Matlab Codes for Download
The purpose of this webpage is to provide reproducible research, and to allow others who want to verify my algorithms without implemting them from scratch. The Matlab codes are for non-commercial use.
The four software packages (GENE, CAMNS-AVM, nLCA-IVM, MVES, CAMNS-LP) are developed based on SeDuMi (a free convex optimization solver). Please download and install it before executing the codes.
PCANet : Matlab codes of PCANet, PCA filters (MultiPIE), and demos for MNIST and CIFAR10.
- T.-H. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, and Y. Ma, "PCANet: A simple deep learning baseline for image classification?" IEEE Trans. Image Processing, 2015.
Lq SD-SOMP : Matlab codes of Lq SD-SOMP for hyperspectral unmixing.
- X. Fu, W.-K. Ma, T.-H. Chan, and J. M. Bioucas-Dias, "Self-dictionary sparse regression for hyperspectral unmixing: Greedy pursuit and pure pixel search are related," IEEE Journal of Selected Topics in Signal Processing, 2015.
DL-SILT : Matlab codes of the dictionary learning algorithm in SILT.
- L. Zhuang, T.-H. Chan, Allen Y. Yang, S. S. Sastry, and Y. Ma, "Sparse illumination learning and transfer for single-sample face recognition with image corruption and misalignment," International Journal of Computer Vision (IJCV), July, 2014.
SNNMF : Matlab codes of SNNMF for high resolution hyperspectral imaging.
- Eliot Wycoff, T.-H. Chan, K. Jia, W.-K. Ma and Y. Ma, "A non-negative sparse promoting algorithm for high resolution hyperspectral imaging," in IEEE ICASSP, 2013.
GENE-CH & GENE-AH : Matlab codes of GENE-CH and GENE-AH algorithms.
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A. Ambikapathi, T.-H. Chan, and C.-Y. Chi, “Convex geometry based estimation of number of endmembers in hyperspectral images,” in IEEE ICASSP, Kyoto, Japan, Mar. 25-30, 2012.
- A. Ambikapathi, T.-H. Chan, C.-Y. Chi, and K. Keizer, “Hyperspectral data geometry based estimation of number of endmembers using p−norm based pure pixel identification,”IEEE Trans. Geoscience and Remote Sensing, 2012.
RASF & RASF-NP : Matlab codes of RASF and RASF-NP algorithms.
- H.-E. Huang, T.-H. Chan, A. Ambikapathi, W.-K. Ma, and C.-Y. Chi, "Outlier-robust dimension reduction and its impact on hyperspectral endmember extraction," in IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Shanghai, China, June 5-7, 2012. (Invited paper)
ADVMM & SDVMM : Matlab codes of ADVMM and SDVMM algorithms.
- T.-H. Chan, Ji-Yuan Liu, A. Ambikapathi, W.-K. Ma and C.-Y. Chi, “Fast algorithms for robust hyperspectral endmember extraction based on worst-case simplex volume maximization,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012.
- T.-H. Chan, A. Ambikapathi, W.-K. Ma, and C.-Y. Chi, “Robust affine set fitting and fast simplex volume max-min for hyperspectral endmember extraction,” IEEE Trans. Geoscience and Remote Sensing.
AVMAX & SVMAX & WAVMAX : Matlab codes of AVMAX, SVMAX, and WAVMAX algorithms.
- T.-H. Chan, W.-K. Ma, A. Ambikapathi, and C.-Y. Chi, “A simplex volume maximization framework for hyperspectral endmember extraction,” IEEE Trans. Geoscience and Remote Sensing – Special Issue on Spectral Unmixing of Remotely Sensed Data, vol. 49, no. 11, pp. 4177-4193, Nov. 2011.
- T.-H. Chan, W.-K. Ma, A. Ambikapathi, and C.-Y. Chi, "Robust endmember extraction using worst-case simplex volume maximization," in Proc. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, Portugal, June 6-9, 2011. (Best paper award)
- T.-H. Chan, J.-Y. Liou, A. Ambikapathi, W.-K. Ma and C.-Y. Chi, “Fast algorithms for robust hyperspectral endmember extraction based on worst-case simplex volume maximization,” in IEEE ICASSP, Kyoto, Japan, Mar. 25-30, 2012.
RMVES: Matlab codes of RMVES algorithm.
- A. Ambikapathi, T.-H. Chan, W.-K. Ma, and C.-Y. Chi, “Chance constrained robust minimum volume enclosing simplex algorithm for hyperspectral unmixing,” IEEE Trans. Geoscience and Remote Sensing – Special Issue on Spectral Unmixing of Remotely Sensed Data, vol. 49, no. 11, pp. 4194-4209, Nov. 2011.
- A. Ambikapathi, T.-H. Chan, W.-K. Ma, and C.-Y. Chi, “A robust minimum-volume enclosing simplex algorithm for hyperspectral unmixing,” in Proc. IEEE ICASSP, Dallas, Texas, Mar. 14-19, 2010, pp. 1202-1205.
CAMNS-AVM: Matlab codes of CAMNS-AVM algorithm and three-human-face example.
- W.-K. Ma, T.-H. Chan, C.-Y. Chi, and Y. Wang, Convex analysis for non-negative blind source separation with application in imaging, in Chapter 7, Convex Optimization in Signal Processing and Communications, Editors: D. P. Palomar and Y. C. Eldar, UK: Cambridge University Press, 2010.
nLCA-IVM: Matlab codes of nLCA-IVM algorithm and X-ray image example.
- F.-Y. Wang, C.-Y. Chi, T.-H. Chan, and Y. Wang, “Nonnegative least correlated component analysis for separation of dependent sources by volume maximization,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 875-888, May 2010.
MVES: Matlab codes of MVES algorithm.
- T.-H. Chan, C.-Y. Chi, Y.-M. Huang, and W.-K. Ma, “A convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing,” IEEE Trans. Signal Processing, vol. 57, no. 11, pp. 4418-4432, Nov. 2009.
- T.-H. Chan, C.-Y. Chi, Y.-M. Huang and W.-K. Ma, “A convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 19-24, 2009, pp. 1089-1092.
CAMNS-LP: Matlab codes for CAMNS-LP algorithm and three-human-face example.
- T.-H. Chan, W.-K. Ma, C.-Y. Chi and Y. Wang, “A convex analysis framework for blind separation of non-negative sources,” IEEE Trans. Signal Processing, vol. 56, no. 10, pp. 5120–5134, Oct. 2008.
If there are any bugs in the codes and questions, please send an email to thchan@ieee.org. Thank you!