Remote Sensing Technology and Application, Volume. 39, Issue 3, 527(2024)

Topic Model for High Resolution Remote Sensing Data Interpretation: A Review

Zhen LI, Qiqi ZHU*, Yang LEI, Jiangqin WAN, Linlin WANG, and Lei XU
Author Affiliations
  • School of Geography and Information Engineering, China University of Geosciences, National Engineering Research Center of GIS, China University of Geosciences, Wuhan, China
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    References(45)

    [1] D M BLEI, A Y NG, M I Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022(2003).

    [2] Q Q Xie, P Tiwari, D Gupta et al. Neural variational sparse topic model for sparse explainable text representation. Information Processing & Management, 58, 102614(2021).

    [3] Zhixue WANG. Research on topic model text classification based on graph convolutional neural network. Technology Innovation and Application, 13, 83-86(2023).

    [4] Q Q ZHU, Y LEI, X L SUN et al. Knowledge-guided land pattern depiction for urban land use mapping: A case study of Chinese cities. Remote Sensing of Environment, 272, 112916(2022).

    [5] Shichuan LIU. Exploration and practice on building recognition method of high resolution remote sensing image based on topic model. Geomatics & Spatial Information Technology, 45, 135-138(2022).

    [6] Deren LI. Towards geo-spatial information science in big data era. Acta Geodaetica et Cartographica Sinica, 45, 379-384(2016).

    [7] Q Y LI, R F ZHONG, X DU et al. TransUNetCD: A hybrid transformer network for change detection in optical remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-19(2022).

    [8] L P ZHANG, L F ZHANG, B DU. Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4, 22-40(2016).

    [9] X M LI, Y F ZHONG, Yu SU et al. Scene-change detection based on multi-feature-fusion latent dirichlet allocation model for high-spatial-resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 87, 669-681(2021).

    [10] J A G JARAMAGO, M E PAOLETTI, J M HAUT et al. GPU parallel implementation of dual-depth sparse probabilistic latent semantic analysis for hyperspectral unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 3156-3167(2019).

    [11] Q Q ZHU, X L SUN. ZHONG Y F, 2019-2019(2019).

    [12] B LIU, H Y WANG, K Z WANG et al. Scene interpretation for SAR images using supervised topic models;Proceedings of the 2011(2011).

    [13] J S HUANG, X D GAO, W P JING. Research on remote sen-sing change monitoring of urban land types based on BOVW and SVM. Journal of Nanjing Forestry University, 47, 37-44(2023).

    [14] P QUELHAS, F MONAY, J M ODOBEZ et al. A thousand words in a scene. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 1575-1589(2007).

    [15] T HOFMANN. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42, 177-196(2001).

    [16] Yong MA. Research on intelligent evaluation system of sports training based on video image acquisition and scene semantics. Advances in Multimedia, 2022, 4726450(2022).

    [17] Y M LIU, M CHEN. The knowledge structure and development trend in artificial intelligence based on latent feature topic model. IEEE Transactions on Engineering Management, 1-12(2023).

    [18] Y F ZHONG, Q Q ZHU, L P ZHANG. Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery. IEEE Tran-sactions on Geoscience and Remote Sensing, 53, 6207-6222(2015).

    [19] K THAN, T B HO. Fully sparse topic models.

    [20] K L CLARKSON. Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm. ACM Transactions on Algorithms (TALG), 6, 1-30(2010).

    [21] Y MIAO, E GREFENSTETTE, P BLUNSOM. Discovering discrete latent topics with neural variational inference.

    [22] [22] DINGR, NALLAPATIRAMESH , XIANGB. Coherence-aware neural topic modeling[S]. arXiv preprint arXiv:180902687,2018. DOI: 10.48550/arXiv.1809.02687

    [23] I GEMP, R NALLAPATI, R DING et al. Weakly semi-supervised neural topic models(2019).

    [24] Y YANG, K P ZHANG, Y Y FAN. SDTM:A supervised bayesian deep topic model for text analytics. Information Systems Research, 34, 137-156(2023).

    [25] X FU, K J HUANG, N D SIDIROPOULOS et al. Anchor-free correlated topic modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 1056-1071(2018).

    [26] H B ZHANG, S HUATING, X Y WU. Topic model for graph mining based on hierarchical dirichlet process. Statistical Theory and Related Fields, 4, 66-77(2020).

    [27] S J BLAIR, Y BI, M D MULVENNA. Aggregated topic models for increasing social media topic coherence. Applied Intelligence, 50, 138-156(2020).

    [28] Ziwei GAO, Weiwei SUN, Penggen CHENG et al. Identify urban functional zones using multi feature latent semantic fused information of high-spatial resolution remote sensing image and POI data. Remote Sensing Technology and Application, 36, 618-626(2021).

    [29] H TANG, L SHEN, YF QI et al. A multiscale latent Dirichlet allocation model for object-oriented clustering of VHR panchromatic satellite images. IEEE Transactions on Geoscience and Remote Sensing, 51, 1680-1692(2012).

    [30] T ZHANG, W J YAN, C M SU et al. Accurate object retrieval for high-resolution remote-sensing imagery using high-order topic consistency potentials. International Journal of Remote Sensing, 36, 4250-4273(2015).

    [31] Z Z KANG, J T YANG. A probabilistic graphical model for the classification of mobile LiDAR point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 143, 108-123(2018).

    [32] R FERNANDEZ-BELTRAN, J M HAUT, M E PAOLETTI et al. Remote sensing image fusion using hierarchical multimodal probabilistic latent semantic analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 4982-4993(2018).

    [33] G CHENG, L GUO, T Y ZHAO et al. Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA. International Journal of Remote Sensing, 34, 45-59(2013).

    [34] G POLATKAN, M ZHOU, L CARIN et al. A Bayesian nonparametric approach to image super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 346-358(2014).

    [35] A L MA, Y T WAN, Y F ZHONG et al. SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search. ISPRS Journal of Photogrammetry and Remote Sensing, 172, 171-188(2021).

    [36] W MIAO, J GENG, W JIANG. Multigranularity decoupling network with pseudolabel selection for remote sensing image scene classification. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-13(2023).

    [37] M LIENOU, H MAITRE, M DATCU. Semantic annotation of satellite images using latent dirichlet allocation. IEEE Geoscience and Remote Sensing Letters, 7, 28-32(2009).

    [38] Q Q ZHU, Y F ZHONG, L P ZHANG et al. Scene classification based on the fully sparse semantic topic model. IEEE Transactions on Geoscience and Remote Sensing, 55, 5525-5538(2017).

    [39] F HU, G S XIA, W YANG et al. Mining deep semantic representations for scene classification of high-resolution remote sensing imagery. IEEE Transactions on Big Data, 6, 522-536(2019).

    [40] Y LIU, D MINH NGUYEN, N DELIGIANNIS et al. Hourglass-shapenetwork based semantic segmentation for high resolution aerial imagery. Remote Sensing, 9, 522(2017).

    [41] L SHEN, H TANG, Y H CHEN et al. A semisupervised latent dirichlet allocation model for object-based classification of VHR panchromatic satellite images. IEEE Geoscience and Remote Sensing Letters, 11, 863-867(2013).

    [42] A BOSCH, A ZISSERMAN, X MUNOZ. Scene classification using a hybrid generative/discriminative approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 712-727(2008).

    [43] Rui XIAO, Yuxiang GUO, Xinghua LI. Dynamic semantic extraction of urban blocks activity based on topic model. Remote Sensing Technology and Application, 38, 649-661(2023).

    [44] A RAJAGOPAL, G P JOSHI, A RAMACHANDRAN et al. A deep learning model based on multi-objective particle swarm optimization for scene classification in unmanned aerial vehicles. IEEE Access, 8, 135383-135393(2020).

    [45] Q Q ZHU, Y F ZHONG, L P ZHANG et al. Adaptive deep sparse semantic modeling framework for high spatial resolution image scene classification. IEEE Transactions on Geoscience and Remote Sensing, 56, 6180-6195(2018).

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    Zhen LI, Qiqi ZHU, Yang LEI, Jiangqin WAN, Linlin WANG, Lei XU. Topic Model for High Resolution Remote Sensing Data Interpretation: A Review[J]. Remote Sensing Technology and Application, 2024, 39(3): 527

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    Paper Information

    Category:

    Received: Sep. 11, 2020

    Accepted: --

    Published Online: Dec. 9, 2024

    The Author Email: ZHU Qiqi (zhuqq@cug.edu.cn)

    DOI:10.11873/j.issn.1004-0323.2024.3.0527

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