Improved real-time intelligent recommendation method of remote sensing information in deep cross network

doi:10.11947/j.AGCS.2024.20230065

Abstract

Abstract: With the advent of the era of remote sensing big data, the problem of active and real-time push of massive remote sensing data has become a bottleneck limiting the development of remote sensing information intelligent services. Aiming at the problems of insufficient spatial feature expression ability, insufficient cross feature expression ability and non-discriminatory treatment of cross features in existing remote sensing information recommendation models, this paper proposes an attention deep cross spatial transformation network (ADCSTN) integrating attention mechanism. Firstly, the model uses deep cross network to extract the cross features of different associations of remote sensing information. Then, based on grid division, the model uses the spatial transformation layer to convert the one-dimensional spatial attribute data into two-dimensional spatial matrix, fully capturing the spatial structure characteristics of remote sensing information. Finally, the attention layer sets different weights for the different associated cross features to enhance the performance of the model and realize the active, real-time and intelligent push of remote sensing information. In this paper, the remote sensing satellite constellation composed of 1584 intelligent remote sensing satellites is simulated by STK to provide real-time remote sensing data for ships in the area of 20°N—40°N, 120°E—140°E, and set user interests to obtain the experimental data set. The experimental results show that the recommendation effect of the model in this paper is better. Compared with the traditional quad model, the F₁ score is increased by about 50%.

Key words: remote sensing information, recommendation system, deep crossing network, instant push, intelligent remote sensing satellite

CLC Number:

P237

PENG Ranshu, CHEN Shi, CHEN Yu. Improved real-time intelligent recommendation method of remote sensing information in deep cross network[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(3): 537-547.

References

[1] 王密, 杨芳. 智能遥感卫星与遥感影像实时服务[J]. 测绘学报, 2019, 48(12):1586-1594.DOI:10.11947/j.AGCS.2019.20190454. WANG Mi, YANG Fang. Intelligent remote sensing satellite and remote sensing image real-time service[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(12):1586-1594.DOI:10.11947/j.AGCS.2019.20190454.
[2] 李德仁, 丁霖, 邵振峰. 面向实时应用的遥感服务技术[J]. 遥感学报, 2021, 25(1):15-24. LI Deren, DING Lin, SHAO Zhenfeng. Application-oriented real-time remote sensing service technology[J]. National Remote Sensing Bulletin, 2021, 25(1):15-24.
[3] 程滔. 大数据时代遥感数据管理与服务模式探索[J]. 国土资源遥感, 2016, 28(4):202-206. CHENG Tao. Exploring management and service mode for remote sensing data in big data era[J]. Remote Sensing for Natural Resources, 2016, 28(4):202-206.
[4] 李德仁, 沈欣, 李迪龙, 等. 论军民融合的卫星通信、遥感、导航一体天基信息实时服务系统[J]. 武汉大学学报(信息科学版), 2017, 42(11):1501-1505. LI Deren, SHEN Xin, LI Dilong, et al. On civil-military integrated space-based real-time information service system[J]. Geomatics and Information Science of Wuhan University, 2017, 42(11):1501-1505.
[5] 李德仁, 沈欣. 我国天基信息实时智能服务系统发展战略研究[J]. 中国工程科学, 2020, 22(2):138-143. LI Deren, SHEN Xin. Research on the development strategy of real time and intelligent space-based information service system in China[J]. Strategic Study of CAE, 2020, 22(2):138-143.
[6] ZHU Jiefan, YOU Xiong, XIA Qing, et al. Object-oriented battlefieldenvironment simulation process object model based on task-driven[J].Journal of Geodesy and Geoinformation Science,2019,2(3):31-43.
[7] 柳林, 李德仁, 李万武, 等. 从地球空间信息学的角度对智慧地球的若干思考[J]. 武汉大学学报(信息科学版), 2012, 37(10):1248-1251. LIU Lin, LI Deren, LI Wanwu, et al. Thoughts on smarter planet from the view of geomatics[J]. Geomatics and Information Science of Wuhan University, 2012, 37(10):1248-1251.
[8] 吴巍. 我军新一代通信网络发展设想[J]. 中国电子科学研究院学报, 2007, 2(5):445-449, 463. WU Wei. Development assumption on Chinese army's new generation communication network[J]. Journal of China Academy of Electronics and Information Technology, 2007, 2(5):445-449, 463.
[9] DOUGLASS R J, MORK J, SURESH R. Battlefield awareness and data dissemination (badd) for the warfighter[C]//Proceedings of 1997 Digitization of the Battlefield II. Orlando:SPIE, 1997, 3080:18-24.
[10] STEPHENSON T P, DECLEENE B T, SPECKERT G, et al.BADD phase II:DDS information management architecture[C]//Proceedings of 1997 Digitization of the Battlefield II.Orlando:SPIE, 1997, 3080:49-58.
[11] YU X. Towards intelligent spatial information dissemination based on user profile model[J]. Journal of Tongji University, 2008, 13(6):401-410.
[12] 夏宇, 朱欣焰. 利用区间分析的空间信息智能分发决策[J]. 武汉大学学报(信息科学版), 2013, 38(9):1103-1107. XIA Yu, ZHU Xinyan. Intelligent spatial information delivery decision-making by using interval analysis[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9):1103-1107.
[13] 李新广, 范明虎, 杜武. 面向空间信息智能分发的动态化用户偏好模型研究[J]. 测绘学报, 2011, 40(5):646-654. LI Xinguang, FAN Minghu, DU Wu. Research on dynamic user profile model for intelligent distribution of spatial information[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(5):646-654.
[14] 龙恩, 吕守业, 岑鹏瑞, 等. 顾及用户画像的多源遥感信息智能推荐方法[J]. 测绘学报, 2023, 52(2):297-306.DOI:10.11947/j.AGCS.2023.20210335. LONG En, LÜ Shouye, CEN Pengrui, et al. An intelligent recommendation method of multi-source remote sensing information consi-dering user portrait[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(2):297-306.DOI:10.11947/j.AGCS.2023.20210335.
[15] 张秀红. 基于知识图谱的遥感影像应用领域知识服务研究[D]. 武汉:武汉大学, 2020. ZHANG Xiuhong. Remote sensing image application domain knowledge service research based on knowledge graph[D]. Wuhan:Wuhan University, 2020.
[16] 黄立威, 江碧涛, 吕守业, 等. 基于深度学习的推荐系统研究综述[J]. 计算机学报, 2018, 41(7):1619-1647. HUANG Liwei, JIANG Bitao, LÜ Shouye, et al. Survey on deep learning based recommender systems[J]. Chinese Journal of Compu-ters, 2018, 41(7):1619-1647.
[17] WANG Jizhe, HUANG Pipei, ZHAO Huan, et al. Billion-scale commodity embedding forE-commerce recommendation in alibaba[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.London:ACM Press, 2018:839-848.
[18] COVINGTON P, ADAMS J, SARGIN E. Deep neural networks for YouTube recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems.Boston:ACM Press, 2016:191-198.
[19] ELKAHKY A M, SONG Yang, HE Xiaodong. A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//Proceedings of the 24th International Conference on World Wide Web. New York:ACM Press, 2015:278-288.
[20] GRBOVIC M, RADOSAVLJEVIC V, DJURIC N, et al. E-commerce in your inbox:product recommendations at scale[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Sydney:ACM Press, 2015:1809-1818.
[21] 张兵. 智能遥感卫星系统[J]. 遥感学报, 2011, 15(3):415-431. ZHANG Bing. Intelligent remote sensing satellite system[J]. Journal of Remote Sensing, 2011, 15(3):415-431.
[22] WANG Ruoxi, FU Bin, FU Gang, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17.Halifax:ACM Press, 2017:1-7.
[23] MIKOLOV T, CHEN Kai, CORRADO G, et al. Efficient estimation of word representations in vector space[EB/OL].[2023-05-10].http://www.arxiv.org/pdf/1301.3781.pdf.
[24] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780.
[25] BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[EB/OL].[2023-05-10].https://arXivpreprint/arXiv:1409.0473.
[26] CHENG H T, KOC L, HARMSEN J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems.Boston:ACM Press, 2016:7-10.