Publications
Dr. Zhao Chen
Journal papers (* for contact author)
1. Zhao Chen，Bing Wang*. Spectrally-spatially regularized low-rank and sparse decomposition: a novel method for change detection in multitemporal hyperspectral images. Remote Sensing. 2017, 9 (10), 1044: 1-21（SCI, EI）.
2. Zhao Chen，Bing Wang*. Spectral-spatial classification based on affinity scoring for hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016, 9 (6): 2305-2320 (SCI, EI).
3. Zhao Chen, Bin Wang*. Semisupervised spectral-spatial classification of hyperspectral imagery with affinity scoring. IEEE Geoscience and Remote Sensing Letters. 2015, 12 (8): 1710-1714 (SCI, EI).
4. Zhao Chen, Hanye Pu, Bin Wang*, Geng-Ming Jiang. Fusion of hyperspectral and multispectral images: a novel framework based on generalization of pan-sharpening methods. IEEE Geoscience and Remote Sensing Letters. 2014, 11 (8): 1418-1422 (SCI, EI).
5. Zhao Chen, Bing Wang*. Spectral-spatial classification for hyperspectral imagery: a novel combination method based on affinity scoring. Science China Information Sciences. 2016, 59 (10): 102313:1-102313:13 (SCI, EI).
6. Zhao Chen, Bing Wang*, Li-Ming Zhang. Dimensionality reduction and classification based on lower rank tensor analysis for hyperspectral imagery. Journal of Infrared and Millimeter Waves. 2013, 32 (6): 569-575 (SCI, EI).
7. Hanye Pu, Zhao Chen, Bin Wang*, Wei Xia. Constrained least squares algorithms for nonlinear unmixing of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing. 2015, 53 (3): 1287-1303 (SCI, EI).
8. Hanye Pu, Zhao Chen, Bin Wang*, Gengming Jiang. A novel spatial–spectral similarity measure for dimensionality reduction and classification of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing. 2014, 52 (11): 7008-7022 (SCI, EI).
9. Zhao Chen*, Xingxing Yu. A novel framework based on tensor networks for tropical cyclone intensity estimation. IEEE Transactions on Geoscience and Remote Sensing (undergoing major revision).
10. Bin Yang, Zhao Chen, Bin Wang. Nonlinear endmember identification for hyperspectral imagery via hyperpath based simplex growing and fuzzy assessment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (undergoing major revision).
Conference papers (* for contact author)
1. Zhao Chen*, Feng Zhou. Multitemporal hyperspectral image change detection by joint affinity and convolutional neural networks. MultiTemp 2019 (EI).
2. Zhao Chen*, Xinxin Wang, Yuxin Zheng, Yan Wan. Region-based convolutional neural networks for profiled fiber recognition. ICALIP 2018 (EI).
3. Zhao Chen*, Xingxing Yu, Guangchen Chen, Junfeng Zhou. Cyclone intensity estimation using multispectral imagery from the FY-4 satellite. ICALIP 2018 (EI).
4. Zhao Chen, Chengzhi Zhou, Yijun Zhou, Lingyun Zhu, Ting Lu and Guohua Liu*. Multi-Dimensional Regression for Colour Prediction in Pad Dyeing. ICCCS 2018 (EI).
5. Zhao Chen, Bin Yang, Bin Wang*, Guohua Liu, Wei Xia. Change detection in hyperspectral imagery based on spectrally-spatially regularized low-rank matrix decomposition. IGARSS 2017 (EI).
6. Zhao Chen*, Muhammad Sohail, Bin Wang. Low-rank matrix decomposition with a spectral-spatial regularization for change detection in hyperspectral imagery. RSIP 2017 (EI).
7. Zhao Chen, Bin Wang*, Yubin Niu, Wei Xia, Jian Qiu Zhang, Bo Hu. Semisupervised hyperspectral image classification based on affinity scoring. IGARSS 2015 (EI).
8. Zhao Chen, Bin Wang*, Yubin Niu, Wei Xia, Jian Qiu Zhang, Bo Hu. Change detection for hyperspectral images based on tensor analysis. IGARSS 2015 (EI).
9. Zhao Chen, Bin Wang*. An improved spectral-spatial classification framework for hyperspectral remote sensing images. ICALIP 2014 (EI).
10. Xingxing Yu, Zhao Chen* (contact author), Guangchen Chen, He Zhang. A tensor network for tropical cyclone wind speed estimation. IGARSS 2019 (EI).
11. Guangchen Chen, Zhao Chen* (contact author), Feng Zhou, Xingxing Yu, He Zhang, Lingyun Zhu. A semisupervised deep learning framework for tropical cyclone intensity estimation. MultiTemp 2019 (EI).
12. Xinxin Wang, Zhao Chen* (contact author), Guohua Liu, Yan Wan. Fiber image classification using convolutional neural network. ICSAI 2017 (EI).
13. Jiayan Cao, Zhao Chen, Bin Wang*. Deep convolutional networks with superpixel segmentation for hyperspectral image classification. IGARSS 2016 (EI).
14. Jiayan Cao, Zhao Chen, Bin Wang*. Graph-based deep convolutional networks for hyperspectral image classification. IGARSS 2016 (EI).
Dr. Bin Yang
Journal Papers
1. Bin Yang and Bin Wang, “Band-wise nonlinear unmixing for hyperspectral imagery using an extended multilinear mixing model,” IEEE Trans. Geosci. Remote Sens., 2018. (Accepted in Jun. 2018，DOI: 10.1109/TGRS.2018.2842707)
2. Bin Yang, Bin Wang, and Zongmin Wu, “Nonlinear hyperspectral unmixing based on geometric characteristics of bilinear mixture models,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 2, pp. 694–714, Feb. 2018. (DOI: 10.1109/TGRS.2017.2753847)
3. Bin Yang, Bin Wang, and Zongmin Wu, “Unsupervised nonlinear hyperspectral unmixing based on bilinear mixture models via geometric projection and constrained nonnegative matrix factorization,” Remote Sens., vol. 10, no. 5, pp. 801(1–30), May. 2018. (DOI: 10.3390/rs10050801)
4. Bin Yang, Wenfei Luo, and Bin Wang, “Constrained nonnegative matrix factorization based on particle swarm optimization for hyperspectral unmixing,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no. 8, pp. 3693–3710, Aug. 2017. (DOI: 10.1109/JSTARS.2017.2682281)
5. Bin Yang, Bin Wang, and Zongmin Wu, “Nonlinear spectral unmixing for hyperspectral imagery based on bilinear mixture models,” Infrared Millim.Waves., 2017. (accepted in Nov. 2016, in Chinese)
6. Bin Yang and Bin Wang, “Review of nonlinear unmixing for hyperspectral remote sensing imagery,” Infrared Millim.Waves., vol. 36, no. 2, pp. 173–185, Apr. 2017. (in Chinese)
7. Bin Yang and Wenfei Luo, “Constrained NMF based high-dimension adaptive particle swarm optimization algorithm for endmember extraction from hyperspectral remote sensing image,” Journal of Remote Sensing, vol. 19, no. 2, pp. 240–253, 2015. (in Chinese)
8. Bin Yang, Zhao Chen, and Bin Wang, “Nonlinear endmember identification for hyperspectral imagery via hyperpath-based simplex growing and fuzzy assessment,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.,, 2020. (DOI: 10.1109/JSTARS.2019.2962609)
9. Zehao Chen, Bin Yang, and Bin Wang, “A preprocessing method for hyperspectral target detection based on tensor principal component analysis,” Remote Sens., vol. 10, no. 7, pp. 1033(1–21), Jun. 2018. (DOI: 10.3390/rs10071033)
10. Wenfei Luo, Lianru Gao, Antonio Plaza, Andrea Marinoni, Bin Yang, Liang Zhong, Paolo Gamba, and Bing Zhang, “A new algorithm for bilinear spectral unmixing of hyperspectral images using particle swarm optimization,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 12, pp. 5776–5790, Dec. 2016. (DOI: 10.1109/JSTARS.2016. 2602882)
11. Tongxiang Zhi, Bin Yang, and Bin Wang, “A nonlinear unmixing algorithm dealing with spectral variability for hyperspectral imagery,” Infrared Millim.Waves., 2018. (accepted, in Chinese)
12. Tongxiang Zhi, Bin Yang, and Bin Wang, “Hyperspectral nonlinear unmixing based on abundances constrained kernel nonnegative matrix factorization,” Journal of Fudan University (Natural Science), 2018. (accepted, in Chinese)
13. Shiyin Qin, Wenfei Luo, Bin Yang, and Ruihao Zhang, “Simplex volume minimization based differential evolution algorithm for spectral unmixing,” Journal of Image and Graphics, vol. 20, no. 11, pp. 1535–1544, 2015. (in Chinese)
Conference Papers
1. Bin Yang, Bin Wang, Bo Hu, and Jian Qiu Zhang, “Nonlinear hyperspectral unmixing via modelling band dependent nonlinearity,” 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’18), Valencia, Spain, 22-27 July 2018. (Accepted)
2. Bin Yang, Bin Wang, Zongmin Wu, and Qiyong Lu, “Bilinear mixture models based unsupervised nonlinear unmixing using constrained nonnegative matrix factorization,” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’17), Fort Worth, TX, 23-28 July 2017, 582–585. (DOI: 10.1109/IGARSS.2017.8127020)
3. Bin Yang, Bin Wang, Zongmin Wu, and Qiyong Lu, “Abundance estimation for hyperspectral images based on bilinear mixture models,” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’17), Fort Worth, TX, 23-28 July 2017, 644–647. (DOI: 10.1109/IGARSS.2017.8127036)
4. Zehao Chen, Bin Yang, and Bin Wang, “Hyperspectral target detection: A preprocessing method based on tensor principal component analysis,” 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’18), Valencia, Spain, 22-27 July 2018. (Accepted)
5. Tongxiang Zhi, Bing Yang, Zhao Chen, and Bin Wang, “Nonnegative matrix factorization with constraints on endmember and abundance for hyperspectral unmixing,” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’17), Fort Worth, TX, 2017, 1149–1152. (DOI: 10.1109/IGARSS.2017.8127161)
6. Zhao Chen, Bin Yang, Bin Wang, Guohua Liu, and Wei Xia, “Change detection in hyperspectral imagery based on spectrally-spatially regularized low-rank matrix decomposition,” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’17), Fort Worth, TX, 23-28 July 2017, 157–160. (DOI: 10.1109/IGARSS.2017.8126918)