Nan Wang Assis.Professor

Research interests:
Hyperspectral unmixing;Remote sensing Time series analysis

Education Experiences:
2009.09 2014.07 State Key Laboratory of Remote Sensing and Surveying and Mapping Information Engineering A doctor’s degree in photogrammetry and remote sensing 2005.09 2009.07 the PLA information engineering university institute of surveying and mapping A bachelor's degree in remote sensing science and technology

Work Experiences:
2014.07- now Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Post Doctor

  1. Wang N, Du B, Zhang L*. An endmember dissimilarity constrained non-negative matrix factorization method for hyperspectral unmixing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 554- 569. (SCI, IF = 2.827)
  2. Wang, N., Du, B., Zhang, L*. Abundance Characteristic Based Independent Component Analysis for Hyperspectral Unmxing[J]. IEEE Transaction on Geoscience and remote sensing, 2015,53(1): 416-428. (SCI)
  3. Du B. ,Wang S., Wang N*, Zhang L., Tao D., Zhang L. Hyperspectral signal unmixing based on constrained NMF approach, Neurocomputing, PP: 1-9, 2015 under review. (SCI)
  4. Wang N., Zhang L., and Du B., An Endmember Dissimilarity Based Non-negative Matrix Factorization Method for Hyperspectral Unmixing [C] in 2012 4tn Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing WHISPERS, PP:1-4 June .4-June. 7, 2012,Shanghai, China, (EI)
  5. Du, B., Wang, N., Zhang, L, Tao D., Hyperspectral medical images unmixing for cancer screening based on rotational independent component analysis[C], International Conference on Intelligence Science and Big Data Engineering ISCIDE, PP:1-4 Aug.2- July.31, 2013, Beijing China. (EI)
  6. Wang, N., Du, B., Zhang, L, An hyperspectral unmixing approach with spatial auto-correlation information[C],2014 International Geoscience and Remote Sensing Symposium IGRASS, PP:1-4 July.13- July.18, 2014, Quebec, Canada (EI)
  7. Wang S., Wang N*, Zhang L., Tao D. Du B. ,, A K-L Divergence Constrained Sparse NMF for Hyperspectral Signal Unmixing[C], International Conference on Security, Pattern Analysis, and Cybernetics ICSPAC, PP:1-4 Otc.18-19, 2014, Wuhan, China. (EI)
  8. Wang N, Zhang L., Semi-supervised hyperspectral unmixing approach based nonnegative matrix factorization[C], SPIE conference on Satellite Data Compression, Communications, and Processing XI, PP:1-4 April 20-24, 2015 Baltimore, United States (EI)

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