Representation of Multirelations of Geographic Scenes Based on Hyperbolic Space

The recognition of complex geographic scenes requires to simultaneously describe different kinds of relations such as hyponymy and spatial relations among multiple-scale categories. The former is hierarchical structure, and the latter is called eigen structure. Current researches focus on representi...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 62; s. 1 - 13
Hlavní autoři:	Cui, Wei, Xu, Xing, Chen, Jiale, Feng, Zhanyun, Zhao, Huilin, Hao, Yuanjie, Tian, Yueling, Wang, Jin, Xia, Cong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial neural networks Computational modeling Embedding Euclidean geometry Euclidean space Feature extraction Geographic scenes Geometric transformation Graph representations Graphical representations Hyperbolic coordinates hyperbolic space Knowledge graphs Knowledge representation Machine learning multirelations Neural networks Object recognition Quaternions Remote sensing Semantic segmentation Semantics Vectors Visualization
ISSN:	0196-2892, 1558-0644
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Abstract	The recognition of complex geographic scenes requires to simultaneously describe different kinds of relations such as hyponymy and spatial relations among multiple-scale categories. The former is hierarchical structure, and the latter is called eigen structure. Current researches focus on representing these relations in Euclidean space. However, the key characteristic of the Euclidean spaces is that it expands polynomially with respect to the radius, which makes it difficult to describe both the hierarchical and eigen structure within finite computable dimensions simultaneously. To address the above issues, a hyperbolic embedding model for representing complex geographic scenes (HEMR) is first proposed. First, the visual features of remote sensing objects are extracted through a deep neural network in Euclidean space and mapped onto the n-dimensional Poincare ball in hyperbolic space by an exponential projection. Second, a two-stage contrastive learning mechanism based on Möbius transformation is designed, which uses the property of exponential expansion to obtain the ability to represent different kinds of relations. Finally, a new quaternion loss is designed to describe the relations between feature categories in remote sensing scenarios by hyperbolic distance. The experiments indicate that our model can leverage the characteristic of spatial exponential growth with the radius in hyperbolic space, simultaneously describing the hierarchical and eigen structures of complex remote sensing scenes in finite dimensions. This provides support for downstream tasks such as semantic segmentation of remote sensing, knowledge graph representation, and link prediction.
AbstractList	The recognition of complex geographic scenes requires to simultaneously describe different kinds of relations such as hyponymy and spatial relations among multiple-scale categories. The former is hierarchical structure, and the latter is called eigen structure. Current researches focus on representing these relations in Euclidean space. However, the key characteristic of the Euclidean spaces is that it expands polynomially with respect to the radius, which makes it difficult to describe both the hierarchical and eigen structure within finite computable dimensions simultaneously. To address the above issues, a hyperbolic embedding model for representing complex geographic scenes (HEMR) is first proposed. First, the visual features of remote sensing objects are extracted through a deep neural network in Euclidean space and mapped onto the n-dimensional Poincare ball in hyperbolic space by an exponential projection. Second, a two-stage contrastive learning mechanism based on Möbius transformation is designed, which uses the property of exponential expansion to obtain the ability to represent different kinds of relations. Finally, a new quaternion loss is designed to describe the relations between feature categories in remote sensing scenarios by hyperbolic distance. The experiments indicate that our model can leverage the characteristic of spatial exponential growth with the radius in hyperbolic space, simultaneously describing the hierarchical and eigen structures of complex remote sensing scenes in finite dimensions. This provides support for downstream tasks such as semantic segmentation of remote sensing, knowledge graph representation, and link prediction.
Author	Cui, Wei Zhao, Huilin Tian, Yueling Hao, Yuanjie Wang, Jin Xu, Xing Chen, Jiale Feng, Zhanyun Xia, Cong
Author_xml	– sequence: 1 givenname: Wei surname: Cui fullname: Cui, Wei email: cuiwei@whut.edu.cn organization: School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China – sequence: 2 givenname: Xing orcidid: 0000-0002-2310-4385 surname: Xu fullname: Xu, Xing email: xuxing@whut.edu.cn organization: School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China – sequence: 3 givenname: Jiale surname: Chen fullname: Chen, Jiale email: chenjl07@whut.edu.cn organization: School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China – sequence: 4 givenname: Zhanyun surname: Feng fullname: Feng, Zhanyun email: 276762@whut.edu.cn organization: School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China – sequence: 5 givenname: Huilin orcidid: 0000-0002-2843-1123 surname: Zhao fullname: Zhao, Huilin email: zhaohl2016@whut.edu.cn organization: School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China – sequence: 6 givenname: Yuanjie surname: Hao fullname: Hao, Yuanjie email: haoyuanjie@whut.edu.cn organization: School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China – sequence: 7 givenname: Yueling surname: Tian fullname: Tian, Yueling email: tianyueling@whut.edu.cn organization: School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China – sequence: 8 givenname: Jin surname: Wang fullname: Wang, Jin email: 0121608900228@whut.edu.cn organization: School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China – sequence: 9 givenname: Cong surname: Xia fullname: Xia, Cong email: 265107@whut.edu.cn organization: School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China
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References	ref13 Hamilton (ref7); 30 ref12 ref15 ref37 ref14 ref31 ref30 ref11 ref33 ref10 ref32 ref2 Dosovitskiy (ref36) 2020 ref1 ref39 ref38 Hian Lee (ref24) 2023 ref18 Deng (ref23) 2023 Nickel (ref17) Yang (ref20) 2022 Velickovic (ref6) 2017; 1050 Gu (ref35) ref25 ref22 ref21 ref28 ref27 Balazevic (ref16); 32 Nickel (ref19); 80 ref29 ref8 ref9 Bai (ref26) 2021 ref4 ref3 Ganea (ref34) ref5 ref40 McInnes (ref41) 2018
References_xml	– ident: ref11 doi: 10.3390/rs13071312 – ident: ref13 doi: 10.1016/j.isprsjprs.2022.01.009 – ident: ref1 doi: 10.1609/aaai.v28i1.8870 – ident: ref5 doi: 10.48550/ARXIV.1609.02907 – start-page: 6341 volume-title: Proc. 31st Int. Conf. Neural Inf. Process. Syst. ident: ref17 article-title: Poincaré embeddings for learning hierarchical representations – year: 2020 ident: ref36 article-title: An image is worth 16×16 words: Transformers for image recognition at scale publication-title: arXiv:2010.11929 – volume: 30 start-page: 1025 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref7 article-title: Inductive representation learning on large graphs – volume: 80 start-page: 3776 volume-title: Proc. 35th Int. Conf. Mach. Learn. ident: ref19 article-title: Learning continuous hierarchies in the Lorentz model of hyperbolic geometry – ident: ref25 doi: 10.1145/3583780.3614979 – ident: ref3 doi: 10.1145/2623330.2623732 – ident: ref9 doi: 10.1016/j.rse.2022.112916 – ident: ref31 doi: 10.3390/rs15040880 – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent. ident: ref35 article-title: Learning mixed-curvature representations in product spaces – year: 2023 ident: ref24 article-title: Node-specific space selection via localized geometric hyperbolicity in graph neural networks publication-title: arXiv:2303.01724 – ident: ref39 doi: 10.1109/TAC.2013.2254619 – start-page: 1646 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref34 article-title: Hyperbolic entailment cones for learning hierarchical embeddings – ident: ref14 doi: 10.3390/rs11010083 – volume: 32 start-page: 1 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref16 article-title: Multi-relational Poincaré graph embeddings – year: 2021 ident: ref26 article-title: Modeling heterogeneous hierarchies with relation-specific hyperbolic cones publication-title: arXiv:2110.14923 – year: 2022 ident: ref20 article-title: Hyperbolic curvature graph neural network publication-title: arXiv:2212.01793 – year: 2023 ident: ref23 article-title: FMGNN: Fused manifold graph neural network publication-title: arXiv:2304.01081 – ident: ref32 doi: 10.3390/ijgi10010032 – ident: ref40 doi: 10.1109/CSPC.2017.8305844 – ident: ref21 doi: 10.1016/j.neucom.2022.04.026 – ident: ref22 doi: 10.1016/j.ins.2022.07.060 – ident: ref2 doi: 10.1609/aaai.v29i1.9491 – ident: ref15 doi: 10.1109/MSP.2017.2693418 – ident: ref10 doi: 10.1016/j.inffus.2023.101909 – ident: ref4 doi: 10.1145/2939672.2939754 – ident: ref18 doi: 10.1109/tbdata.2021.3081431 – ident: ref38 doi: 10.1109/CVPR.2015.7298682 – ident: ref37 doi: 10.3390/rs9030243 – year: 2018 ident: ref41 article-title: UMAP: Uniform manifold approximation and projection for dimension reduction publication-title: arXiv:1802.03426 – ident: ref28 doi: 10.1109/TGRS.2020.3016020 – ident: ref12 doi: 10.3390/rs14194872 – ident: ref27 doi: 10.18653/v1/2020.acl-main.617 – ident: ref29 doi: 10.1109/TGRS.2021.3073269 – ident: ref30 doi: 10.3390/rs14051161 – volume: 1050 start-page: 48550 issue: 20 year: 2017 ident: ref6 article-title: Graph attention networks publication-title: Stat – ident: ref8 doi: 10.1016/j.isprsjprs.2021.03.016 – ident: ref33 doi: 10.3390/rs13214201
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SubjectTerms	Artificial neural networks Computational modeling Embedding Euclidean geometry Euclidean space Feature extraction Geographic scenes Geometric transformation Graph representations Graphical representations Hyperbolic coordinates hyperbolic space Knowledge graphs Knowledge representation Machine learning multirelations Neural networks Object recognition Quaternions Remote sensing Semantic segmentation Semantics Vectors Visualization
Title	Representation of Multirelations of Geographic Scenes Based on Hyperbolic Space
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