高连如

中国科学院空天信息创新研究院

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  • 高连如
  • 研究员
  • 北京市海淀区邓庄南路9号

简 历:

简历:

主要研究方向为高光谱图像信息提取机理与方法。围绕这一领域研究,主持了国家和部委级的科研项目共10项,包括:国家自然科学基金项目、国家科技支撑计划项目、中国科学院重点部署项目、总装预研项目和国家高分专项项目课题等,主持开发了高光谱图像信息提取软件系统和硬件系统各一套,成果在国家相关部门发挥了重要应用价值。2011年合作出版了学术专著《高光谱图像分类与目标探测》,这是国内专门针对高光谱图像分类与目标探测模型方法研究和应用示例的第一本系统性专著。已经在《IEEE TGRS》、《IEEE JSTARS》、《IEEE GRSL》、《Journal of Applied Remote Sensing》、《Remote Sensing》和《遥感学报》等国内外期刊和会议上发表了学术论文90余篇,其中SCI收录35篇;申请国家发明专利/国防专利共21项,已经获得授权有7项;获得软件著作权登记4项。现为IEEE和SPIE会员,是《IEEE TGRS》、《IEEE JSTARS》、《IEEE GRSL》等多个国际SCI期刊的审稿专家。2012年、2015年国际高光谱学术会议(WHISPERS)分会主席;2014年多源遥感国际研讨会学术委员会委员,做大会特邀报告;2015年、2016年西班牙GISTAM国际学术会议程序委员会委员。

教育背景:

2002.09 – 2007.06,中国科学院遥感应用研究所,博士

1998.09 – 2002.06,清华大学,学士

科研工作经历:

2015.02 – 至今,中国科学院遥感与数字地球研究所,研究员

2013.01 – 2015.01,中国科学院遥感与数字地球研究所,副研究员

2010.01 – 2012.12,中国科学院对地观测与数字地球科学中心,副研究员

2008.01 – 2009.12,中国科学院对地观测与数字地球科学中心,助理研究员

2007.07 – 2007.12,中国科学院遥感应用研究所,助理研究员

研究生培养:

已联合培养博士研究生、硕士研究生8名

社会任职:研究方向:

高光谱遥感

承担科研项目情况:代表论著:

专著:

张兵, 高连如. 《高光谱图像分类与目标探测》, 北京: 科学出版社, 2011年5月第一版, ISBN 978-7-03-030863-4, 45.9万字.

代表性论文:

[1]Qiandong Guo, Ruiliang Pu, Lianru Gao, and Bing Zhang. A novel anomaly detection method incorporating target information derived from hyperspectral imagery, Remote Sensing Letters, 7(1):11-20.

[2] Lianru Gao, Bin Yang, Qian Du, and Bing Zhang. Adjusted spectral matched filter for target detection in hyperspectral imagery, Remote Sensing, 2015, 7(6): 6611-6634.

[3]Xiaoxia Sun, Liwei Li, Bing Zhang, Dongmei Chen, and Lianru Gao. Soft urban water cover extraction using mixed training samples and Support Vector Machines. International Journal of Remote Sensing, 2015, 36(13): 3331-3344.

[4]Yuanfeng Wu, Jun Li, Lianru Gao, Xuemin Tan, and Bing Zhang. Graphics processing unit–accelerated computation of the Markov random fields and loopy belief propagation algorithms for hyperspectral image classification, Journal of Applied Remote Sensing, 2015, 9(1), 097295.

[5]Bin Yang, Minhua Yang, Antonio Plaza, Lianru Gao, Bing Zhang. Dual-mode FPGA implementation of target and anomaly detection algorithms for real-time hyperspectral imaging. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2950-2961.

[6]Xu Sun, Lina Yang, Bing Zhang, Lianru Gao, and Jianwei Gao. An endmember extraction method based on artificial bee colony algorithms for hyperspectral remote sensing images. Remote Sensing, 2015, 7(12): 16363-16383.

[7]Xu Sun, Lina Yang, Lianru Gao, Bing Zhang, Shanshan Li and Jun Li. Hyperspectral image clustering method based on artificial bee colony algorithm and Markov random fields. Journal of Applied Remote Sensing, 2015, 9(1): 095047.

[8]Lianru Gao, Jianwei Gao, Jun Li, Antonio Plaza, Lina Zhuang Xu Sun, and Bing Zhang. Multiple algorithm integration based on ant colony optimization for endmember extraction from hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2569-2582.

[9]Lianru Gao, Qiandong Guo, Antonio Plaza, Jun Li, and Bing Zhang. Probabilistic anomaly detector for remotely sensed hyperspectral data. Journal of Applied Remote Sensing, 2014, 8(1): 083538.

[10] Lianru Gao, Jun Li, Mahdi Khodadadzadeh, Antonio Plaza, Bing Zhang, Zhijian He, and Huiming Yan. Subspace-based support vector machines for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2015, 12(2): 349-353.

[11]Lina Zhuang, Bing Zhang, Lianru Gao, Jun Li and Antonio Plaza. Normal endmember spectral unmixing method for hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2598-2606. (通讯作者)

[12] Bing Zhang, Lina Zhuang, Lianru Gao, Wenfei Luo, Qiong Ran, and Qian Du. PSO-EM: A hyperspectral unmixing algorithm based on normal compositional model. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(12): 7782-7792.

[13]Yuanfeng Wu, Lianru Gao, Bing Zhang, Haina Zhao, and Jun Li. Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images. Journal of Applied Remote Sensing, 2014, 8(1): 084797. (通讯作者)

[14]Qiandong Guo, Bing Zhang, Qiong Ran, Lianru Gao, Jun Li, and Antonio Plaza. Weighted-RXD and linear filter-based RXD: improving background statistics estimation for anomaly detection in hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2351-2366. (通讯作者)

[15]Li Ni, Lianru Gao, Shanshan Li, Jun Li, and Bing Zhang. Edge-constrained Markov random field classification by integrating hyperspectral image with LiDAR data over urban areas. Journal of Applied Remote Sensing, 2014, 8(1): 085089.

[16]Jianwei Gao, Qian Du, Lianru Gao, Xu Sun, and Bing Zhang. Ant colony optimization-based supervised and unsupervised band selections for hyperspectral urban data classification. Journal of Applied Remote Sensing, 2014, 8(1): 085094.

[17]Bing Zhang, Yao Liu, Wenjuan Zhang, Lianru Gao, Jun Li, Jun Wang, and Xia Li. Analysis of the proportion of surface reflected radiance in mid-infrared absorption bands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2639-2646.

[18]Lianru Gao, Qian Du, Bing Zhang, Wei Yang, and Yuanfeng Wu. A comparative study on linear regression-based noise estimation for hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 488-498.

[19]Lianru Gao, Bing Zhang, Xu Sun, Shanshan Li, Qian Du, and Changshan Wu. Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data. EURASIP Journal on Advances in Signal Processing 2013, 2013: 65.

[20]Bing Zhang, Jianwei Gao, Lianru Gao, and Xu Sun. Improvements in the ant colony optimization algorithm for endmember extraction from hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 522-530.

[21]Bing Zhang, Shanshan Li, Changshan Wu, Lianru Gao, Wenjuan Zhang, and Man Peng. A neighborhood constrained k-means approach to classify very high spatial resolution hyperspectral imagery. Remote Sensing Letters, 2013,4(2): 161-170.

[22]Shanshan Li, Bing Zhang, An Li, Xiuping Jia, Lianru Gao, and Man Peng. Hyperspectral imagery clustering with neighborhood constraints. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 588-592.

[23]Bing Zhang, Wei Yang, Lianru Gao, and Dongmei Chen. Real-time target detection in hyperspectral images based on spatial-spectral information extraction. EURASIP Journal on Advances in Signal Processing 2012, 2012: 142.

[24]Bing Zhang, Jianjun Sha, Xiangwei Wang, and Lianru Gao. Impact analysis of atmospheric state for target detection in hyperspectral radiance image. Spectroscopy and Spectral Analysis, 2012, 32(8): 2043-2049.

[25]Bing Zhang, Xun Sun, Lianru Gao, and Lina Yang. Endmember extraction of hyperspectral remote sensing images based on the discrete particle swarm optimization algorithm. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4173-4176.

[26]Bing Zhang, Xun Sun, Lianru Gao, and Lina Yang. Endmember extraction of hyperspectral remote sensing images based on the ant colony optimization (ACO) algorithm. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(7): 2635-2646.

[27]Bing Zhang, Shanshan Li, Xiuping Jia, Lianru Gao, and Man Peng. Adaptive markov random field approach for classification of hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 2011, 8(5): 973-977.

[28]Shanshan Li, Bing Zhang, Dongmei Chen, Lianru Gao, and Man Peng. Adaptive support vector machine and Markov random field model for classifying hyperspectral imagery. Journal of Applied Remote Sensing, 2011, 5(1): 053538.

[29]Bing Zhang, Xu Sun, Lianru Gao, and Lina Yang. A method of endmember extraction in hyperspectral remote sensing images based on Discrete Particle Swarm Optimization (D-PSO). Spectroscopy and Spectral Analysis, 2011, 31(9): 2455-2461.

[30]Wenfei Luo, Liang Zhong, Bing Zhang, and Lianru Gao. Null space spectral projection algorithm for hyperspectral image endmember extraction. Journal of Infrared and Millimeter Waves, 2010, 29(4): 307-320.

[31]Wenfei Luo, Liang Zhong, Bing Zhang, and Lianru Gao. Independent component analysis for spectral unmixing in hyperspectral remote sensing image. Spectroscopy and Spectral Analysis, 2010, 30(6); 1628-1633.

[32]Xiang Liu, Bing Zhang, Lianru Gao, and Dongmei Chen. A maximum noise fraction transform with improved noise estimation for hyperspectral images. Science in China Series F-Information Sciences, 2009, 52(9): 1578-1587.

[33]Wenjuan Zhang, Bing Zhang, Xia Zhang, Lianru Gao, and Wei Zhang. Effects of apodization functions of imaging Fourier transform spectrometer on reconstructed spectrum. Journal of Infrared and Millimeter Waves, 2008, 27(3): 227-232.

[34]Lianru Gao, Bing Zhang, Xia Zhang, Wenjuan Zhang and Qingxi Tong. A new operational method for estimating noise in hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 2008, 5(1): 83-87.

[35]Lianru Gao, Bing Zhang, Xia Zhang, and Junsheng Li. Infrared spectral analysis of architectural materials covered by different paints. Journal of Infrared and Millimeter Waves, 2006, 25(6): 411-416.

专利:

[1]张兵, 高连如, 孙旭, 吴远峰, 张文娟, 申茜. 高维空间定向投影端元提取方法, ZL201110107797.1.

[2]张兵, 高连如, 孙旭, 张文娟, 吴远峰和高东生. 目标投影探测方法, ZL201110014723.3.

[3]张兵, 张文娟, 高连如, 孙旭, 吴远峰和高东生. 一种遥感器指标设计方法, ZL201110015607.3.

[4]张兵, 高连如, 杨威, 孙旭, 吴远峰, 李利伟. 一种高光谱图像中目标地物检测方法及装置, ZL201210056079.0.

[5]张兵, 高连如, 孙旭, 高建威, 吴远峰, 申茜. 一种高维数据可视化方法及装置, ZL201210163032.4.

[6]张兵, 高连如, 孙旭, 吴远峰, 郭乾东, 高建威. 地物光谱获取方法及装置、高光谱图像目标探测方法及装置, ZL201310021964.X.

[7]张兵, 吴远峰, 高连如, 张文娟, 申茜. 数字图像显示方法以及高光谱望远镜, ZL 201410075088.3.

软件著作权:

[1]高光谱遥感图像目标探测软件V1.0, 2011SR036579.

[2]高光谱遥感图像自动分类软件V1.0, 2011SR036581.

[3]CE-1干涉成像光谱仪光谱复原软件V1.0, 2011SR008706.

[4]航空高光谱遥感数据精细分类与丰度反演软件V1.0, 2013SR041114.

获奖及荣誉: