Pedram Ghamisi

 
Machine Learning Group Leader at HZDR-HIF [Link]
CTO and co-founder at VasoGnosis [Link]
 
Visiting Professor at the Institute of Advanced Research in Artificial Intelligence (IARAI) [Link]
 
Co-chair of IEEE Image Analysis and Data Fusion Committee [Link]
For more info, please check my personal website [Link]

Email: p.ghamisi@gmail.com 
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Hello! I'm Pedram

 

Here you can find more information on my publication list and ongoing projects. For detailed information about my professional activities please see:

[http://pedram-ghamisi.com]

I am a senior member of IEEE and act as the associate editor of IEEE Geoscience and Remote Sensing Letters (GRSL) and Remote Sensing (MDPI). 

 
Journal Papers
  1. P. Ghamisi, A. Mohammadzadeh, M. R. Sahebi, F. Sepehrband and J. Choupan, "A Novel Real Time Algorithm for Remote Sensing Lossless Data Compression based on Enhanced DPCM", International Journal of Computer Applications, 27(1):47-53, August 2011. Published by Foundation of Computer Science, New York, USA.
  2. P. Ghamisi, "A Novel Method for Segmentation of Remote Sensing Images based on Hybrid GA-PSO", International Journal of Computer Applications, 29(2):7-14, September 2011. Published by Foundation of Computer Science, New York, USA.
  3. F. Sepehrband, P. Ghamisi, A. Mohammadzadeh, M. R. Sahebi, J. Choupan, "Efficient Adaptive Lossless Compression of Hyperspectral Data Using Enhanced DPCM", International Journal of Computer Applications 35(4):6-11, December 2011. Published by Foundation of Computer Science, New York, USA.
  4. P. Ghamisi, F. Sepehrband, L. Kumar, M. S. Couceiro, Fernando M. L. Martins, "A New Method for Compression of Remote Sensing Images Based on Enhanced Differential Pulse Code Modulation Transformation", Science Asia, 39 (5), 449-455.
  5. P. Ghamisi, M. S. Couceiro, J. A. Benediktsson and N. M. F. Ferreira, "An Efficient Method for Segmentation of Images Based on Fractional Calculus and Natural Selection," Expert Systems With Applications, vol. 39, no. 16, pp. 12407-12417, Nov. 2012 [code].
  6. P. Ghamisi, M. S. Couceiro, F. M. L. Martins and J. A. Benediktsson, "Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2382-2394, May 2014 [code].
  7. P. Ghamisi, M. S. Couceiro, M. Fauvel and J. A. Benediktsson, "Integration of Segmentation Techniques for Classification of Hyperspectral Images," IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 1, pp. 342-346, Jan. 2014.
  8. P. Ghamisi, J. A. Benediktsson and M. O. Ulfarsson, "Spectral-Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2565-2574, May 2014 [code].
  9. P. Ghamisi, J. A. Benediktsson and J. R. Sveinsson, "Automatic Spectral-Spatial Classi fication Framework Based on Attribute Profiles and Supervised Feature Extraction," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 9, pp. 5771-5782, Dec. 2014.
  10. P. Ghamisi, J. A. Benediktsson, G. Cavallaro and A. Plaza, "Automatic Framework for Spectral{Spatial Classi fication Based on Supervised Feature Extraction and Morphological Attribute Profiles," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2147 - 2160, Jun. 2014.
  11. P. Ghamisi and J. A. Benediktsson, "Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization," IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 2, pp. 309-313, Jul. 2015.
  12. P. Ghamisi, M. Dalla Mura and J. A. Benediktsson, "A Survey on Spectral-Spatial Classi fication Techniques Based on Attribute Pro files," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 5, pp. 2335-2353, May 2015 [Selected as Highly Cited Paper by Web of Science].
  13. P. Ghamisi, M. S. Couceiro and J. A. Benediktsson, "A Novel Feature Selection Approach Based on FODPSO and SVM," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 5, pp. 2935-2947, May 2015 [code].
  14. S. K. Nahavandi, P. Ghamisi, L. Kumar and M. S. Couceiro, "A Novel Adaptive Compression Technique for Dealing with Corrupt Bands and High Levels of Band Correlations in Hyperspectral Images based on Binary Hybrid GAPSO for Big Data Compression", International Journal of Computer Applications, vol. 109, no. 8, pp. 18-25, January 2015.
  15. P. Ghamisi, A. ALi, M. S. Couceiro and J. A. Benediktsson, "A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 6, pp. 2447 - 2456, 2015.
  16. P. Ghamisi, J. A. Benediktsson and S. Phinn, P. Ghamisi, J. A. Benediktsson, and S. Phinn, "Landcover classi cation using both hyperspectral and LiDAR data," International Journal of Image and Data Fusion, vol. 6, no. 3, pp. 189215, 2015.
  17. P. Ghamisi, R. Souza, J. A. Benediktsson, X. X. Zhu, L. Rittner, and R. Lotufo, "Extinction Profi les for the Classi fication of Remote Sensing Data", IEEE Transactions on Geoscience and Remote Sensing, vol.54, no.10, pp.5631 - 5645, 2016 [The most popular paper published by IEEE TGRS in July, August, and September 2016] [code] .
  18. Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, Deep Feature Extraction and Classi fication of Hyperspectral Images Based on Convolutional Neural Networks, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 10, pp. 6232-6251, Oct. 2016 [The most popular paper published by IEEE TGRS in October, November, and December 2016] [Selected as Highly Cited Paper by Web of Science] [Winner of the IEEE Geoscience and Remote Sensing Society 2020 Highest-Impact Paper Award].
  19. P. Ghamisi, Y. Chen, and X. X. Zhu, "A Self-Improving Convolution Neural Network for the Classi fication of Hyperspectral Data", IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 10, pp. 1537 - 1541, Oct. 2016 [The most popular paper published by IEEE GRSL in October and November 2016].
  20. P. Ghamisi, R. Souza, J. A. Benediktsson, L. Rittner, R. Lotufo, X. X. Zhu, "Hyperspectral Data Classi fication Using Extended Extinction Profi le", IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 11, pp. 1641-1645, Nov. 2016.
  21. Y. Chen, S. Ma, X. Chen, and P. Ghamisi, "Hyperspectral Data Clustering Based on Density Analysis Ensemble", Remote Sensing Letters, vol. 8, no. 2, pp. 194-203, 2017.
  22. P. Ghamisi, J. Plaza, Y. Chen, J. Li, and A. Plaza, "Advanced Spectral Classifi ers for Hyperspectral Images: A Review", IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 1, pp. 8-32, 2017.
  23. P. Ghamisi, G. Cavallaro, D. Wu, Jon Atli Benediktsson and A. Plaza, "Fusion of LiDAR and Hyperspectral Data for the Classifi cation of Urban Areas: A Case Study", International Journal of Image and Data Fusion, accepted.
  24. P. Ghamisi, B. Hofle, X. X. Zhu, "Hyperspectral and LiDAR Data Fusion Using Extinction Pro lfies and Deep Convolutional Neural Network", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 6, pp. 3011-3024, 2017.
  25. P. Ghamisi and B. Hofle, "LiDAR Data Classi fication Using Extinction Pro lfies and a Composite Kernel Support Vector Machine", IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 5, pp. 659-663, 2017.
  26. L. Mou, P. Ghamisi, X. X. Zhu, "Deep Recurrent Neural Networks for Hyperspectral Image Classifi cation", IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3639-3655, 2017 [The most popular paper published by IEEE TGRS since July 2017 till now].
  27. B. Rasti, P. Ghamisi, and R. Gloaguan, "Hyperspectral and LiDAR Fusion Using Extinction Pro files and Total Variation Component Analysis", IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3997-4007, 2017 [code].
  28. R. pullanagari, G. Kereszturi, I. Yule, P. Ghamisi, "Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classifi cation of urban land cover classes using support vector machines and arti ficial neural network", Journal of Applied Remote Sensing, vol. 11, no. 2, pp. 026009, 2017.
  29. Y. Chen, C. Li, P. Ghamisi, X. Jia, Y. Gu, "Deep Fusion of Remote Sensing Data for Accurate Classi fication," IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1253-1257, 2017.
  30. M. Zhang, P. Ghamisi, and W. Li, "Classi fication of hyperspectral and LiDAR data using extinction pro les with feature fusion", Remote Sensing Letters, vol. 8, no. 10, pp. 957-966, 2017.
  31. B. Rasti, P. Ghamisi, J. Plaza, and A. Plaza, "Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis", IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 11, pp. 6354-6365, Nov. 2017.
  32. Y. Chen, L. Zhu, P. Ghamisi, X. Jia, and L. Tang, "Hyperspectral Images Classi fication with Gabor Filtering and Convolutional Neural Network", IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 12, pp. 2355-2359, Dec. 2017 [code].
  33. P. Ghamisi, N. Yokoya, J. Li, W. Liao, S. Liu, J. Plaza, B. Rasti and A. Plaza. Advances in Hyperspectral Image and Signal Processing. IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 4, pp. 37-78, Dec. 2017.
  34. B. Rasti, M. O. Ulfarsson, and P. Ghamisi, "Automatic Hyperspectral Image Restoration Using Sparse and Low-Rank Modeling", IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 12, pp. 2335-2339, Dec. 2017  [code].
  35. J. Xia, P. Ghamisi, N. Yokoya, and A. Iwasaki, "Random Forest Ensembles and Extended Multi-Extinction Pro files for Hyperspectral Image Classi fication", IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 1, pp. 202-216, Jan. 2018.
  36. L. Mou, P. Ghamisi, X. X. Zhu, "Unsupervised Spectral-Spatial Feature Learning via Deep Residual Conv-Deconv Network for Hyperspectral Image Classi fication", IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 1, pp. 391-406, Jan. 2018.
  37. L. Fang, N. He S. Li, P. Ghamisi and J. A. Benediktsson "Extinction Profiles Fusion for Hyperspectral Images Classification", IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 3, pp. 1803-1815, March 2018. [code].
  38. N. Yokoya, P. Ghamisi, J. Xia, S. Sukhanov, R. Heremans, I. Tankoyeu, B. Bechtel, B. Le Saux, G. Moser, and D. Tuia, "Open data for global multimodal land use classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 5, pp. 1363-1377, May 2018.
  39. L. Zhu,Y. Chen, P. Ghamisi, and J. A. Benediktsson, "Generative Adversarial Networks for Hyperspectral Image Classification", IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 9, pp. 5046-5063, Sept. 2018 [code].
  40. P. Ghamisi and N. Yokoya, "IMG2DSM: Height Simulation from Single Imagery Using Conditional Generative Adversarial Nets", IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 5, pp. 794-798, May 2018.
  41. A. Wang, X. He, P. Ghamisi, and Y. Chen, "LiDAR Data Classification Using Morphological Profiles and Convolutional Neural Networks," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 5, pp. 774-778, May 2018.
  42. B. Rasti, P. Scheunders, P. Ghamisi, G. Licciardi, and J. Chanussot, "Noise Reduction in Hyperspectral Imagery: Overview and Application", Remote Sensing, vol. 10, no. 3, 2018 [code].
  43. J. Zhu, L. Fang, and P. Ghamisi, "Deformable Convolutional Neural Networks for Hyperspectral Images Classification", IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 8, pp. 1254-1258, Aug. 2018 [code].
  44. P. Ghamisi, E. Maggiori, S. Li, R. Souza, Y. Tarabalka, G. Moser, A. D. Giorgi, L. Fang, Y. Chen, M. Chi, S. B. Serpico, and J. A. Benediktsson, "New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning", IEEE Geoscience and Remote Sensing Magazine, vol. 6, no. 3, pp. 10-43, Sep. 2018.  [PDF]
  45. H. Ghanbari, S. Homayouni, A. Safari, and P. Ghamisi, "Gaussian Mixture Model and Markov Random Fields for Hyperspectral Image Classification", European Journal of Remote Sensing, vol. 51, no. 1, pp. 889-900, Sep. 2018.
  46. L. Fang, G. Liu, S. Li, P. Ghamisi, and J. A. Benediktsson, Hyperspectral Image Classification with Squeeze Multi-Bias Network, IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 3, pp. 1291-1301, 2018.
  47. X. He, A. Wang, P. Ghamisi, Y. Chen, LiDAR data Classification Using Spatial Transformation and Convolutional Neural Networks, IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 5, pp. 774-778, May 2018.
  48. J. Hu, P. Ghamisi, X. X. Zhu, Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification, ISPRS International Journal of Geo-Information, vol. 7, no. 9, 2018. 
  49. H. Ghanbari, S. Homayouni, P. Ghamisi, and A. Safari, Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images based on a Gaussian Mixture Model and Error Ellipse, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 11, pp. 4526-4533, Nov. 2018.
  50. C. Qiu, M. Schmitt, L. Mou, P. Ghamisi, and X. X. Zhu, "Feature importance analysis for Local Climate Zone classification using a residual convolutional neural network with multi-source datasets," Remote Sensing, vol. 10, no. 10, p. 1572, 2018.
  51. H. Li, P. Ghamisi, U. Soergel, and X. X. Zhu, Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks, Remote Sensing, vol. 10, no. 10, p. 1649, 2018.
  52. X. Wu, D. Hong, P. Ghamisi, W. Li, and Ran Tao, MsRi-CCF: Multi-Scale and Rotation-Insensitive Convolutional Channel Features for Geospatial Object Detection, Remote Sens. vol. 10, no. 12, 2018.
  53. P. Ghamisi, B. Rasti, N. Yokoya, Q. Wang, B. Hofle, L. Bruzzone, F. Bovolo, M. Chi, K. Anders, R. Gloaguen, P. M. Atkinson, J. A. Benediktsson, "Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art," in IEEE Geoscience and Remote Sensing Magazine, vol. 7, no. 1, pp. 6-39, March 2019 [PDF].
  54. P. Ghamisi, B. Rasti, and J. A. Benediktsson, Multisensor Composite Kernels Based on Extreme Learning Machines, IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 2, pp. 196-200, Feb. 2019. 
  55. B. Rasti, P. Ghamisi, and M. O. Ulfarsson, Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis, Remote Sensing, vol. 11, no. 2, 2019 [code].
  56. K. Zhu, Y. Chen, P. Ghamisi, X. Jia, and J. A. Benediktsson, Deep Convolutional Capsule Network for Hyperspectral Image Spectral and Spectral-Spatial Classification, Remote Sensing, vol. 11, no. 3, 2019 [code].
  57. X. He, A. Wang, P. Ghamisi, G. Li and Y. Chen, "LiDAR Data Classification Using Spatial Transformation and CNN," IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 1, pp. 125-129, Jan. 2019.
  58. R. Hang, Q. Liu, D. Hong, and P. Ghamisi, "Cascaded Recurrent Neural Networks for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 8, pp. 5384-5394, Aug. 2019 [code].
  59. G. Zhao, G. Liu, L. Fang, B. Tu, and P. Ghamisi, Multiple convolutional layers fusion framework for hyperspectral image classification, Neurocomputing, vol. 339, pp. 149-160, Apr. 2019.
  60. Y. Chen, K. Zhu, L. Zhu, X. He, P. Ghamisi, and J. A. Benediktsson, "Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 7048-7066, Sept. 2019. 
  61. S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi, and J. A. Benediktsson, "Deep Learning for Hyperspectral Image Classification: An Overview," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6690-6709, Sept. 2019.
  62. G. Zhang, P. Ghamisi, and X. X. Zhu, "Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones", in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 10, pp. 7623-7642, Oct. 2019.
  63. Y. Chen, Y. Wang, Y. Gu, X. He, P. Ghamisi, and X. Jia, ``Deep Learning Ensemble for Hyperspectral Image Classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 6, pp. 1882-1897, June 2019.
  64. P. Duan, X. Kang, S. Li, and P. Ghamisi, ``Noise-Robust Hyperspectral Image Classification via Multi-Scale Total Variation," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 6, pp. 1948-1962, June 2019.
  65. S. Lorenz, P. Seidel, P. Ghamisi, R. Zimmermann, L. Tusa, M. Khodadadzadeh, I. Cecilia Contreras, and R. Gloaguen, ``Multi-Sensor Spectral Imaging of Geological Samples: A Data Fusion Approach Using Spatio-Spectral Feature Extraction, Sensors, vol. 19, no. 12, 2019.
  66. B. Rasti, P. Ghamisi, and J. A. Benediktsson, ``Hyperspectral Mixed Gaussian and Sparse Noise Reduction", IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 3, pp. 474-478, March 2020.
  67. I. Cecilia Contreras Acosta, M. Khodadadzadeh, L. Tusa, P. Ghamisi, and R. Gloaguen, "A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. vol. 12, no. 12, pp. 4829-4842, Dec. 2019.
  68. P. Duan, X. Kang, S. Li, P. Ghamisi, and J. A. Benediktsson, "Fusion of Multiple Edge-Preserving Operations for Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 12, pp. 10336-10349, Dec. 2019. 
  69. X. He, Y. Chen and P. Ghamisi, "Heterogeneous Transfer Learning for Hyperspectral Image Classification Based on Convolutional Neural Network," in IEEE Transactions on Geoscience and Remote Sensing. vol. 58, no. 5, pp. 3246-3263, May 2020.
  70. Y. Lin, S. Li, L. Fang, and P. Ghamisi, "Multispectral Change Detection With Bilinear Convolutional Neural Networks," IEEE Geoscience and Remote Sensing Letters.doi: 10.1109/LGRS.2019.2953754.
  71. M. Kirsch, S. Lorenz, R. Zimmermann, L. Andreani, L. Tusa, S. Pospiech, R. Jackisch, M. Khodadadzadeh, P. Ghamisi, G. Unger, P. Hödl, R. Gloaguen, M. Middleton, R. Sutinen, A. Ojala, J. Mattila, N. Nordbäck, J. Palmu, M. Tiljander, and T. Ruskeeniemi, "Hyperspectral outcrop models for palaeoseismic studies", The Photogrammetric Record, vol. 34, no. 168, pp. 385-407, Dec. 2019.
  72. B. Choubin, A. Mosavi, E. H. Alamdarloo, F. S. Hosseini, S. Shamshirband, K. Dashtekian, and P. Ghamisi, "Earth fissure hazard prediction using machine learning models", Environmental Research, vol. 179, Part A, 2019.
  73. B. Choubin, M. Abdolshahnejad, E. Moradi, X. Querol, A. Mosavi, S. Shamshirband, and P. Ghamisi, "Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain", Science of The Total Environment, vol. 701, 2020.
  74. B. Rasti, D. Hong, R. Hang, P. Ghamisi, X. Kang, J. Chanussot, and J. A. Benediktsson, "Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)," in IEEE Geoscience and Remote Sensing Magazine, vol. 8, no. 4, pp. 60-88, Dec. 2020 [code].
  75. J. Xie, N. He, L. Fang, and P. Ghamisi, "Multiscale Densely-Connected Fusion Networks for Hyperspectral Images Classification," in IEEE Transactions on Circuits and Systems for Video Technology. doi: 10.1109/TCSVT.2020.2975566.
  76. R. Huang, Y. Xu, D. Hong, W. Yao, P. Ghamisi, and U. Stilla, "Deep Point Embedding for Urban Classification Using ALS Point Cloud: A New Perspective from Local to Global", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 163, pp. 62-81, May 2020.
  77. D. Hong, X. Wu, P. Ghamisi, J. Chanussot, N. Yokoya and X. X. Zhu, "Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 6, pp. 3791-3808, June 2020.
  78. M. Dehghani, S. Salehi, A. Mosavi, N. Nabipour, S. Shamshirband, P. Ghamisi, "Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices." ISPRS Int. J. Geo-Inf., vol. 9, no. 73, 2020.
  79. R. Hang, Z. Li, P. Ghamisi, D. Hong, G. Xia, and Q. Liu, "Classification of Hyperspectral and LiDAR Data Using Coupled CNNs", IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 7, pp. 4939-4950, 2020 [code].
  80. N. Yokoya, P. Ghamisi, R. Haensch, and M. Schmitt, "2020 IEEE GRSS Data Fusion Contest: Global Land Cover Mapping With Weak Supervision [Technical Committees]," in IEEE Geoscience and Remote Sensing Magazine, vol. 8, no. 1, pp. 154-157, March 2020.
  81. A. Mosavi, P. Ghamisi, Y. Faghan, and P. Duan, ”Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics”, Mathematics, vol. 8, no. 10, 2020.
  82. S. Nosratabadi, A. Mosavi, P. Duan, and P. Ghamisi, Data Science in Economics, arXiv, 2020.
  83. S. F. Ardabili, A. Mosavi, P. Ghamisi, F. Ferdinand, A. R. Varkonyi-Koczy, U. Reuter, T. Rabczuk, and P. M. Atkinson, "COVID-19 Outbreak Prediction with Machine Learning", Algorithms, vol. 13, no. 10, 2020.
  84. M. M. Sheikholeslami, S. Nadi, A. A. Naeini, and P. Ghamisi, ”An Efficient Deep Unsupervised Superresolution Model for Remote Sensing Images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.13, pp. 1937-1945, 2020.
  85. V. Sudharshan, P. Seidel, P. Ghamisi, S. Lorenz, M. Fuchs, J. S. Fareedh, P. Neubert, S. Schubert, and R. Gloaguen, ”Object detection routine for material streams combining RGB and hyperspectral reflectance data based on Guided Object Localization,” IEEE Sensors Journal, vol. 20, no. 19, pp. 11490-11498, 2020.
  86. P. Duan, X. Kang, S. Li and P. Ghamisi, "Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 4, pp. 2444-2456, April 2020.
  87. M. E. Paoletti, J. M. Haut, P. Ghamisi, N. Yokoya, J. Plaza, and A. Plaza, "U-IMG2DSM: Unpaired Simulation of Digital Surface Models With Generative Adversarial Networks," in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2020.2997295 [code].
  88. J. Kang, R. Fernandez-Beltran, Z. Ye, X. Tong, P. Ghamisi, and A. Plaza, ”Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 12, pp. 8905-8918, Dec. 2020 [code].
  89. H. Li, P. Ghamisi, B. Rasti, Z. Wu, A. Shapiro, M. Schultz, and A. Zipf, ”A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks”, Remote Sensing, vol. 12, 2020.
  90. B. Rasti, B. Koirala, P. Scheunders, and P. Ghamisi, "How Hyperspectral Image Unmixing and Denoising Can Boost Each Other", Remote Sensing, vol. 12, 2020.
  91. R Hang, Z Li, Q Liu, P. Ghamisi, and S. S. Bhattacharyya, ”Hyperspectral Image Classification with Attention Aided CNNs”, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3007921.
  92. R. Hang, F. Zhou, Q. Liu, and P. Ghamisi, "Classification of Hyperspectral Images via Multitask Generative Adversarial Networks,” IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3003341.
  93. B. Rasti and P. Ghamisi, Remote Sensing Image Classification Using Subspace Sensor Fusion, Information Fusion, vol. 64, pp. 121-130, 2020 [code].
  94. S. Salcedo-Sanz, P. Ghamisi, M. Piles, M. Werner, L. Cuadra, A. Moreno-Martnez, E. Izquierdo-Verdiguier, J. Munoz-Mar, A. Mosavi, and G. Camps-Valls, ”Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources”, Information Fusion, vol. 63, pp 256-272, 2020.
  95. P. Duan, J. Lai, J. Kang, X. Kang, P. Ghamisi, and S. Li, ”Texture-aware total variation-based removal of sun glint in hyperspectral images”, ISPRS Journal of Photogrammetry and Remote Sensing, volume 166, 2020.
  96. J. Kang, R. Fernández-Beltrán, Z. Ye, X. Tong, P. Ghamisi, and A. Plaza, "High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery", Remote Sensing, vol. 12, no. 16, 2020 [code].
  97. B. Rasti, P. Ghamisi, P. Seidel, S. Lorenz, and R. Gloaguen, Multiple OpticalSensor Fusion for Mineral Mapping of Core Samples, Sensors, vol. 20, no. 13, p.3766, Jul. 2020.
  98. K. Rafiezadeh Shahi, M. Khodadadzadeh, L. Tusa, P. Ghamisi, R. Tolosana-Delgado, and R. Gloaguen, Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis, Remote Sensing, vol. 12, no. 15, p. 2421, Jul. 2020 [code].
  99. M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni, "Support Vector Machine vs. Random Forest for Remote Sensing Image Classification: A Meta-analysis and systematic review," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 6308-6325, 2020.
  100. P. Duan, P. Ghamisi, X. Kang, B. Rasti, S. Li, and R. Gloaguen, ”Fusion of Dual Spatial Information for Hyperspectral Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3031928 [code].
  101. P. Duan, J. Lai, P. Ghamisi, X. Kang, R. Jackisch, J. Kang, R. Gloaguen, ”Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping” Remote Sensing, vol. 12, 2020 [code].
  102. K. Rafiezadeh Shahi, P. Ghamisi, B. Rasti, R. Jackisch, P. Scheunders, and R. Gloaguen, ”Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm”, Remote Sensing, vol. 12, 2020. 
  103. P. Duan, X. Kang, P. Ghamisi, Y. Liu, ”Multilevel Structure Extraction-Based Multi-Sensor Data Fusion”, Remote Sensing, vol. 12, 2020.
  104. S. Lorenz, P. Ghamisi, M. Kirsch, R. Jackisch, B. Rasti, and R. Gloaguen, ”Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods”, Remote Sensing of Environment, vol. 252, 2021
  105. N. Yokoya, P. Ghamisi, R. Hansch and M. Schmitt, ”Report on the 2020 IEEE GRSS Data Fusion Contest-Global Land Cover Mapping With Weak Supervision [Technical Committees],” IEEE Geoscience and Remote Sensing Magazine, vol. 8, no. 4, pp. 134-137, Dec. 2020.
  106. J. Yue, L. Fang, H. Rahmani and P. Ghamisi, "Self-Supervised Learning With Adaptive Distillation for Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, 2021, doi: 10.1109/TGRS.2021.3057768 [code].
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Conference Papers
 
 
  1. P. Ghamisi, F. Sepehrband, A. Mohammadzadeh, M. Mortazavi, J. Choupan, "Fast and Efficient Algorithm for Real Time Lossless Compression of LiDAR rasterized data Based on Improving Energy Compaction", The 6th IEEE GRSS and ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, JURSE'11, Munich, Germany, April 2011.
  2. F. Sepehrband, P. Ghamisi, M. Mortazavi and J. Choupan, "Simple and efficient remote sensing image transformation for lossless compression", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82854A (September 30, 2011); doi:10.1117/12.913262.
  3. F. Sepehrband, P. Ghamisi, M. Mortazavi, J. Choupan, "Simple and Efficient Remote Sensing Image Transformation for Lossless Compression", International Conference on Signal and Information Processing (ICSIP'10), Changsha, China, December, 2010.
  4. P. Ghamisi, F. Sepehrband, J. Choupan, M. Mortazavi, "Binary Hybrid GAPSO based algorithm for compression of hyperspectral data," Signal Processing and Communication Systems (ICSPCS), vol., no., pp.1-8, 12-14 Dec. 2011; doi: 10.1109/ICSPCS.2011.6140839.
  5. P. Ghamisi and L. Kumar, "A novel adaptive compression method for hyperspectral images by using EDT and particle swarm optimization", Proc. SPIE 8299, Digital Photography VIII, 82990M (January 24, 2012); doi:10.1117/12.904727.
  6. P. Ghamisi, M. S. Couceiro, N. M. F. Ferreira, L. Kumar, "Use of Darwinian Particle Swarm Optimization technique for the segmentation of Remote Sensing images", IGARSS 2012, vol., no., pp.4295-4298, 22-27 July 2012, doi: 10.1109/IGARSS.2012.6351718.
  7. P. Ghamisi, M. S. Couceiro and J. A. Benediktsson, "Extending the Fractional Order Darwinian Particle Swarm Optimization to Segmentation of Hyperspectral Images," in Proc. SPIE, Image and Signal Processing for Remote Sensing XVIII, 2012, pp. 85370F-85370F-11.
  8. P. Ghamisi, M. S. Couceiro, M. Fauvel, J. A. Benediktsson, "Spectral-Spatial Classi fication Based on Integrated Segmentation," in Proc. IEEE IGARSS, 2012, pp. 1458-1461, 2013.
  9. P. Ghamisi, Jon Atli Benediktsson, Magnus O. Ulfarsson, "The Spectral Spatial Classi fication of Hyperspectral Images Based on Hidden Markov Random Field and its Expectation-Maximization," in Proc. IEEE IGARSS, 2013, pp. 1107-1110, [The winner of the IEEE Mikio Takagi student prize 2013 for winning the Student Paper Competition at the conference between almost 70 people].
  10. P. Ghamisi, M. S. Couceiro, and J. A. Benediktsson, "Classi cation of Hyperspectral Images with Binary Fractional Order Darwinian PSO and Random Forests," in Proc. SPIE, Image and Signal Processing for Remote Sensing XIX, 2013, pp. 88920S88920S-8.
  11. P. Ghamisi, M. S. Couceiro and J. A. Benediktsson, "FODSPO Based Feature Selection for Hyperspectral Remote Sensing Data," WHISPERS 2014, Lausane, Switzerland.
  12. P. Ghamisi, J. A. Benediktsson, S. Phinn, "Fusion of Hyperspectral and LiDAR Data in Classifi cation of Urban Areas," in Proc. IEEE IGARSS, 2014, pp. 181-184, [Invited paper].
  13. P. Ghamisi and J. A. Benediktsson, "Feature Selection of Hyperspectral Data by Considering the Integration of Genetic Algorithms and Particle Swarm Optimization," in Proc. SPIE, Image and Signal Processing for Remote Sensing XX, 2014, pp. 92440J-92440J-6.
  14. P. Ghamisi, D. Wu, G. Cavallaro, J. A. Benediktsson, S. Phinn and N. Falco, "An advanced classi er for the joint use of LiDAR and hyperspectral data: Case study in Queensland, Australia," 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, 2015, pp. 2354-2357.
  15. Y. Chen, C. Li, P. Ghamisi, C. Shi, "Convolutional neural network fusion of hyperspectral and LiDAR data for thematic classi cation," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.
  16. P. Ghamisi, R. Souza, L. Rittner, J. A. Benediktsson, R. Lotufo, and X. X. Zhu, "Extinction profi les: A novel approach for the analysis of remote sensing," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.
  17. P. Ghamisi, R. Souza, J. A. Benediktsson, X. X. Zhu, L. Rittner and R. Lotufo, "Extended extinction pro file for the classifi cation of hyperspectral images", WHISPERS 2016, Los Angles, California.
  18. N. Yokoya and P. Ghamisi, "Land-Cover monitoring using time-series hyperspectral data via fractional-order Darwinian particle swarm optimization Segmentation", WHISPERS 2016, Los Angles, California.
  19. J. Hu, P. Ghamisi, A. Schmitt, and X. X. Zhu, "Object based fusion of polarimetric SAR and hyperspectral imaging for land use classi fication", WHISPERS 2016, Los Angles, USA.
  20. N. He, L. Fang, S. Li, P. Ghamisi, J. A. Benediktsson, "Hyperspectral Images Classi fication by Fusing Extinction Pro files Feature", IGARSS 2017, 2017.
  21. P. Ghamisi, B. Rasti, and X. X. Zhu, "Feature Fusion of Hyperspectral and LiDAR Data Using Extinction Profi les and Total Variation", IGARSS 2017, 2017.
  22. J. Hu, Y. Wang, P. Ghamisi, X. X. Zhu, "Evaluation of PolSAR Similarity Measures with Spectral Clustering", IGARSS 2017, 2017.
  23. L. Mou, P. Ghamisi, and X. X. Zhu, "Fully Conv-Deconv Network for Unsupervised Spectral-Spatial Feature Extraction of Hyperspectral Imagery via Residual Learning", IGARSS 2017, accepted, [Invited paper].
  24. P. Du, J. Xia, P. Ghamisi, A. Iwasaki, J. A. Benediktsson, "Multiple Composite Kernel Learning for Hyperspectral Image Classi fication", IGARSS 2017, 2017.
  25. N. Yokoya and P. Ghamisi, "Multimodal, Multitemporal, and Multisource Global Data Fusion for Local Climate Zones Classi fication Based on Ensemble Learning", IGARSS 2017, 2017.
  26. C. P. Qiu, M. Schmitt, P. Ghamisi, and X. X. Zhu, "Effect of the Training Set Configuration on Sentinel-2-Based Urban Local Climate Zone Classification, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 931-936, https://doi.org/10.5194/isprs-archives-XLII-2-931-2018, 2018. 
  27. R. Gloaguen et al., "Multi-Source and multi-Scale Imaging-Data Integration to boost Mineral Mapping," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 5587-5589.
  28. X. Wu, D. Hong, P. Ghamisi, W. Li and R. Tao, "LW-ODF: A Light-Weight Object Detection Framework for Optical Remote Sensing Imagery," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 1462-1465.
  29. B. Rasti, P. Ghamisi and R. Gloaguen, "Multisensor Feature Fusion Using Low-Rank Modeling and Component Analysis," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 4811-4814.
  30. P. Ghamisi, B. Rasti and R. Gloaguen, "A Novel Composite Kernel Approach for Multisensor Remote Sensing Data Fusion," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 2507-2510.
  31. C. Contreras, M. Khodadadzadeh, P. Ghamisi and R. Gloaguen, "Mineral Mapping of Drill Core Hyperspectral Data with Extreme Learning Machines," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 2686-2689.
  32. K. R. Shahi, et al. ”A NEW SPECTRAL-SPATIAL SUBSPACE CLUSTERING ALGORITHM FOR HYPERSPECTRAL IMAGE ANALYSIS.” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.-3-2020, 2020, pp. 185-191. 
 
 
 
 
 
 
 
Data Sets
  1. For those of you who are interested in the fusion of LiDAR and hyperspectral data or the classification of hyperspectral images, we made our data set public. The data set was captured over Samford Ecological Research Facility (SERF), Queensland, Australia. The data set is composed of hyperspectral and LiDAR data as well as their corresponding training and test samples. You may download the data here: [download]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Books
 
 
[B2] J. A. Benediktsson and P. Ghamisi, Spectral-Spatial Classification of Hyperspectral Remote Sensing Images, Artech House Publishers, INC, Boston, USA, 2015. [Link]
[B1] M. S. Couceiro and P. Ghamisi, Fractional Order Darwinian Particle Swarm Optimization: Applications and Evaluation of an Evolutionary Algorithm. Springer Verlag, London, 2015. [Link]
 
 
CONTACT ME

Pedram Ghamisi

Research Scientist

 

Phone:

+49 179 693 1140

 

Email:

p.ghamisi@gmail.com 

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