NCKUEE Faculty Data
Chinese Version
Associate Professor Chia-Hsiang Lin
Address
EE Building 9F R92907
Email
TEL
+886-6-2757575 ext.62335
Lab Weblink
Intelligent Hyperspectral Computing Lab
(R92931+92A53/ext.92931+92A53)
Background
Educations
2016
Ph.D. in Communications Engineering, National Tsing Hua University
2010
B.S. in Electrical Engineering, National Tsing Hua University
Experiences
2022-present
Associate Professor, Institute of Computer and Communication Engineering, National Cheng Kung University
2022-present
Associate Professor, Institute of Computer and Communication Engineering, National Cheng Kung University
2019-2022
Assistant Professor, Institute of Computer and Communication Engineering, National Cheng Kung University
2019-2022
Assistant Professor, Department of Electrical Engineering, National Cheng Kung University
2019
Visiting Professor, University of Lisbon, Portugal
2018
Assistant Professor, National Central University
2017-2018
Postdoctoral Researcher, University of Lisbon, Portugal
2017
Visiting Scholar, The Chinese Univ. of Hong Kong
2016-2017
Postdoctoral Researcher, National Tsing Hua University
2015-2016
Visiting Ph.D. Student, Virginia Tech, VA, USA
2014
Visiting Ph.D. Student, The Chinese Univ. of Hong Kong
Specialities
  • blind signal processing / unsupervised machine learning
  • hyperspectral image processing
  • quantum image processing
  • fast algorithm / big data optimization theory
  • deep learning
  • convex optimization
  • 5G/6G wireless communications
  • satellite remote sensing
  • bio-informatics / biomedical imaging
Publication
Journal
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  1. P.-C. Chuan, J.-T. Lin, Chia-Hsiang Lin, P.-W. Tang, and Y. Liu, “Optimization-based hyperspectral spatiotemporal super-resolution,”accepted by IEEE Access, 2022.
  2. L. Chen, C.-T. Wu, Chia-Hsiang Lin, R. Dai, C. Liu, R. Clarke, G. Yu, J. E. Van Eyk, D. M. Herrington, and Y. Wang, “swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution,” accepted by Bioinformatics, 2021.
  3. Chia-Hsiang Lin, Y.-C. Lin, and P.-W. Tang, “ADMM-ADAM: A new inverse imaging framework blending the advantages of convex optimization and deep learning,” accepted by IEEE Transactions on Geoscience and Remote Sensing, 2021.
  4. C.-H. Lee, R. Chang, S.-M. Cheng, Chia-Hsiang Lin, and C.-H. Hsiao, “Joint beamforming and power allocation for M2M/H2H co-existence in green dynamic TDD networks: Low-complexity optimal designs,” accepted by IEEE Internet of Things Journal, 2021.
  5. Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, H.-C. Wu, H.-T. Kuo, C.-F. Lin, P. Chen, and P.-C. Wu, “Automatic inverse design of high-performance beam-steering metasurfaces via genetic-type tree optimization,” Nano Letters, vol. 21, no. 12, pp. 4981-4989, Jun. 2021.
  6. Chia-Hsiang Lin, and T.-H. Lin, “All-addition hyperspectral compressed sensing for metasurface-driven miniaturized satellite,” accepted by IEEE Transactions on Geoscience and Remote Sensing, 2021.
  7. C.-C. Hsu, Chia-Hsiang Lin, C.-H. Kao, and Y.-C. Lin, “DCSN: Deep compressed sensing network for efficient hyperspectral data transmission of miniaturized satellite,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7773-7789, Sep. 2021.
  8. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “Non-negative blind source separation for ill-conditioned mixtures via John ellipsoid,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 2209-2223, May 2021.
  9. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “An explicit and scene-adapted definition of convex self-similarity prior with application to unsupervised Sentinel-2 superresolution,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 5, pp. 3352-3365, May 2020.
  10. L. Zhuang, Chia-Hsiang Lin, M. A. T. Figueiredo, and J. M. Bioucas-Dias, “Regularization parameter selection in minimum volume hyperspectral unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 12, pp. 9858-9877, Dec. 2019.
  11. Y.-R. Syu, Chia-Hsiang Lin, and C.-Y. Chi, “An outlier-insensitive unmixing algorithm with spatially varying hyperspectral signatures,” IEEE Access, vol. 7, pp.15086-15101, Jan. 2019.
  12. Chia-Hsiang Lin, C.-Y. Chi, L. Chen, D. J. Miller, and Y.Wang, “Detection of sources in non-negative blind source separation by minimum description length criterion,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4022-4037, Sep. 2018.
  13. Chia-Hsiang Lin, R. Wu, W.-K. Ma, C.-Y. Chi, and Y. Wang, “Maximum volume inscribed ellipsoid: A new simplex-structured matrix factorization framework via facet enumeration and convex optimization,” SIAM Journal on Imaging Sciences, vol. 11, no. 2, pp. 1651-1679, Jun. 2018.
  14. Chia-Hsiang Lin, F. Ma, C.-Y. Chi, and C.-H. Hsieh, “A convex optimization based coupled non-negative matrix factorization algorithm for hyperspectral and multispectral data fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 3, pp. 1652-1667, Mar. 2018.
  15. G. Xu, Chia-Hsiang Lin, W. Ma, S. Chen, and C.-Y. Chi, “Outage constrained robust hybrid coordinated beamforming for massive MIMO enabled heterogeneous cellular networks,” IEEE Access, vol. 5, pp. 13601-13616, Mar. 2017.
  16. Chia-Hsiang Lin, C.-Y. Chi, Y.-H. Wang, and T.-H. Chan, “A fast hyperplane-based minimum-volume enclosing simplex algorithm for blind hyperspectral unmixing,” IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 1946-1961, Apr. 2016.
  17. A. Ambikapathi, T.-H. Chan, Chia-Hsiang Lin, F.-S. Yang, C.-Y. Chi, and Y. Wang, “Convex optimization-based compartmental pharmacokinetic analysis for prostate tumor characterization using DCE-MRI,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 707-720, Apr. 2016.
  18. Chia-Hsiang Lin, W.-K. Ma, W.-C. Li, C.-Y. Chi, and A. Ambikapathi, “Identifiability of the simplex volume minimization criterion for blind hyperspectral unmixing: The no pure-pixel case,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 10, pp. 5530-5546, Oct. 2015.
Conference
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  1. Chia-Hsiang Lin, M.-C. Chu, and H.-J. Chu, “High-dimensional multiresolution satellite image classification: An approach blending the advantages of convex optimization and deep learning,” accepted by IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  2. Chia-Hsiang Lin, T.-H. Lin, T.-H. Lin, and T.-H. Lin, “Fast reconstruction of hyperspectral image from its RGB counterpart using ADMM-Adam theory,” accepted by IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  3. Y. Liu, Chia-Hsiang Lin, and Y.-C. Kuo, “Low-rank representation with morphological-attribute-filter based regularization for hyperspectral anomaly detection,” accepted by IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  4. P.-W. Tang, and Chia-Hsiang Lin, “Hyperspectral dehazing using ADMM-Adam theory,” accepted by IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  5. P.-C. Chuan, J.-T. Lin, Chia-Hsiang Lin, P.-W. Tang, and Y. Liu, “A fast multidimensional data fusion algorithm for hyperspectral spatiotemporal super-resolution,” accepted by IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  6. T.-H. Lin, and Chia-Hsiang Lin, “Single hyperspectral image super-resolution using ADMM-Adam theory,” accepted by IEEE IGARSS, Kuala Lumpur, Malaysia, July 17-22, 2022.
  7. J.-T. Lin, and Chia-Hsiang Lin, “Real-time hyperspectral anomaly detection using collaborative superpixel representation with boundary refinement,” accepted by IEEE IGARSS, Kuala Lumpur, Malaysia, July 17-22, 2022.
  8. C.-H. Yu, Z.-C. Leng, Y. Liu, J.-Y. Huang, Chia-Hsiang Lin, and T.-Y. Tu, “A total solutioning workflow for sample processing and precise nuclei quantification in 3D tumor spheroids using unsupervised algorithm,” accepted by World Congress of Biomechanics, Taipei, Taiwan, Jul. 10-14, 2022.
  9. A. Hassanfiroozi, Chia-Hsiang Lin, J.-T. Lin, and P.-C.Wu, “High-performance metasurfaces for wavefront engineering,” accepted by Materials Research Society Fall Meeting and Exhibit, Boston, MA, USA, Nov. 28 - Dec. 3, 2021.
  10. C.-H. Kao, Chia-Hsiang Lin, S.-W. Jian, and P.-Y. Lin, “Solving hyperspectral single image super-resolution via fusion-based inverse problem transform,” The 34th IPPR Conference on Computer Vision, Graphics, and Image Processing, Taipei, Taiwan, Aug. 22-24, 2021. (“Outstanding Paper Award”)
  11. Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, Y.-C. Cheng, A. Hassanfiroozi, H.-C. Wu, H.-T. Kuo, and P.-C. Wu, “Toward high-performance plasmonic metasurfaces: From forward to inverse design approach,” accepted by SPIE Optics and Photonics, San Diego, CA, USA, Aug. 1-5, 2021.
  12. Chia-Hsiang Lin, C.-Y. Sie, P.-Y. Lin, and J.-T. Lin, “Fast unsupervised spatiotemporal super-resolution for multispectral satellite imaging using plug-and-play machinery strategy,” accepted by IEEE IGARSS, Brussels, Belgium, Jul. 11-16, 2021.
  13. Chia-Hsiang Lin, Y.-C. Lin, P.-W. Tang, and M.-C. Chu, “Deep hyperspectral tensor completion just using small data,” accepted by IEEE IGARSS, Brussels, Belgium, July 11-16, 2021.
  14. Chia-Hsiang Lin, and P.-W. Tang, “Inverse problem transform: Solving hyperspec- tral inpainting via deterministic compressed sensing,” accepted by IEEE WHISPERS, Amsterdam, Netherlands, Mar. 24-26, 2021.
  15. Chia-Hsiang Lin, and Y. Liu, “Blind hyperspectral inpainting via John ellipsoid,” accepted by IEEE WHISPERS, Amsterdam, Netherlands, Mar. 24-26, 2021.
  16. Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, and P.-C. Wu, “Inverse design of non- periodical metasurfaces via high-performance automatic optimization,” in Proc. Op- tics & Photonics Taiwan International Conference (OPTIC), Taipei, Taiwan, Dec. 3-5, 2020.
  17. C.-C. Hsu, W.-H. Zheng, H.-T. Yang, Chia-Hsiang Lin, and C.-H. Kao, “Rethinking relation between model stacking and recurrent neural networks for social media prediction,” in Proc. ACM Multimedia (MM), Seattle, WA, USA, Oct. 12-16, 2020. (“Invited Paper”) (“Top Performance Award”)
  18. Y.-C. Hung*, Chia-Hsiang Lin*, F.-Y. Wang, and S.-H. Yang, “Penetrating tera- hertz hyperspectral unmixing via Lo ̈wner-John ellipsoid: An unsupervised algorithm,” in Proc. IRMMW-THz, Buffalo, NY, USA, Sep. 13-18, 2020. (*Contributed Equally)
  19. C.-C. Hsu, Y.-C. Lin, C.-H. Kao, and Chia-Hsiang Lin, “Deep joint compression and super-resolution low-rank network for fast hyperspectral data transmission,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18, 2020. (“Invited Paper”)
  20. T.-H. Lin, Chia-Hsiang Lin, Y. Liu, and C.-H. Kao, “A simple spatial-spectral proximal compression method for high-dimensional imagery with proximal computing based blind reconstruction,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18, 2020. (“Invited Paper”)
  21. C.-Y. Sie, Chia-Hsiang Lin, P.-W. Tang, and Y.-C. Lin, “Solving the algebraic hyperspectral inpainting problem: A fast hyperplane geometry based approach,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18, 2020. (“Invited Paper”) (“Outstanding Paper Award”)
  22. Chia-Hsiang Lin, J. M. Bioucas-Dias, T.-H. Lin, Y.-C. Lin, and C.-H. Kao, “A new hyperspectral compressed sensing method for efficient satellite communications,” in Proc. IEEE SAM, Hangzhou, China, June 8-11, 2020. (“Invited Paper”)
  23. W.-C. Zheng, K.-H. Tseng, and Chia-Hsiang Lin, “Unsupervised change detection using convex relaxation and dynamic threshold selection in remotely sensed images,” American Geophysical Union (AGU) Fall Meeting, San Francisco, CA, USA, Dec. 9-13, 2019.
  24. C.-C. Hsu, and Chia-Hsiang Lin, “Dual reconstruction with densely connected residual network for single image super-resolution,” in Proc. IEEE ICCV, Seoul, Korea, Oct.27 - Nov. 2, 2019. (“Invited Paper”)
  25. C.-H. Wang, K.-H. Tseng, and Chia-Hsiang Lin, “Waterline detection using fusion based super-resolution of multispectral satellite image with self-similarity,” The 38th Conference on Surveying and Geoinformatics, Taoyuan, Taiwan, Aug. 29-30, 2019.
  26. T.-Y. Lin, H. Ren, and Chia-Hsiang Lin, “Bathymetry estimation via convex geometry in multispectral satellite imagery: A case study in Dongsha Atoll,” The 38th Conference on Surveying and Geoinformatics, Taoyuan, Taiwan, Aug. 29-30, 2019.
  27. W.-C. Zheng, Chia-Hsiang Lin, K.-H. Tseng, C.-Y. Huang, T.-H. Lin, C.-H. Wang, and C.-Y. Chi, “Unsupervised change detection in multitemporal multispectral satellite images: A convex relaxation approach,” in Proc. IEEE IGARSS, Yokohama, Japan, Jul. 28 - Aug. 2, 2019.
  28. C.-H. Wang, Chia-Hsiang Lin, J. M. Bioucas-Dias, W.-C. Zheng, and K.-H. Tseng, “Panchromatic sharpening of multispectral satellite imagery via an explicitly defined convex self-similarity regularization,” in Proc. IEEE IGARSS, Yokohama, Japan, Jul. 28 - Aug. 2, 2019. (“Interactive Session Prize Paper Award”)
  29. W-C. Zheng, Chia-Hsiang Lin, K.-H. Tseng, C.-Y. Huang, and T.-H. Lin, “Criterion design and large-scale optimization algorithm for blind change detection in multispectral images,” International Symposium on Remote Sensing, Taipei, Taiwan, Apr. 17-19, 2019.
  30. C.-H. Wang, Chia-Hsiang Lin, and K.-H. Tseng, “Patch similarity guided super-resolution algorithm for fusing panchromatic and multispectral images,” International Symposium on Remote Sensing, Taipei, Taiwan, Apr. 17-19, 2019.
  31. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “Linear spectral unmixing via matrix factorization: Identifiability criteria for sparse abundances,” in Proc. IEEE IGARSS, Valencia, Spain, Jul. 23-27, 2018.
  32. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “New theory for unmixing ill-conditioned hyperspectral mixtures,” in Proc. IEEE SAM, Sheffield, UK, Jul. 8-11, 2018. (“Invited Paper”)
  33. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “Provably and robust blind source separation of ill-conditioned hyperspectral mixtures,” in Proc. IEEE SSP, Freiburg, Germany, Jun. 10-13, 2018.
  34. G. Xu, Chia-Hsiang Lin, W. Ma, and C.-Y. Chi, “Outage constrained robust hybrid coordinated beamforming for massive MIMO enabled heterogeneous cellular networks,” in Proc. IEEE ICC, Paris, France, May 21-25, 2017.
  35. W.-K. Ma, Chia-Hsiang Lin, W.-C. Li, and C.-Y. Chi, “When can the minimum volume enclosing simplex identify the endmembers correctly when there is no pure pixel?,” in Proc. IEEE WHISPERS, Tokyo, Japan, Jun. 2-5, 2015.
  36. Chia-Hsiang Lin, C.-Y. Chi, Y.-H. Wang, and T.-H. Chan, “A fast hyperplane-based MVES algorithm for hyperspectral unmixing,” in Proc. IEEE ICASSP, Brisbane, Australia, Apr. 19-24, 2015.
  37. A. Ambikapathi, T.-H. Chan, Chia-Hsiang Lin, and C.-Y. Chi, “Convex geometry based outlier-insensitive estimation of number of endmembers in hyperspectral images,” in Proc. IEEE WHISPERS, Gainesville, Florida, USA, Jun. 25-28, 2013. (“Invited Paper”)
  38. Chia-Hsiang Lin, A. Ambikapathi, W.-C. Li, and C.-Y. Chi, “On the endmember identifiability of Craig’s criterion for hyperspectral unmixing: A statistical analysis for three-source case,” in Proc. IEEE ICASSP, Vancouver, Canada, May 26-31, 2013.
Patent
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Others
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  1. C.-Y. Chi, W.-C. Li, and Chia-Hsiang Lin, Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications, CRC Press, Boca Raton, FL, Feb. 2017. (Available in CRC Press; also available in Taiwan SCI-TECH.)
  2. Chinese version (信号处理与通中的凸优化: 从基础到应用) translated by Chen Xiang (陈翔) and Shen Chao (沈超) and published by 电子工业出版社, Dec. 2020.
Projects
  1. Advanced Blind Source Separation and Hyperspectral Super-resolution Imaging via Convex Geometry and Big Data Optimization, Ministry of Science and Technology (Einstein Program), from 2018 to 2023
  2. Mathematical Theory and Metagrating Design for Advanced Satellite Imaging, Ministry of Education, 2019 to 2022
  3. Development of Dynamic Electrical Resistivity Tomography Spectrum: Integration of Numerical Solutions and Disaster Prevention Applications (Co-PI), Ministry of Education, 2019
Students
Current Academic Year Lab Members
Ph.D.
Tzu-Hsuan Lin
Po-Wei Tang
Yangrui Liu
Jhao-Ting Lin
Master
Man-Chun Chu
Shao-Wei Jian
Zi-Chao Leng
Ting-Hsuan Lin
Yo-Yu Lai
Gheng-Ying Hsieh
Yu-Chun Kuo
Kuan-Huang Yu
Yu-Che Tung
Guang-Jie Wei
Yi-Xuan Zhong
Chen-Yu Kuo
You-Yao Chen
Si-Sheng Young
Undergraduate
Yen-Ting Ho
Shang-Jun Shi
Jong-Zing Sui
Che-Yu Wu
Graduates of all Previous Years
Master
109
Chi-Hung Kao   Cheng-Yu Sie   Yen-Cheng Lin
Bachelor
109
Yi-Xun Li   Zi-Chao Leng
110
Hsiao-Ching Huang   Wan-Hsuan Lin   Pai-Chuan Chang   You-Yao Chen
Honors
  1. Outstanding Paper Award from IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP), 2022.
  2. Outstanding Poster Exhibition Award, Communications Engineering Program, Ministry of Science and Technology, 2022.
  3. Best Young Professional Member Award, IEEE Tainan Section, 2021.
  4. Outstanding Paper Award from IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP), 2021.
  5. Excellent Research Project Award, Communications Engineering Program, Ministry of Science and Technology, 2021.
  6. Top Performance Award, Social Media Prediction Challenge, ACM Multimedia, 2020.
  7. Outstanding Paper Award from IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP), 2020.
  8. Symposium Interactive Session Prize Paper Award (IGARSS'19) from IEEE Geoscience & Remote Sensing Society, 2020.
  9. 3rd Place (Objective) and 5th Place (Subjective), AIM Real World Super-Resolution Challenge, IEEE International Conference on Computer Vision (ICCV), 2019.
  10. Einstein Grant Award from Ministry of Science and Technology, from 2018 to 2023.
  11. Best Doctoral Dissertation Award from IEEE Geoscience & Remote Sensing Society, 2016.
  12. Outstanding Dissertation Award, Chinese Image Processing and Pattern Recognition (IPPR) Society, 2016.