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/ext.62400-1666)
Background
Educations
B.S. in Electrical Engineering, National Tsing Hua University (2010)
Ph.D. in Communications Engineering, National Tsing Hua University (2016)
Experiences
2014, Research Assistant, The Chinese University of Hong Kong, HK
2015-2016, Research Assistant, Virginia Tech, VA, USA
2017, Postdoctoral Researcher, The Chinese University of Hong Kong, HK
2017-2018, Postdoctoral Researcher, University of Lisbon, Portugal
2018, Assistant Professor, Center for Space and Remote Sensing Research, National Central University
2019-2021, Assistant Professor, Department of Electrical Engineering, National Cheng Kung University
2019-2021, Assistant Professor, Miin Wu School of Computing, National Cheng Kung University
2019, Visiting Professor, University of Lisbon, Portugal
2022-2023, Director, Division of Algorithm and Optimization, Center for Data Science, National Cheng Kung University
2022-2024，Affiliated Member, Division of Data Analysis and Software Development, Taiwan Space Union
2023-2025, Director, Division of International Relations, Office of International Affairs, National Cheng Kung University
2022-present, Associate Professor, Miin Wu School of Computing, National Cheng Kung University
2022-present, Associate Professor, Department of Electrical Engineering, National Cheng Kung University
2024-present，Affiliated Member, Division of International Collaboration, Taiwan Space Union
2024-present，Affiliated Member, Mathematics Division, National Center for Theoretical Sciences (NCTS)
2025-present，Deputy Director, Information Section, Kaohsiung Chapter, The Chinese Institute of Engineers (CIE)
Specialities
  • Remote sensing / Satellite data analysis
  • Hyperspectral / Multispectral / RGB image processing
  • Blind signal processing / Unsupervised machine learning
  • Artificial intelligence / Machine learning / Deep learning
  • Quantum deep learning / Hybrid quantum-classical AI
  • Quantum image processing / Quantum machine learning
  • Fast algorithm design / Convex optimization theory
  • 3D optical foreign object detection / Image super-resolution
  • 3D computer vision / Pattern recognition
  • Biomedical science / Neuroscience
  • 5G/6G wireless communications
Publication
Journal
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  1. Chia-Hsiang Lin, W.-H. Li, S.-M. Hsu, and H.-J. Chu, “A full-spectrum time-reversal deep network for label imbalance learning: A case study with novel ground-truth labeling strategy for mangrove forest change detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025.
  2. P.-W. Tang, Chia-Hsiang Lin, J.-K. Huang, and A. R. Huete, “A quantum-empowered SPEI drought forecasting algorithm using spatially-aware mamba network,” IEEE Transactions on Geoscience and Remote Sensing, 2025.
  3. Chia-Hsiang Lin, and S.-S. Young, “HyperKING: Quantum-classical generative adversarial networks for hyperspectral image restoration,” IEEE Transactions on Geoscience and Remote Sensing, 2025.
  4. Chia-Hsiang Lin, Z.-C. Leng, C.-H. Yu, L. Bharali, C.-L. Lin, B.-H. Mao, and T.-Y. Tu, “Microscopic-based analysis of nuclei in spheroids via SUNSHINE: An on-chip workflow integrating optical clearing, fluorescence calibration and supervoxel segmentation,” Computers in Biology and Medicine, 2025.
  5. Chia-Hsiang Lin, and J.-T. Lin, “PRIME: Unsupervised multispectral unmixing using virtual quantum prism and convex geometry,” IEEE Transactions on Geoscience and Remote Sensing, 2025.
  6. Chia-Hsiang Lin, P.-W. Tang, and A. R. Huete, “Quantum feature-empowered deep classification for fast mangrove mapping,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
  7. Chia-Hsiang Lin, T.-H. Lin, and J. Chanussot, “Quantum information-empowered graph neural network for hyperspectral change detection,” IEEE Transactions on Geoscience and Remote Sensing, 2024
  8. J.-T. Lin, and Chia-Hsiang Lin, “SuperRPCA: A collaborative superpixel representation prior-aided RPCA for hyperspectral anomaly detection,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
  9. S.-S. Young, Chia-Hsiang Lin, and Z.-C. Leng, “Unsupervised abundance matrix reconstruction transformer guided fractional attention mechanism for hyperspectral anomaly detection,” IEEE Transactions on Neural Networks and Learning Systems, 2024.
  10. Chia-Hsiang Lin, C.-Y. Hsieh, and J.-T. Lin, “CODE-IF: A convex/deep image fusion algorithm for efficient hyperspectral super-resolution,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
  11. Chia-Hsiang Lin, and S.-S. Young, “Signal subspace identification for incomplete hyperspectral image with applications to various inverse problems,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
  12. Chia-Hsiang Lin, C.-C. Hsu, S.-S. Young, C.-Y. Hsieh, and S.-C. Tai, “QRCODE: Quasi-Residual Convex Deep Network for Fusing Misaligned Hyperspectral and Multispectral Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-15, 2024.
  13. P.-W. Tang, Chia-Hsiang Lin, and Y.-R. Liu,“Transformer-driven inverse problem transform for fast blind hyperspectral image dehazing,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
  14. Chia-Hsiang Lin, S.-H. Huang, T.-H. Lin, and P.-C. Wu, “Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory,” Nature Communications, 2023.
  15. Chia-Hsiang Lin, M.-C. Chu, and P.-W. Tang,“CODE-MM: Convex deep mangrove mapping algorithm based on optical satellite images,” IEEE Transactions on Geoscience and Remote Sensing, 2023.
  16. Chia-Hsiang Lin, and T.-H. Lin, "Hyperspectral change detection using semi-supervised graph neural network and convex deep learning," IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023.
  17. Chia-Hsiang Lin, and Y.-Y. Chen, “HyperQUEEN: Hyperspectral quantum deep network for image restoration,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-20, 2023.
  18. Chia-Hsiang Lin, Y. Liu, C.-Y. Chi, C.-C. Hsu, H. Ren, and T. Q. S. Quek, “Hyperspectral tensor completion using low-rank modeling and convex functional analysis,” IEEE Transactions on Neural Networks and Learning Systems, 2023.
  19. P.-C. Chuan, J.-T. Lin, Chia-Hsiang Lin, P.-W. Tang, and Y. Liu, “Optimization-based hyperspectral spatiotemporal super-resolution,” IEEE Access, vol. 10, pp. 37477-37494, 2022.
  20. 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,” IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4799-4815, 2022.
  21. 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,” Bioinformatics, vol. 38, no, 5, pp. 1403-1410, 2022.
  22. 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,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2021.
  23. 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, 2021.
  24. Chia-Hsiang Lin and J. M. Bioucas-Dias, “Nonnegative 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.
  25. Chia-Hsiang Lin and T.-H. Lin, “All-addition hyperspectral compressed sensing for metasurface-driven miniaturized satellite,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2021.
  26. 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, 2020.
  27. 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 super-resolution,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 5, pp. 3352-3365, May 2020.
  28. 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.
  29. Y.-R. Syu, Chia-Hsiang Lin, C.-Y. Chi, “An outlier-insensitive unmixing algorithm with spatially varying hyperspectral signatures,” IEEE Access, vol. 7, pp. 15086-15101, Jan. 2019.
  30. 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.
  31. 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, 2018.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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, C.-Y. Liang, and P.-W. Tang, “μ-HCD: Annotation-free and scalable long short-term memory hyperspectral calibration for change detection,” IEEE WHISPERS, Bellaterra, Spain, Nov. 12-14, 2025.
  2. Chia-Hsiang Lin, S.-S. Young, and R. Langari, “Quantum fuzzy detection of anomaly objects using hyperspectral generative adversarial learning,” IEEE WHISPERS, Bellaterra, Spain, Nov. 12-14, 2025.
  3. Chia-Hsiang Lin, J.-T. Chen, and S.-M. Hsu, “Interpretable Sentinel-converted hyperspectral data using deep image prior and spatiospectral super-resolution deep unfolding,” IEEE WHISPERS, Bellaterra, Spain, Nov. 12-14, 2025.
  4. S.-J. Huang, Chia-Hsiang Lin, and P.-W. Tang “Quantum-inspired deep hyperspectral dehazing algorithm,” IEEE WHISPERS, Bellaterra, Spain, Nov. 12-14, 2025.
  5. S.-S. Young, Chia-Hsiang Lin, S.-J. Huang, and S.-Q. Chen, “Fuzzy-driven quantum detection with spatial-frequency hyperspectral network,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Taipei, Taiwan, Aug. 24-26, 2025.
  6. Chia-Hsiang Lin, and S.-S. Young, “Quantum-inspired virtual prism for multispectral unmixing,” IEEE IGARSS, Brisbane, Australia, Aug. 3-8, 2025.
  7. Chia-Hsiang Lin, P.-W. Tang, J.-K. Huang, C. Kung, and A. R. Huete, “Quantum-assisted mangrove forest mapping algorithm,” IEEE IGARSS, Brisbane, Australia, Aug. 3-8, 2025.
  8. Chia-Hsiang Lin, P.-Y. Chiu, J.-T. Lin, S.-P. Chen, and Charles Lin, “HydroQUEEN: Hydrological detection using CYGNSS and quantum deep AI,” IEEE IGARSS, Brisbane, Australia, Aug. 3-8, 2025.
  9. Chia-Hsiang Lin, T.-H. Lin, C.-Y. Liang, and J. Chanussot, “Hybrid quantum-classical network for hyperspectral change detection,” IEEE IGARSS, Brisbane, Australia, Aug. 3-8, 2025.
  10. Chia-Hsiang Lin, S.-S. Young, C. Liu, L.-Y. Chang, and T.-Y. Liao, “Image resolution enhancing of Sentinel-2 red edge bands via Pléiades-1 multispectral data and fast convex deep learning,” SPIE Asia-Pacific Remote Sensing Symposium, Kaohsiung, Taiwan, Dec. 2-4, 2024.
  11. C.-W. Fan, C.-F. Cheng, Z.-C. Leng, C. Hsu, Chia-Hsiang Lin, and Y.-N. Chang, “A fast unsupervised small-data-driven neuroimaging algorithm for detection of Alzheimer's disease,” TSBME Global Conference on Biomedical Engineering (GCBME), Tainan, Taiwan, Nov. 1-3, 2024.
  12. Z.-C. Leng, B.-H. Mao, C.-H. Yu, Chia-Hsiang Lin, and T.-Y. Tu, “Evaluation of cell nuclei in spheroids using SENSE: A method combining fluorescence calibration and supervoxel segmentation techniques,” TSBME Global Conference on Biomedical Engineering (GCBME), Tainan, Taiwan, Nov. 1-3, 2024.
  13. S.-S. Young, Chia-Hsiang Lin, J.-Y. Chen, and J.-K. Huang, “HyperQUEEN-CD: Quantum neural network for unsupervised hyperspectral change detection,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Hualien, Taiwan, Aug. 18-20, 2024.
  14. G.-J. Wei, Chia-Hsiang Lin, and S.-M. Hsu, “A channel-wise quantum attention mechanism for RGB and hyperspectral image super-resolution,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Hualien, Taiwan, Aug. 18-20, 2024.
  15. (Invited Paper) S.-M. Hsu, T.-H. Lin, and Chia-Hsiang Lin, “HyperQUEEN-MF: Hyperspectral quantum deep network with multi-scale feature fusion for quantum image super-resolution,” accepted by IEEE SAM, Corvallis, OR, USA, July 8-11, 2024.
  16. Chia-Hsiang Lin, C.-Y. Kuo, and S.-S. Young, “Quantum adversarial learning for hyperspectral remote sensing,” IEEE IGARSS, Athens, Greece, July 7-12, 2024.
  17. Chia-Hsiang Lin, S.-S. Young, L.-Y. Chang, and Cynthia S.J. Liu, “Synthesis of high-resolution FORMOSAT-8 satellite image using fast convex deep learning algorithm,” IEEE IGARSS, Athens, Greece, July 7-12, 2024.
  18. S.-S. Young, Chia-Hsiang Lin, and J.-T. Lin, “CiDAR-Former: Cosine-weighting deep abundance reconstruction transformer for fast unsupervised hyperspectral anomaly detection,” IEEE WHISPERS, Athens, Greece, Oct. 31-Nov. 2, 2023.
  19. (Invited Paper) T.-H. Lin, and Chia-Hsiang Lin, and S.-S. Young, “GNN-based small-data learning with area-control mechanism for hyperspectral satellite change detection,” Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Taipei, Taiwan, Oct. 31-Nov. 3, 2023.
  20. Chia-Hsiang Lin, and Y.-Y. Chen, “Quantum deep hyperspectral satellite remote sensing,” IEEE IGARSS, Pasadena, California, July 16-21, 2023.
  21. 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,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  22. 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,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  23. Y. Liu, Chia-Hsiang Lin, and Y.-C. Kuo, “Low-rank representation with morphological-attribute-filter based regularization for hyperspectral anomaly detection,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  24. P.-W. Tang, and Chia-Hsiang Lin, “Hyperspectral dehazing using ADMM-Adam theory,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  25. 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,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  26. Chia-Hsiang Lin, M.-C. Chu, and T.-H. Lin, “Convex optimization aided deep classification of ESA's Sentinel-2 satellite image,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Nantou, Taiwan, Aug. 18-20, 2022.
  27. Chia-Hsiang Lin, T.-H. Lin, Y.-Y. Lai, K.-H. Yu, and T.-H. Lin, “Reconstructing hyperspectral image from its RGB counterpart: An approach blending deep learning and convex optimization,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Nantou, Taiwan, Aug. 18-20, 2022.
  28. Chia-Hsiang Lin, C.-C. Hsu, S.-W. Jian, and Y.-C. Kuo, “Convex feature-aligned quasiresidual network for alignment-free deep high-dimensional image fusion,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Nantou, Taiwan, Aug. 18-20, 2022.
  29. Y. Liu, Chia-Hsiang Lin, Z.-C. Leng, and C.-Y. Hsieh, “Convex hyperspectral anomaly detection algorithm using random dictionary without column-sparsity-promoting regularization,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Nantou, Taiwan, Aug. 18-20, 2022.
  30. T.-H. Lin, and Chia-Hsiang Lin, “Single hyperspectral image super-resolution using ADMM-Adam theory,” IEEE IGARSS, Kuala Lumpur, Malaysia, July 17-22, 2022.
  31. J.-T. Lin, and Chia-Hsiang Lin, “Real-time hyperspectral anomaly detection using collaborative superpixel representation with boundary refinement,” IEEE IGARSS, Kuala Lumpur, Malaysia, July 17-22, 2022.
  32. 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,” World Congress of Biomechanics, Taipei, Taiwan, Jul. 10-14, 2022.
  33. A. Hassanfiroozi, Chia-Hsiang Lin, J.-T. Lin, and P.-C. Wu, “High-performance metasurfaces for wavefront engineering,” Materials Research Society Fall Meeting & Exhibit, Boston, MA, USA, Nov. 28 - Dec. 3, 2021.
  34. 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.
  35. 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,” SPIE Optics and Photonics, San Diego, CA, USA, Aug. 1-5, 2021.
  36. 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,” IEEE IGARSS, Brussels, Belgium, July 11-16, 2021.
  37. Chia-Hsiang Lin, Y.-C. Lin, P.-W. Tang, and M.-C. Chu, “Deep hyperspectral tensor completion just using small data,” IEEE IGARSS, Brussels, Belgium, July 11-16, 2021.
  38. Chia-Hsiang Lin and P.-W. Tang, “Inverse problem transform: Solving hyperspectral inpainting via deterministic compressed sensing,” IEEE WHISPERS, Amsterdam, Netherlands, Mar. 24-26, 2021.
  39. Chia-Hsiang Lin and Y. Liu, “Blind hyperspectral inpainting via John ellipsoid,” IEEE WHISPERS, Amsterdam, Netherlands, Mar. 24-26, 2021.
  40. Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, and P.-C. Wu, “Inverse design of non-periodical metasurfaces via high-performance automatic optimization,” Optics & Photonics Taiwan International Conference (OPTIC), Taipei, Taiwan, Dec. 3-5, 2020.
  41. (Invited Paper) (Top Performance Award) 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,” ACM Multimedia, Seattle, WA, USA, Oct. 12-16, 2020.
  42. Y.-C. Hung*, Chia-Hsiang Lin*, F.-Y. Wang, and S.-H. Yang, “Penetrating terahertz hyperspectral unmixing via Löwner-John ellipsoid: An unsupervised algorithm,” International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), Buffalo, NY, USA, Sep. 13-18, 2020. (*Contributed Equally)
  43. (Invited Paper) 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.
  44. (Invited Paper) 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.
  45. (Invited Paper) (Outstanding Paper Award) 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.
  46. (Invited Paper) 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,” IEEE SAM, Hangzhou, China, June 8-11, 2020.
  47. 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.
  48. (Invited Paper) C.-C. Hsu, and Chia-Hsiang Lin, “Dual reconstruction with densely connected residual network for single image super-resolution,” IEEE International Conference on Computer Vision (ICCV), Seoul, Korea, Oct. 27 - Nov. 2, 2019.
  49. 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.
  50. 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.
  51. 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,” IEEE IGARSS, Yokohama, Japan, July 28 - Aug. 2, 2019.
  52. (Interactive Session Prize Paper Award) C.-H. Wang, Chia-Hsiang Lin, J. M. Bioucas-Dias, W.-C. Zheng, K.-H. Tseng, “Panchromatic sharpening of multispectral satellite imagery via an explicitly defined convex self-similarity regularization” IEEE IGARSS, Yokohama, Japan, July 28 - Aug. 2, 2019.
  53. 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.
  54. 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.
  55. 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, July 23-27, 2018.
  56. (Invited Paper) Chia-Hsiang Lin and J. M. Bioucas-Dias, “New theory for unmixing ill-conditioned hyperspectral mixtures,” in Proc. IEEE SAM, Sheffield, UK, July 8-11, 2018.
  57. 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, June 10-13, 2018.
  58. 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.
  59. 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, June 2-5, 2015.
  60. 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.
  61. (Invited Paper) 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, June 25-28, 2013.
  62. 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. [Book] 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. [Book] 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.
  3. [Technology transfer] Licensee: E Ink Holdings Inc.; Technology Title: Spatial Dithering via Non-Quantum or Quantum Deep Learning, 2024
Projects
  1. Spatial Dithering via Non-Quantum or Quantum Deep Learning, E Ink Holdings Inc., PI, from 2024 to 2027
  2. International Integrated Collaboration Project for The Texas A&M University System (TAMUS) & University Academic Alliance in Taiwan (UAAT)：Quantum AI and Convex Algorithm for Optical Satellite Anomaly Detection, Ministry of Education, PI, from 2024 to 2026
  3. Application of Intelligent Underwater Vehicles and Deep Learning-Empowered Image Analysis for Developing a High-Temporal-Resolution Monitoring Platform of Taiwan's Coastal Ecosystem(Co-PI), Ministry of Education, from 2025 to 2026
  4. HyperQUEEN: Hyperspectral Quantum Deep Network for Space Remote, National Science and Technology Council, PI, from 2023 to 2027
  5. A Study on the Simulation of Satellite Red-Edge Band Imagery Using Intelligent Computing Methods,National Space Organization (NSPO), PI, from 2023 to 2024
  6. Mathematical Theory and Metagrating Design for Advanced Satellite Imaging, Ministry of Education, PI, from 2019 to 2022
  7. Development of Dynamic Electrical Resistivity Tomography Spectrum: Integration of Numerical Solutions and Disaster Prevention Applications (Co-PI), Ministry of Education, 2019
  8. Advanced Blind Source Separation and Hyperspectral Super-resolution Imaging via Convex Geometry and Big Data Optimization, Ministry of Science and Technology (Einstein Program), PI, from 2018 to 2023
Classes
2019 Spring
2019 Fall
2020 Spring
2020 Fall
2021 Spring
2021 Fall
2022 Spring
2022 Fall
2023 Spring
2023 Fall
2024 Spring
2024 Fall
2025 Spring
2025 Fall
Students
Current Academic Year Lab Members
Ph.D.
Tzu-Hsuan Lin
Po-Wei Tang
Jhao-Ting Lin
Zi-Chao Leng
Si-Sheng Young
Shih-Min Hsu
Che Kung
Jian-Kai Huang
Master
Wei-Heng Li
Jhih-Yan Chen
Cheng-Wei Fan
Yung-Sheng Liang
Wei-Chun Lin
Bo-Yu Lin
Shih-Je Huang
Jui-Ting Chen
Po-Ying Chiu
Ching-Yun Liang
Wei-Chih Liu
Yu-En Chiu
Kuang-Te Chang
Chen-Yu Liu
Undergraduate
Chen Chen
Tzu-Hsiang Wei
Zhi-Xiang Kuo
Chi-Fei Chang
Honors
  1. 李國鼎研究獎 K. T. Li Research Award, 2025.
  2. Outstanding Patent, Center for Corporate Relations and Technology Transfer, NCKU, 2025. (企業關係與技轉中心優質專利徵選)
  3. Excellent Poster Exhibition Award, Communications Engineering Program, National Science and Technology Council, 2025.
  4. IEEE Senior Member, 2024.
  5. 臺灣綜合大學「年輕學者創新研發優等獎」（工程領域）Excellence Award from Young Scholar Innovative R&D Results Selection of Taiwan Comprehensive University System, 2024.
  6. GENESYS Logic Paper Excellence Award, 2024. (創惟科技論文「卓越獎」36 萬)
  7. Excellent EMI Teaching Innovation Award of University Social Responsibility (USR) from NCKU, 2024. (教學創新與大學社會責任：EMI創新教學優良教師)
  8. 中華民國青年獎章（學術拔萃類）The Republic of China Youth Medal Award, 2024.
  9. Excellent Poster Exhibition Award, Communications Engineering Program, National Science and Technology Council, 2024.
  10. 國科會 2030 跨世代「新秀學者」Emerging Young Scholar Award from National Science and Technology Council (2030 Cross-Generation Young Scholars Program), from 2023 to 2027.
  11. Teaching Excellence Award from NCKU, 2023. (校級教學優良獎)
  12. Excellent Mentor Award from NCKU, 2023. (輔導優良導師)
  13. Excellent Poster Exhibition Award, Communications Engineering Program, National Science and Technology Council, 2023.
  14. 中國電機工程學會「優秀青年電機工程師獎」Outstanding Youth Electrical Engineer Award from The Chinese Institute of Electrical Engineering, 2022.
  15. FutureTech Award from National Science & Technology Council, 2022. (國科會「未來科技獎」)
  16. Outstanding Paper Award (x2) from IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP), 2022.
  17. Excellent Poster Exhibition Award, Communications Engineering Program, Ministry of Science and Technology, 2022.
  18. IEEE 中華民國第一分會「最佳年輕學者」Best Young Professional Member Award from IEEE Tainan Section, 2021.
  19. Outstanding Paper Award from IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP), 2021.
  20. Excellent Research Project Award, Communications Engineering Program, Ministry of Science and Technology, 2021.
  21. Top Performance Award, Social Media Prediction Challenge, ACM Multimedia, 2020.
  22. Outstanding Paper Award from IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP), 2020.
  23. Symposium Interactive Session Prize Paper Award (IGARSS'19) from IEEE Geoscience & Remote Sensing Society, 2020.
  24. 3rd Place (Objective) and 5th Place (Subjective), AIM Real World Super-Resolution Challenge, IEEE International Conference on Computer Vision (ICCV), 2019.
  25. 科技部「愛因斯坦學者」Einstein Grant Award from Ministry of Science and Technology, from 2018 to 2023.
  26. Best Doctoral Dissertation Award from IEEE Geoscience & Remote Sensing Society, 2016.
  27. Outstanding Doctoral Dissertation Award from Chinese Image Processing and Pattern Recognition (IPPR) Society, 2016.
NCKU EE