中國地質大學(武漢)地理與信息工程學院副教授，碩士生導師。2013年免試攻讀武漢大學碩士學位，2015年碩博連讀提前攻博，師從李德仁院士、鐘燕飛教授與張良培教授，2018年6月，畢業於武漢大學測繪遙感信息工程國家重點實驗室，獲攝影測量與遙感專業工學博士學位。2018年7月，以“地大學者”青年優秀人才引進至中國地質大學（武漢）地理與信息工程學院地理系。
基本介紹
- 中文名：朱祺琪
- 國籍：中國
- 民族：漢族
- 出生日期：1993年
- 畢業院校：武漢大學
- 學位/學歷：博士
- 職業：教師
- 職稱：副教授
教育經歷,研究方向,學術成果,學術任職,代表性學術論文,
教育經歷
2013 - 2018年，武漢大學 博士
研究方向
基於航天、航空、無人機等高解析度、高光譜多源遙感數據及多源地理數據，重點研究：
（1）機率圖模型、遷移學習、深度學習等機器學習方法
（2）場景分類、目標探測、語義分割、視頻目標追蹤、道路提取、變化檢測等遙感圖像解譯任務
（3）城市遙感、功能區規劃、農業遙感、地理信息服務等套用
學術成果
已發表SCI論文十餘篇，兩篇論文入選2017年及2019年ESI全球1%高被引論文。已主持或參與國家重點研發計畫課題、國家自然科學基金、國家發改委項目等科研項目十餘項。
學術任職
擔任IEEE Transaction on Geoscience and Remote Sensing、IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing、IEEE Access、Remote Sensing、IEEE Geoscience and Remote Sensing Letter、International Journal of Remote Sensing等遙感、計算機領域國際SCI期刊的審稿人。
代表性學術論文
[1] Q. Zhu, Y. Zhong, L. Zhang, and D. Li, “Adaptive deep sparse semantic modeling framework for high spatial resolution image scene classification,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 10, pp. 6180 – 6195, 2018. (SCI top, IF=4.942,中科院TOP)
[2] Q. Zhu, Y. Zhong, S. Wu, L. Zhang, and D. Li, “Scene classification based on the sparse homogeneous-heterogeneous topic feature model,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 5,pp. 2689 – 2703, 2018. (SCI top, IF=4.942,中科院TOP)
[3] Q. Zhu, Y. Zhong, L. Zhang, and D. Li, “Scene classification based on the fully sparse semantic topic model,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, pp. 5525 – 5538, 2017. (SCI top, IF=4.942,中科院TOP)
[4] Y. Zhong, Q. Zhu, and L. Zhang, “Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 11, pp. 6207–6222, Nov. 2015. (SCI top, ESI 高被引論文, Google citation: 101, IF=4.942, 中科院TOP)
[5] Q. Zhu, Y. Zhong, B. Zhao, G. S. Xia, and L. Zhang, “Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery,” IEEE Geosci. Remote Sens. Lett., vol. 13, no. 6, pp. 747–751, Jun. 2016. (SCI, ESI 高被引論文, Google citation: 77, IF=2.761, 中科院三區)
[6] Q. Zhu, Y. Zhong, Y. Liu, L. Zhang, and D. Li, “A deep-local-global feature fusion framework for high spatial resolution image scene classification”, Remote Sensing, vol. 10, no. 4, pp. 568, 2018. (SCI, IF=3.244, 中科院二區)
[7] Y. Liu, Y. Zhong, F. Fei, Q. Zhu, Q. Qin, “Scene Classification Based on a Deep Random-Scale Stretched Convolutional Neural Network”, Remote Sensing, vol. 10, no. 3, pp. 444, 2018. (SCI, IF=3.244, 中科院二區)
[8] Y. Zhong, M. Cui, Q. Zhu, and L. Zhang, “Scene classification based on multifeature probabilistic latent semantic analysis for high spatial resolution remote sensing images,” J. Appl. Remote Sens., vol. 9, no. 1, pp. 095064–095064, Jul. 2015. (SCI, IF=2.761,中科院三區)
[9] Q. Zhu, X. Sun, Y. Zhong, L. Zhang, “High-resolution remote sensing image scene understanding: a review,” in Proc. 2019IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 27–August 3, 2019, Yokohama, Japan. (EI)
[10] Q. Zhu, Y. Zhong, L. Zhang, “Scene classification based on the semantic-feature fusion fully sparse topic model for high spatial resolution remote sensing imagery,” in Proc. 2016 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS), July 12–19, 2016, Czech, Prague. (EI)
[11] Q. Zhu, Y. Zhong, L. Zhang, “The bag-of-visual-words scene classifier combining local and global features for high spatial resolution imagery,” in Proc. 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), August 15–17, 2015, Zhangjiajie, China. (EI)
[12] Q. Zhu, Y. Zhong, L. Zhang, “Multi-feature probability topic scene classifier for high spatial resolution remote sensing imagery,” in Proc. 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 13–18, 2014, Quebec, Canada. (EI)
[13] M. Song, Y. Zhong, A. Ma, Q. Zhu, L. Cao, L. Zhang, “Sub-pixel mapping with multiple shifted hyperspectral images based on multiobjective evolutionary algorithm,” in Proc. 2019IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 27–August 3, 2019, Yokohama, Japan. (EI)
