top of page
This site was designed with the
.com
website builder. Create your website today.Start Now

Pedram Ghamisi

 
Machine Learning Group Leader at HZDR-HIF [Link]
​
Co-founder at VasoGnosis [Link]
 
Research Professor and the leader of AI4RS at the Institute of Advanced Research in Artificial Intelligence (IARAI) [Link]
​
For more info, please check my personal website [Link]

​

Email: p.ghamisi@gmail.com 
  • LinkedIn Social Icon
  • google-scholar-e1430680940234
  • AS_267458164789257_1440778403888_l
  • dblp-logo
  • Instagram Social Icon

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]

EXPERIENCE
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 Classification 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 Classification 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 Classification Techniques Based on Attribute Profiles," 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 classication 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 Profiles for the Classification 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 Classification 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 Classification 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 Classification Using Extended Extinction Profile", 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 Classifiers 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 Classification 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 Prolfies 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 Classification Using Extinction Prolfies 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 Classification", 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 Profiles 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 classification of urban land cover classes using support vector machines and artificial 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 Classification," IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1253-1257, 2017.
  30. M. Zhang, P. Ghamisi, and W. Li, "Classification of hyperspectral and LiDAR data using extinction proles 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 Classification 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 Profiles for Hyperspectral Image Classification", 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 Classification", 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].
  107. C. Robinson, K. Malkin, N. Jojic, H. Chen, R. Qin, C. Xiao, M. Schmitt, P. Ghamisi, R. Hnsch, and N. Yokoya, "Global Land-Cover Mapping With Weak Supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 3185-3199, 2021, doi: 10.1109/JSTARS.2021.3063849.
  108. N. Yokoya, P. Ghamisi, R. Hansch, C. Prieur, H. Malha, J. Chanussot, C. Robinson, K. Malkin, and N. Jojic, ”2021 Data Fusion Contest: Geospatial Artificial Intelligence for Social Good [Technical Committees],” IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 1, pp. 287-C3, March 2021.
  109. X. He, Y. Chen, and P. Ghamisi, ”Dual Graph Convolutional Network for Hyperspectral Image Classification With Limited Training Samples,” in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3061088.
  110. B. Rasti, B. Koirala, P. Scheunders, and P. Ghamisi, "UnDIP: Hyperspectral Unmixing Using Deep Image Prior,” IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3067802.
  111. B. Rasti, B. Koirala, P. Scheunders and P. Ghamisi, "UnDIP: Hyperspectral Unmixing Using Deep Image Prior," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3067802 [code].
  112. X. Liu, D. Hong, J. Chanussot, B. Zhao and P. Ghamisi, "Modality Translation in Remote Sensing Time Series," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3079294 [Data].
  113. S. Mohammed, K. Tai-hoon, P. Ghamisi, and R. -S. Chang, ”A Special Issue on Recent Progress in Developing Artificial Intelligence and Machine LearningMethodologies [From the Guest Editors],” IEEE Geoscience and Remote SensingMagazine, vol. 9, no. 2, pp. 7-128, June 2021, doi: 10.1109/MGRS.2021.3078373.
  114. O. Ghorbanzadeh, A. Crivellari,P. Ghamisiet al., ”A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2data (case study areas from Taiwan, China, and Japan)”, Scientific Report, 11, 14629, 2021.
  115. B. Rasti, Y. Chang, E. Dalsasso, L. Denis, and P. Ghamisi, "Image Restoration for Remote Sensing: Overview and Toolbox", https://arxiv.org/abs/2107.00557, 2021 [code].
  116. J. Yue, D. Zhu, L. Fang, P. Ghamisi, and Y. Wang, ”Adaptive Spatial Pyramid Constraint for Hyperspectral Image Classification With Limited Training Samples,” IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3095056.
  117. Y. Han, M. Yin, P. Duan, and P. Ghamisi, ”Edge-Preserving Filtering-Based Dehazing for Remote Sensing Images,” IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2021.3103381.
  118. P. Ghamisi et al., ”The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 8971-8988, 2021, doi: 10.1109/JSTARS.2021.3108049.
  119. M. Schmitt, C. Persello, G. Vivone, D. Lunga, W. Liao, N. Yokoya, P. Ghamisi, and R. Hnsch, ”The New Working Groups of the GRSS Technical Committee on Image Analysis and Data Fusion [Technical Committees],” IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 3, pp. 165-166, Sept. 2021.
  120. Q. Li, Y. Chen, and P. Ghamisi, ”Complementary Learning-Based Scene Classification of Remote Sensing Images With Noisy Labels,” IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2021.3112960.
  121. H. Xie, Y. Chen, and P. Ghamisi, Remote Sensing Image Scene Classification via Label Augmentation and Intra-Class Constraint, Remote Sensing, vol. 13, no. 13, p. 2566, Jun. 2021.
  122. Y. Cai, Z. Zhang, Z. Cai, X. Liu, Y. Ding, and P. Ghamisi, ”Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering”, 2021, ArXiv, abs/2111.07942.
  123. Y. Cai, Z. Zhang, Y. Liu, P. Ghamisi, K. Li, X. Liu, and Z. Cai, ”LargeScale Hyperspectral Image Clustering Using Contrastive Learning”, 2021, ArXiv, abs/2111.07945.
  124. H. Shahabi, M. Rahimzad, S. Tavakkoli Piralilou, O. Ghorbanzadeh, S. Homayouni, T. Blaschke, S. Lim, and P. Ghamisi, Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery, Remote Sensing, vol. 13, no. 22, p. 4698, Nov. 2021.
  125. Y. Ma et al., "The Outcome of the 2021 IEEE GRSS Data Fusion Contest - Track DSE: Detection of Settlements Without Electricity," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 12375-12385, 2021.
  126. N. Yokoya et al., "Report on the 2021 IEEE GRSS Data Fusion Contest—Geospatial Artificial Intelligence for Social Good [Technical Committees]," in IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 4, pp. 274-277, Dec. 2021.
  127. B. Rasti, P. Ghamisi and R. Gloaguen, "OptFus: Optical Sensor Fusion for the Classification of Multisource Data: Application to Mineralogical Mapping," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022 [code].
  128. Z. Zhang, Y. Cai, W. Gong, P. Ghamisi, X. Liu and R. Gloaguen, ”Hypergraph Convolutional Subspace Clustering with Multi-hop Aggregation for Hyperspectral Image,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 676-686, 2022 [code].
  129. C. Persello, J. D. Wegner, R. Hansch, D. Tuia, P. Ghamisi, M. Koeva, G. CampsValls, "Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current Approaches, Open Challenges, and Future Opportunities," in IEEE Geoscience and Remote Sensing Magazine, doi: 10.1109/MGRS.2021.3136100.
  130. K. R. Shahi, P. Ghamisi, B. Rasti, P. Scheunders and R. Gloaguen, "Unsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep Clustering Algorithm," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 284-296, 2022 [code].
  131. S. Tavakkoli Piralilou et al., “A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions,” Remote Sensing, vol. 14, no. 3, p. 672, Jan. 2022.
  132. M. Lu, L. Fang, M. Li, B. Zhang, Y. Zhang and P. Ghamisi, "NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2022.3140323 [code].
  133. W. Song, Z. Gao, R. Dian, P. Ghamisi, Y. Zhang and J. A. Benediktsson, "Asymmetric Hash Code Learning for Remote Sensing Image Retrieval," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2022.3143571 [code].
  134. O. Ghorbanzadeh, H. Shahabi, A. Crivellari, et al. Landslide detection using deep learning and object-based image analysis. Landslides (2022). https://doi.org/10.1007/s10346-021-01843-x.
  135. Z. Li et al., "The Outcome of the 2021 IEEE GRSS Data Fusion Contest—Track MSD: Multitemporal Semantic Change Detection," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 1643-1655, 2022.
  136. O. Ghorbanzadeh, K. Gholamnia, and P. Ghamisi, "The application of ResU-net and OBIA for landslide detection from multi-temporal sentinel-2 images", Big Earth Data, 2022, DOI: 10.1080/20964471.2022.2031544.
  137. Y. Xu and P. Ghamisi, "Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark", 2202.07054, arXiv, 2022 [code].
  138. Y. Xu and P. Ghamisi, Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations, https://arxiv.org/abs/2202.03740, 2022.
  139. Shizhen Chang and Pedram Ghamisi, "Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection", arXiv, 2022, https://arxiv.org/abs/2203.10030. 
  140. Jun Yue, Leyuan Fang, Pedram Ghamisi, Weiying Xie, Jun Li, Jocelyn Chanussot, Antonio J Plaza, "Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives," in IEEE Geoscience and Remote Sensing Magazine, doi: 10.1109/MGRS.2022.3161377.
  141. B. Aslam et al., “Evaluation of Different Landslide Susceptibility Models for a Local Scale in the Chitral District, Northern Pakistan,” Sensors, vol. 22, no. 9, p. 3107, Apr. 2022, doi: 10.3390/s22093107.
  142. S. Das, S. Pratiher, C. Kyal and P. Ghamisi, "Sparsity Regularized Deep Subspace Clustering for Multi-criterion-based Hyperspectral Band Selection," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2022.3172112.
  143. H. Li, J. Zech, D. Hong, P. Ghamisi, M. Schultz, A. Zipf, "Leveraging OpenStreetMap and Multimodal Remote Sensing Data with Joint Deep Learning for Wastewater Treatment Plants Detection", International Journal of Applied Earth Observation and Geoinformation,
    Volume 110, 2022.
​
​
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 Classification 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 Classification 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, "Classication 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 Classification 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 classier 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 classication," 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 profiles: 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 profile for the classification 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 classification", WHISPERS 2016, Los Angles, USA.
  20. N. He, L. Fang, S. Li, P. Ghamisi, J. A. Benediktsson, "Hyperspectral Images Classification by Fusing Extinction Profiles Feature", IGARSS 2017, 2017.
  21. P. Ghamisi, B. Rasti, and X. X. Zhu, "Feature Fusion of Hyperspectral and LiDAR Data Using Extinction Profiles 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 Classification", IGARSS 2017, 2017.
  25. N. Yokoya and P. Ghamisi, "Multimodal, Multitemporal, and Multisource Global Data Fusion for Local Climate Zones Classification 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. 
  33. P. Duan, P. Ghamisi, R. Jackisch, X. Kang, R. Gloaguen and S. Li, ”Intrinsic Image Decomposition-Based Resolution Enhancement for Mineral Mapping,” IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 4112-4115.
  34. B. Rasti, P. Ghamisi and R. Gloaguen, ”Fusion of Multispectral LiDAR and Hyperspectral Imagery,” IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 2659-2662.
  35. R. Gloaguen, M. Kirsch, S. Lorenz, R. Booysen, R. Zimmermann, P. Ghamisi, and B. Rasti, ”Towards 4D Virtual Outcrops with Hyperspectral Imaging,” IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 4035-4036.
  36. P. Duan, J. Kang, X. Kang, P. Ghamisi, and S. Li, ”Sun Glint Removal of Hyperspectral Images via Texture-Aware Total Variation,” IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 7005-7008.
  37. P. Ghamisi, H. Li, R. Jackisch, B. Rasti, and R. Gloaguen, ”Remote Sensing and Deep Learning for Sustainable Mining,” IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 3739- 3742.
  38. 38. K. R. Shahi, P. Ghamisi, R. Jackisch, B. Rasti, P. Scheunders and R. Gloaguen, ”A Multi-Sensor Subspace-Based Clustering Algorithm Using RGB and Hyperspectral Data,” 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2021, pp. 1-5, doi: 10.1109/WHISPERS52202.2021.9483953.
  39. A. Gruca, P. Herruzo, P. Rpodas, A. Kucik, C. Briese, M. K. Kopp, S. Hochreiter, P. Ghamisi, and D. P. Kreil (2021) CDCEO21 First Workshop on Complex Data Challenges in Earth Observation. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 21), November 15, 2021, Virtual Event, QLD, Australia. ACM, New York, NY, USA.
  40. 40. B. Rasti, B. Koirala, P. Scheunders and P. Ghamisi, ”Spectral Unmixing Using Deep Convolutional Encoder-Decoder,” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 3829-3832, doi: 10.1109/IGARSS47720.2021.9553425.
  41. 41. B. Rasti, B. Koirala, P. Scheunders, P. Ghamisi and R. Gloaguen, ”Boosting Hyperspectral Image Unmixing Using Denoising: Four Scenarios,” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 3821-3824, doi: 10.1109/IGARSS47720.2021.9553942.
  42. K. R. Shahi, B. Rasti, P. Ghamisi, P. Scheunders and R. Gloaguen, ”When is the Right Time to Apply Denoising?,” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 2464-2467, doi: 10.1109/IGARSS47720.2021.9553263.
  43. K. R. Shahi, P. Ghamisi, R. Jackisch, B. Rasti, P. Scheunders and R. Gloaguen, ”A Multi-Sensor Subspace-Based Clustering Algorithm Using RGB and Hyperspectral Data,” 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2021, pp. 1-5, doi: 10.1109/WHISPERS52202.2021.9483953.
  44. A. Gruca, P. Herruzo, P. Rpodas, A. Kucik, C. Briese, M. K. Kopp, S. Hochreiter, P. Ghamisi, and D. P. Kreil, ”CDCEO’21 - First Workshop on Complex Data Challenges in Earth Observation”, In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM ’21), 2021. Association for Computing Machinery, New York, NY, USA, 48784879. DOI:https://doi.org/10.1145/3459637.3482044
  45. S. Chang and P. Ghamisi, Hyperspectral Anomaly Detection based on Low-rank Structure Exploration, In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM ’21), 1st Workshop on Complex Data Challenges in Earth Observation (CDCEO’21), http://ceur-ws.org/ Vol-3052/short2.pdf.
  46. D. Coquelin, B. Rasti, M. Götz, P. Ghamisi, R. Gloaguen, A. Streit, "HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package", https://arxiv.org/abs/2204.06979 [code].
 
 
 
 
 
 
 
Data Sets
​
  1. LiDAR and hyperspectral fusion [download]
  2. 2020 IEEE GRSS Data Fusion Contest [download]
  3. 2021 IEEE GRSS Data Fusion Contest - Track DSE [download]
  4. 2021 IEEE GRSS Data Fusion Contest - Track MSD [download]
  5. Modality Translation in Remote Sensing Time-series [download]
  6. Universal Adversarial Examples in Remote Sensing - UAE-RS [download]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Books
Journals
Conference Papers
[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]
​
 
Data Sets
aW4EVv0C (2).png
hif_hzdf_logos_eng_subline_below_blue_on
IARAI_Logo@2x.jpeg
bottom of page