高连如，中国科学院空天信息创新研究院，研究员、博导，研究方向为高光谱遥感图像处理与信息提取，曾获得国家优青项目资助，是IET Fellow。近十年先后主持国家自然科学基金、国家重点研发计划课题、国家高分专项项目、国防基础加强计划课题等国家和部委级科研项目10余项，作为技术负责人主持研制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。在国内外学术期刊发表学术论文200余篇，其中SCI收录130余篇；参与出版《高光谱图像信息提取》、《高光谱图像分类与目标探测》等学术著作3部；获得国家发明专利授权29项。获得国家科技进步二等奖、中科院杰出科技成就奖、军队科技进步一等奖各1项；获得国际高光谱遥感顶级会议WHISPERS的杰出论文奖1项。现担任IEEE TGRS、IET Image Processing期刊的副主编以及Chinese Geographical Science、《遥感学报》的编委，担任IGARSS国际会议评奖委员会委员和WHISPERS国际会议技术委员会委员。

工作经历:

2020.03-至今     中国科学院空天信息创新研究院          研究员

2015.02-2020.02  中国科学院遥感与数字地球研究所        研究员

2012.09-2015.01  中国科学院遥感与数字地球研究所        副研究员

2010.01-2012.08  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

高光谱遥感

（1）国家自然科学基金国际(地区)合作与交流项目  负责人  国家任务 2022.01—2025.12

（2）国家重点研发计划课题  负责人  国家任务  2021.12—2025.11

（3）基础加强计划重点基础研究课题  负责人  国家任务  2020.11—2023.05

（4）高分辨率对地观测系统重大专项项目  负责人    国家任务  2019.12—2021.06

（5）国家自然科学基金国际(地区)合作与交流项目  负责人  国家任务 2022.01—2025.12

（6）预研项目  负责人  国家任务  2019.12—2020.10

（7）国家优秀青年科学基金项目  负责人  国家任务  2018.01—2020.12

（8）国家自然科学基金面上项目  负责人    国家任务  2016.01—2019.12

（9）中国科学院重点部署项目  负责人  中国科学院计划 2013.01—2015.12

（10）高分辨率对地观测系统重大专项课题  负责人  国家任务  2012.01—2015.12

（11）预研项目  负责人  国家任务  2011.01—2015.12

（12）国家科技支撑计划专题  负责人    国家任务  2011.01—2013.06

（1）学术论文

[1]Lianru Gao, Degang Wang, Lina Zhuang, Xu Sun, Min Huang, Antonio Plaza. BS3LNet: A new blind-spot self-supervised learning network for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61(5504218): 1-18.

[2]Lianru Gao, Jiaxin Li, Ke Zheng, Xiuping Jia. Enhanced autoencoders with attention-embedded degradation learning for unsupervised hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61(5509417): 1-17.

[3]Lianru Gao, Xiaotong Sun, Xu Sun, Lina Zhuang, Qian Du, Bing Zhang. Hyperspectral anomaly detection based on chessboard topology. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61(5505016): 1-16.

[4]Lianru Gao, Zhu Han, Danfeng Hong, Bing Zhang, Jocelyn Chanussot. CyCU-Net: cycle-consistency unmixing network by learning cascaded autoencoders. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5503914): 1-14.

[5]Lianru Gao, Zhicheng Wang, Lina Zhuang, Haoyang Yu, Bing Zhang, Jocelyn Chanussot. Using low-rank representation of abundance maps and nonnegative tensor factorization for hyperspectral nonlinear unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5504017): 1-17.

[6]Lianru Gao, Danfeng Hong, Jing Yao, Bing Zhang, Paolo Gamba, Jocelyn Chanussot. Spectral superresolution of multispectral imagery with joint sparse and low-rank learning. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(3): 2269-2280.

[7]Lianru Gao, Daixin Gu, Lina Zhuang, Jinchang Ren, Dong Yang, Bing Zhang. Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2020, 17(8), 1368-1372.

[8]Lianru Gao, Yiqun He, Xu Sun, Xiuping Jia, Bing Zhang. Incorporating negative sample training for ship detection based on deep learning. Sensors, 2019, 19(3), 684.

[9]Lianru Gao, Dan Yao, Qingting Li, Lina Zhuang, Bing Zhang, José M. Bioucas-Dias. A new low-rank representation based hyperspectral image denoising method for mineral mapping. Remote Sensing, 2017, 9(11), 1145.

[10]Lianru Gao, Bin Zhao, Xiuping Jia, Wenzhi Liao, Bing Zhang. Optimized kernel minimum noise fraction transformation for hyperspectral image classification. Remote Sensing, 2017, 9(6), 548.

[11]Lianru Gao, Haoyang Yu, Bing Zhang, Qingting Li. Locality-preserving sparse representation-based classification in hyperspectral imagery. Journal of Applied Remote Sensing, 2016, 10(4), 042004.

[12]Lianru Gao, Lina Zhuang, Bing Zhang. Region-based estimate of endmember variances for hyperspectral image unmixing. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12), 1807-1811.

[13]Lianru Gao, Lina Zhuang, Yuanfeng Wu, Xu Sun, Bing Zhang. A quantitative and comparative analysis of different preprocessing implementations of DPSO: a robust endmember extraction algorithm. Soft Computing, 2016, 20(12): 4669-4683.

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

[15]Lianru Gao, Jianwei Gao, Jun Li, Antonio Plaza, Lina Zhuang, Xu Sun, 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.

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

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

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

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

[22]Xiaotong Sun, Lina Zhuang, Lianru Gao*, Hongmin Gao, Xu Sun, Bing Zhang. Information retrieval with chessboard-shaped topology for hyperspectral target detection. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61(5514515): 1-15.

[23]Longfei Ren, Danfeng Hong, Lianru Gao*, Xu Sun, Min Huang, Jocelyn Chanussot. Orthogonal subspace unmixing to address spectral variability for hyperspectral image. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61(5501713): 1-13.

[24]Wenfei Luo, Lianru Gao*, Danfeng Hong, Jocelyn Chanussot. Endmember purification with affine simplicial cone model. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5545423): 1-23.

[25]Danfeng Hong, Lianru Gao*, Jing Yao, Naoto Yokoya, Jocelyn Chanussot, Uta Heiden, Bing Zhang. Endmember-Guided Unmixing Network (EGU-Net): A general deep learning framework for self-supervised hyperspectral unmixing.  IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(11): 6518-6531.

[26]Jiaxin Li, Danfeng Hong, Lianru Gao* Jing Yao, Ke Zheng, Bing Zhang, Jocelyn Chanussot. Deep learning in multimodal remote sensing data fusion: A comprehensive review. International Journal of Applied Earth Observations and Geoinformation. 2022, 112(102926): 1-16.

[27]Zhu Han, Danfeng Hong, Lianru Gao*, Swalpa Kumar Roy, Bing Zhang, Jocelyn Chanussot. Reinforcement learning for neural architecture search in hyperspectral unmixing. IEEE Geoscience and Remote Sensing Letters, 2022, 19(6012705): 1-5.

[28]Zhicheng Wang, Michael K. Ng, Lina Zhuang, Lianru Gao*, Bing Zhang. Nonlocal self-similarity-based hyperspectral remote sensing image denoising with 3-D convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5531617): 1-17.

[29]Zhu Han, Danfeng Hong, Lianru Gao*, Bing Zhang, Min Huang, Jocelyn Chanussot. AutoNAS: automatic neural architecture search for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5532214): 1-14.

[30]Yuanchao Su, Mengying Jiang, Lianru Gao*, Xu Sun, Xueer You, Pengfei Li. Graph-cut-based collaborative node embeddings for hyperspectral images classification. IEEE Geoscience and Remote Sensing Letters, 2022, 19(6010905): 1-5.

[31]Weiqiang Rao, Lianru Gao*, Ying Qu, Xu Sun, Bing Zhang, Jocelyn Chanussot. Siamese transformer network for hyperspectral image target detection. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5526419): 1-19.

[32]Zhu Han, Danfeng Hong, Lianru Gao*, Jing Yao, Bing Zhang, Jocelyn Chanussot. Multimodal hyperspectral unmixing: insights from attention networks. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5524913): 1-13.

[33]Lian Liu, Danfeng Hong, Li Ni, Lianru Gao*. Multilayer cascade screening strategy for semi-supervised change detection in hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 1926-1940.

[34]Ke Zheng, Lianru Gao*, Danfeng Hong, Bing Zhang, Jocelyn Chanussot. NonRegSRNet: a nonrigid registration hyperspectral super-resolution network. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5520216): 1-16.

[35]Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao*, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. SpectralFormer: rethinking hyperspectral image classification with transformers. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5518615): 1-15.

[36]Lina Zhuang, Lianru Gao*, Bing Zhang, Xiyou Fu, José M. Bioucas-Dias. Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5500117): 1-17.

[37]Xiaotong Sun, Ying Qu, Lianru Gao*, Xu Sun, Hairong Qi, Bing Zhang, Ting Shen. Ensemble-based information retrieval with mass estimation for hyperspectral target detection. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(5508123): 1-23.

[38]Weiqiang Rao, Ying Qu, Lianru Gao*, Xu Sun, Yuanfeng Wu, Bing Zhang. Transferable network with Siamese architecture for anomaly detection in hyperspectral images. International Journal of Applied Earth Observations and Geoinformation. 2022, 106(102669): 1-14.

[39]Danfeng Hong, Lianru Gao*, Renlong Hang, Bing Zhang, Jocelyn Chanussot. Deep encoder-decoder networks for classification of hyperspectral and LiDAR data. IEEE Geoscience and Remote Sensing Letters, 2022, 19(5500205): 1-5.

[40]Danfeng Hong, Xin Wu, Lianru Gao*, Bing Zhang, Jocelyn Chanussot. Learning locality-constrained sparse coding for spectral enhancement of multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 2022, 19(5000405): 1-5.

[41]Danfeng Hong, Lianru Gao*, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph convolutional networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(7): 5966-5978.

[42]Danfeng Hong, Wei He*, Naoto Yokoya, Jing Yao, Lianru Gao*, Liangpei Zhang, Jocelyn Chanussot, Xiao Xiang Zhu. Interpretable hyperspectral artificial intelligence: when nonconvex modeling meets hyperspectral remote sensing. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(2): 52-87.

[43]Xiaotong Sun, Ying Qu, Lianru Gao*, Xu Sun, Hairong Qi, Bing Zhang, Ting Shen. Target detection through tree-structured encoding for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5): 4233-4249. 

[44]Danfeng Hong, Lianru Gao*, Naoto Yokoya, Jing Yao, Jocelyn Chanussot, Qian Du, Bing Zhang. More diverse means better: multimodal deep learning meets remote-sensing imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5): 4340-4354. 

[45]Ke Zheng, Lianru Gao*, Wenzhi Liao, Danfeng Hong, Bing Zhang, Ximin Cui, Jocelyn Chanussot. Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(3): 2487-2502. 

[46]Zhu Han, Danfeng Hong, Lianru Gao*, Bing Zhang, Jocelyn Chanussot.Deep Half-siamese networks for hyperspectral unmixing. IEEE Geoscience and Remote Sensing Letters, 2021, 18(11): 1996-2000.

[47]Yanheng Wang, Lianru Gao*, Danfeng Hong, Jianjun Sha, Lian Liu, Bing Zhang, Xianhui Rong, Yonggang Zhang. Mask DeepLab: end-to-end image segmentation for change detection in high-resolution remote sensing images. International Journal of Applied Earth Observations and Geoinformation, 2021, 104(102582): 1-9.

[48]Xuemei Zhao, Danfeng Hong, Lianru Gao*, Bing Zhang, Jocelyn Chanussot. Transferable deep learning from time series of Landsat data for national land-cover mapping with noisy labels: a case study of China. Remote Sensing, 2021, 13(21), 4194.

[49]Haoyang Yu, Xiaodi Shang, Xiao Zhang, Lianru Gao*, Meiping Song, Jiaochan Hu. Hyperspectral image classification based on adjacent constraint representation. IEEE Geoscience and Remote Sensing Letters, 2021, 18(4): 707-711. 

[50]Haoyang Yu, Lianru Gao*, Wenzhi Liao, Bing Zhang, Lina Zhuang, Meiping Song, Jocelyn Chanussot. Global spatial and local spectral similarity-based manifold learning group sparse representation for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(5): 3043-3056. 

[51]Yao Liu, Lianru Gao*, Chenchao Xiao, Ying Qu, Ke Zheng, Andrea Marinoni. Hyperspectral image classification based on a shuffled group convolutional neural network with transfer learning. Remote Sensing, 2020, 12(11), 1780. 

[52]Haoyang Yu, Lianru Gao*, Jun Li, Bing Zhang. Subspace-based multitask learning framework for hyperspectral imagery classification. Multimedia Tools and Applications, 2020, 79: 8887-8909. 

[53]Zhicheng Wang, Lina Zhuang, Lianru Gao*, Andrea Marinoni, Bing Zhang, Michael K. Ng. Hyperspectral nonlinear unmixing by using plug-and-play prior for abundance maps. Remote Sensing, 2020, 12(24), 4117. 

[54]Wenfei Luo, Lianru Gao*, Ruihao Zhang, Andrea Marinoni, Bing Zhang. Bilinear normal mixing model for spectral unmixing. IET Image Processing, 2019, 13(2): 344-354. 

[55]Ke Zheng, Lianru Gao*, Qiong Ran, Ximin Cui, Bing Zhang, Wenzhi Liao, Sen Jia. Separable-spectral convolution and inception network for hyperspectral image super-resolution. International Journal of Machine Learning and Cybernetics, 2019, 10(10): 2593-2607. 

[56]Ximin Cui, Ke Zheng, Lianru Gao*, Bing Zhang, Dong Yang, Jinchang Ren. Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification. Remote Sensing, 2019, 11(19), 2220. 

[57]Cong Li, Lianru Gao*, Antonio Plaza, Bing Zhang. FPGA implementation of a maximum simplex volume algorithm for endmember extraction from remotely sensed hyperspectral images. Journal of Real-Time Image Processing, 2019, 16(5): 1681-1694. 

[58]Xuemei Zhao, Lianru Gao*, Zhengchao Chen, Bing Zhang, Wenzhi Liao, Xuan Yang. An Entropy and MRF model-based CNN for large-scale Landsat image classification. IEEE Geoscience and Remote Sensing Letters, 2019, 16(7): 1145-1149. 

[59]Yuanfeng Wu, Sebastián López, Bing Zhang, Fei Qiao, Lianru Gao*. Approximate computing for onboard anomaly detection from hyperspectral images. Journal of Real-Time Image Processing, 2019, 16(1): 99-114. 

[60]Xuran Pan, Lianru Gao*, Bing Zhang, Fan Yang, Wenzhi Liao. High-resolution aerial imagery semantic labeling with dense pyramid network. Sensors, 2018, 18(11), 3774. 

[61]Haoyang Yu, Lianru Gao*, Wenzhi Liao, Bing Zhang. Group sparse representation based on nonlocal spatial and local spectral similarity for hyperspectral imagery classification. Sensors, 2018, 18(6), 1695. 

[62]Maofeng Tang, Bing Zhang, Andrea Marinoni, Lianru Gao*, Paolo Gamba. Multiharmonic postnonlinear mixing model for hyperspectral nonlinear unmixing. IEEE Geoscience and Remote Sensing Letters, 2018, 15(11): 1765-1769. 

[63]Xuran Pan, Lianru Gao*, Andrea Marinoni, Bing Zhang, Fan Yang, Paolo Gamba. Semantic labeling of high resolution aerial imagery and LiDAR data with fine segmentation network. Remote Sensing, 2018, 10(5), 743. 

[64]Maofeng Tang, Lianru Gao*, Andrea Marinoni, Paolo Gamba, Bing Zhang. Integrating spatial information in the normalized P-linear algorithm for nonlinear hyperspectral unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(4): 1179-1190. 

[65]Cong Li, Lianru Gao*, Yuanfeng Wu, Bing Zhang, Javier Plaza, Antonio Plaza. A real-time unsupervised background extraction-based target detection method for hyperspectral imagery. Journal of Real-Time Image Processing, 2018, 15(3): 597-615. 

[66]Haoyang Yu, Lianru Gao*, Wenzhi Liao, Bing Zhang, Aleksandra Pi？urica, Wilfried Philips. Multiscale superpixel-level subspace-based support vector machines for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2017, 14(11), 2142-2146. 

[67]Haoyang Yu, Lianru Gao*, Wei Li, Qian Du, Bing Zhang. Locality sensitive discriminant analysis for group sparse representation-based hyperspectral imagery classification. IEEE Geoscience and Remote Sensing Letters, 2017, 14(8), 1358-1362. 

[68]Haoyang Yu, Lianru Gao*, Jun Li, Shanshan Li, Bing Zhang, Jon Alti Benediktsson. Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields. Remote Sensing, 2016, 8(4), 355. 

[69]Wenfei Luo, Lianru Gao*, Antonio Plaza, Andrea Marinoni, Bin Yang, Liang Zhong, Paolo Gamba, Bing Zhang. A new algorithm for bilinear spectral unmixing of hyperspectral images using particle swarm optimization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(12), 5776-5790. 

[70]Yuanfeng Wu, Xinhua Yang, Antonio Plaza, Fei Qiao, Lianru Gao*, Bing Zhang, Yabo Cui. Approximate computing of remotely sensed data: SVM Hyperspectral image classification as a case study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(12): 5806-5818. 

[71]Yuanchao Su, Xu Sun, Lianru Gao*, Jun Li, Bing Zhang. Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images. Journal of Applied Remote Sensing, 2016, 10(4), 045018. 

[72]Lina Zhuang, Bing Zhang, Lianru Gao*, Jun Li, 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. 

[73]Yuanfeng Wu, Lianru Gao*, Bing Zhang, Haina Zhao, 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. 

[74]Qiandong Guo, Bing Zhang, Qiong Ran, Lianru Gao*, Jun Li, 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. 

[75]王艳恒, 高连如*, 陈正超, 张兵. 结合深度学习和超像元的高分遥感影像变化检测. 中国图象图形学报, 2020, 25(6): 1271-1282.

[76]韩竹, 高连如*, 张兵, 孙旭, 李庆亭. 高分五号高光谱图像自编码网络非线性解混. 遥感学报, 2020, 24(4): 388-400.

[77]高连如, 孙旭, 罗文斐, 唐茂峰, 张兵. 高光谱遥感图像混合像元分解的群智能算法. 南京信息工程大学学报, 2018, 10(1): 81-91.

[78]高连如, 张兵, 张霞, 申茜. 基于局部标准差的遥感图像噪声评估方法研究. 遥感学报, 2007, 11(2): 201-208.

[79]苏远超, 孙旭, 高连如*, 陈晓宁. 高光谱影像端元提取算法的进展分析与比较. 遥感技术与应用, 2015, 30(6): 1195-1205. 

（2）专著（参与编写）

[1]《高光谱图像信息提取》，科学出版社，2020

[2]《高光谱卫星图像协同处理理论与方法》，人民邮电出版社，2020

[3]《高光谱图像分类与目标探测》，科学出版社，2011

（1）第11届国际高光谱图像与信号处理研讨会杰出论文奖, 其他, 2021

（2）遥感信息关键技术, 一等奖, 部委级, 2020

（3）高光谱遥感信息机理与多学科应用, 二等奖, 国家级, 2018

（4）2017年度IEEE TGRS期刊最佳审稿人, 其他, 2018

（5）高光谱遥感研究集体, 院级, 2017

（6）2015年度IEEE JSTARS期刊优秀审稿人, 其他, 2016