W2896834648 date "2018-10-09" @default.
- W2896834648 modified "2023-09-26" @default.
- W2896834648 title "Conditional GANs for semantic segmentation of multispectral satellite images" @default.
- W2896834648 cites W1571998429 @default.
- W2896834648 cites W1912954554 @default.
- W2896834648 cites W2000803298 @default.
- W2896834648 cites W2031489346 @default.
- W2896834648 cites W2063396028 @default.
- W2896834648 cites W2066916495 @default.
- W2896834648 cites W2079528439 @default.
- W2896834648 cites W2088805832 @default.
- W2896834648 cites W2102150301 @default.
- W2896834648 cites W2108598243 @default.
- W2896834648 cites W2113940248 @default.
- W2896834648 cites W2248723555 @default.
- W2896834648 cites W2615237590 @default.
- W2896834648 cites W2738232694 @default.
- W2896834648 cites W2963881378 @default.
- W2896834648 doi "https://doi.org/10.1117/12.2325601" @default.
- W2896834648 hasPublicationYear "2018" @default.
- W2896834648 type Work @default.
- W2896834648 sameAs 2896834648 @default.
- W2896834648 citedByCount "9" @default.
- W2896834648 countsByYear W28968346482019 @default.
- W2896834648 countsByYear W28968346482020 @default.
- W2896834648 countsByYear W28968346482021 @default.
- W2896834648 countsByYear W28968346482022 @default.
- W2896834648 crossrefType "proceedings-article" @default.
- W2896834648 hasAuthorship W2896834648A5065934992 @default.
- W2896834648 hasConcept C124504099 @default.
- W2896834648 hasConcept C127313418 @default.
- W2896834648 hasConcept C127413603 @default.
- W2896834648 hasConcept C146978453 @default.
- W2896834648 hasConcept C154945302 @default.
- W2896834648 hasConcept C173163844 @default.
- W2896834648 hasConcept C19269812 @default.
- W2896834648 hasConcept C31972630 @default.
- W2896834648 hasConcept C41008148 @default.
- W2896834648 hasConcept C62649853 @default.
- W2896834648 hasConcept C89600930 @default.
- W2896834648 hasConceptScore W2896834648C124504099 @default.
- W2896834648 hasConceptScore W2896834648C127313418 @default.
- W2896834648 hasConceptScore W2896834648C127413603 @default.
- W2896834648 hasConceptScore W2896834648C146978453 @default.
- W2896834648 hasConceptScore W2896834648C154945302 @default.
- W2896834648 hasConceptScore W2896834648C173163844 @default.
- W2896834648 hasConceptScore W2896834648C19269812 @default.
- W2896834648 hasConceptScore W2896834648C31972630 @default.
- W2896834648 hasConceptScore W2896834648C41008148 @default.
- W2896834648 hasConceptScore W2896834648C62649853 @default.
- W2896834648 hasConceptScore W2896834648C89600930 @default.
- W2896834648 hasLocation W28968346481 @default.
- W2896834648 hasOpenAccess W2896834648 @default.
- W2896834648 hasPrimaryLocation W28968346481 @default.
- W2896834648 hasRelatedWork W1669643531 @default.
- W2896834648 hasRelatedWork W1700740617 @default.
- W2896834648 hasRelatedWork W1721780360 @default.
- W2896834648 hasRelatedWork W2110230079 @default.
- W2896834648 hasRelatedWork W2117664411 @default.
- W2896834648 hasRelatedWork W2117933325 @default.
- W2896834648 hasRelatedWork W2122581818 @default.
- W2896834648 hasRelatedWork W2159066190 @default.
- W2896834648 hasRelatedWork W2739874619 @default.
- W2896834648 hasRelatedWork W1967061043 @default.
- W2896834648 isParatext "false" @default.
- W2896834648 isRetracted "false" @default.
- W2896834648 magId "2896834648" @default.
- W2896834648 workType "article" @default.