Journals and Conference papers




Dataset

Ghamisi, Pedram; Phinn, Stuart (2015): Fusion of LiDAR and Hyperspectral Data. figshare. https://dx.doi.org/10.6084/m9.figshare.2007723.v3

For those of you who are interested in the fusion of LiDAR and hyperspectral data or the classification of hyperspectral images, we made our dataset public. The dataset was captured over Samford Ecological Research Facility (SERF), Queensland, Australia. The dataset is composed of hyperspectral and LiDAR data as well as their corresponding training and test samples. You may download the data from the following address:
https://figshare.com/articles/Main_zip/2007723



PhD Thesis

P. Ghamisi, Spectral and Spatial Classification of Hyperspectral Data, Ph.D. thesis,

P. Ghamisi, Spectral and Spatial Classification of Hyperspectral Data, Ph.D. thesis, University of Iceland, 2015.
http://skemman.is/en/item/view/1946/20837



Books

[B2] J. A. Benediktsson and P. Ghamisi, Spectral-Spatial Classification of Hyperspectral Remote Sensing Images,

[B2] J. A. Benediktsson and P. Ghamisi, Spectral-Spatial Classification of Hyperspectral Remote Sensing Images, Artech House Publishers, INC, Boston, USA.
Spectral-Spatial Classification of Hyperspectral Remote Sensing Images



Journal papers

[J35] B. Rasti, M. O. Ulfarsson, and P.Ghamisi, "Automatic Hyperspectral Image Restoration Using Sparse and Low-Rank Modeling"

B. Rasti, M. O. Ulfarsson, and P.Ghamisi, "Automatic Hyperspectral Image Restoration Using Sparse and Low-Rank Modeling", IEEE Geoscience and Remote Sensing Letters, accepted.

[J34] Y. Chen, L. Zhu, P. Ghamisi, X. Jia, and L. Tang "Hyperspectral Images Classification with Gabor Filtering and Convolutional Neural Network"

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, accepted.

[J33] P. Ghamisi, N. Yokoya, J. Li, W. Liao, J. Plaza, B. Rasti, and A. Plaza, "Advances in Hyperspectral Image and Signal Processing"

P. Ghamisi, N. Yokoya, J. Li, W. Liao, J. Plaza, B. Rasti, and A. Plaza, "Advances in Hyperspectral Image and Signal Processing", IEEE Geoscience and Remote Sensing Magazine, accepted.

[J32] J. Xia, P. Ghamisi, N. Yokoya, and A. Iwasaki, "Random Forest Ensembles and Extended Multi-Extinction Profiles for Hyperspectral Image Classification"

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, [accepted].

[J31] L. Mou, P. Ghamisi, X. X. Zhu, "Unsupervised Spectral-Spatial Feature Learning via Deep Residual Conv-Deconv Network for Hyperspectral Image Classification"

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, [accepted].

[J30] B. Rasti, P. Ghamisi, J. Plaza, and A. Plaza, "Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis"

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, DOI: 10.1109/TGRS.2017.2726901, [in press].

http://ieeexplore.ieee.org/document/8000656/
[J29] M. Zhang, P. Ghamisi, and W. Li, "Classification of hyperspectral and LIDAR data using extinction profiles with feature fusion"

M. Zhang, P. Ghamisi, and W. Li, "Classification of hyperspectral and LIDAR data using extinction profiles with feature fusion", Remote Sensing Letters, , vol. 8, no. 10, pp. 957-966, 2017.

http://www.tandfonline.com/doi/full/10.1080/2150704X.2017.1335902
[J28] Y. Chen, C. Li, P. Ghamisi, X. Jia, Y. Gu, "Deep Fusion of Remote Sensing Data for Accurate Classification,"

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.

http://ieeexplore.ieee.org/document/7940007/
[J27] 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 ..."

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.

http://spie.org/Publications/Journal/10.1117/1.JRS.11.026009?SSO=1
[J26] B. Rasti, P. Ghamisi, and R. Gloaguan, "Hyperspectral and LiDAR Fusion Using Extinction Profiles and Total Variation Component Analysis"

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.

http://ieeexplore.ieee.org/document/7902153/
[J25] P. Ghamisi and B. Hofle, "LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine"

P. Ghamisi and B. Hofle, "LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine", IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 5, pp. 659-663, 2017.

http://ieeexplore.ieee.org/document/7873288/
[J24] P. Ghamisi, B. Hofle, X. X. Zhu, "Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network"

P. Ghamisi, B. Hofle, X. X. Zhu, "Hyperspectral and LiDAR Data Fusion Using Extinction Profiles 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.

http://ieeexplore.ieee.org/document/7786851/
[J23] L. Mou, P. Ghamisi, X. X. Zhu, "Deep Recurrent Neural Networks for Hyperspectral Image Classification"

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 in July 2017].

http://ieeexplore.ieee.org/document/7914752/
[J22] P. Ghamisi, J. Plaza, Y. Chen, J. Li, and A. Plaza, “Advanced Spectral Classifiers for Hyperspectral Images: A Review”

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.

[J21] Y. Chen, S. Ma, X. Chen, and P. Ghamisi, "Hyperspectral Data Clustering Based on Density Analysis Ensemble"

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,


http://www.tandfonline.com/doi/full/10.1080/2150704X.2016.1249295
[J20] P. Ghamisi, R. Souza, J. A. Benediktsson, L. Rittner, R. Lotufo, X. X. Zhu, "Hyperspectral Data Classifcation Using Extended Extinction Profiles"

P. Ghamisi, R. Souza, J. A. Benediktsson, L. Rittner, R. Lotufo, X. X. Zhu, "Hyperspectral Data Classi cation Using Extended Extinction Profi les", IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 11, pp. 1641-1645, Nov. 2016.

[J19] P. Ghamisi, Y. Chen, and X. X. Zhu, "A Self-Improving Convolution Neural Network for the Classi cation of Hyperspectral Data"

P. Ghamisi, Y. Chen, and X. X. Zhu, "A Self-Improving Convolution Neural Network for the Classi cation 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 2016].

[J18] Y. Chen, H. Jiang, C. Li, X. Jia and P. Ghamisi, "Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks,"

Y. Chen, H. Jiang, C. Li, X. Jia and P. Ghamisi, "Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks," in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 10, pp. 6232-6251, Oct. 2016.


http://ieeexplore.ieee.org/document/7514991/
[J17] P. Ghamisi; R. Souza; J. A. Benediktsson; X. X. Zhu; L. Rittner; R. A. Lotufo, "Extinction Profiles for the Classification of Remote Sensing Data,"

P. Ghamisi; R. Souza; J. A. Benediktsson; X. X. Zhu; L. Rittner; R. A. Lotufo, "Extinction Profiles for the Classification of Remote Sensing Data," in 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]


http://ieeexplore.ieee.org/document/7514921/
[J16] P. Ghamisi, J. A. Benediktsson, and S. Phinn, "Landcover classification using both hyperspectral and lidar data,"

[J16] P. Ghamisi, J. A. Benediktsson, and S. Phinn, "Landcover classification using both hyperspectral and lidar data," International Journal of Image and Data Fusion, vol. 6, no. 3, pp. 189ᵬ 2015.

[J15] P. Ghamisi, A. ALi, M. S. Couceiro and J. A. Benediktsson, "A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery,"

[J15] 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, accepted.

[J14] S.Kargozar Nahavandy, P Ghamisi, L Kumar and M S Couceiro. Article: A Novel Adaptive Compression Technique for Dealing with Corrupt Bands and High Levels of ...

S.Kargozar Nahavandy, P Ghamisi, L Kumar and M S Couceiro. Article: A Novel Adaptive Compression Technique for Dealing with Corrupt Bands and High Levels of Band Correlations in Hyperspectral Images Based on Binary Hybrid GA-PSO for Big Data Compression. International Journal of Computer Applications 109(8):18-25, January 2015.
http://www.ijcaonline.org/archives/volume109/number8/19208-0915

[J13] P. Ghamisi, M. S. Couceiro and J. A. Benediktsson, "A Novel Feature Selection Approach Based on FODPSO and SVM,"

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.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6980119&queryText%3Dghamisi

[J12] P. Ghamisi, M. Dalla Mura and J. A. Benediktsson, "A Survey on Spectral-Spatial Classification Techniques Based on Attribute Profiles,"

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.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6945376&queryText%3Dghamisi

[J11] Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization

P. Ghamisi, J. A. Benediktsson, Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization, IEEE Geoscience and Remote Sensing Letter, 12(2), 309-313, Feb. 2015., DOI: 10.1109/LGRS.2014.2337320.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6866865&queryText%3Dghamisi

[J10] Automatic Framework for Spectral-Spatial Classification Based on Supervised Feature Extraction and Morphological Attribute Profiles

P. Ghamisi, J. A. Benediktsson, G. Cavallaro, 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, 7(6): 2147-2160, 2014.
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6729052&isnumber=4609444

[J9] Automatic Spectral-Spatial Classification Framework Based onAttribute Profiles and Supervised Feature Extraction

P. Ghamisi, J.A. Benediktsson and J.R. Sveinsson, Automatic Spectral-Spatial Classification Framework Based onAttribute Profiles and Supervised Feature Extraction, IEEE Trans. on Geoscience and Remote Sensing, 52(9): 5771-5782, 2014.
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6685827&isnumber=4358825

[J8] Spectral-Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields

P. Ghamisi, J. A. Benediktsson, M. O. Ulfarsson, Spectral-Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields, IEEE Trans. Remote Sensing and Geoscience. 52(5): 2565-2574, 2014.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6532336&queryText%3DGHAMISI

[J7] Integration of Segmentation Techniques for Classification of Hyperspectral Images

P. Ghamisi, M. Couceiro, M. Fauvel and J. A. Benediktsson, Integration of Segmentation Techniques for Classification of Hyperspectral Images, IEEE Geosci. Remote Sensing Lett. 11(1): 342-346, 2014.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6545298&queryText%3DGHAMISI

[J6] Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization

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 Trans. Geoscience and Remote Sensing, 52(5): 2382-2394, 2014.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6524014&queryText%3DGHAMISI

[J5] A New Method for Compression of Remote Sensing Images Based on Enhanced Differential Pulse Code Modulation Transformation

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, ScienceAsia, 39(2013): 546-555, 2013.
http://www.scienceasia.org/content/viewabstract.php?ms=3923

[J4] An Efficient Method for Segmentation of Images Based on Fractional Calculus and Natural Selection

P. Ghamisi, M. S. Couceiro, J. A. Benediktsson, N. M. F. Ferreira "An Efficient Method for Segmentation of Images Based on Fractional Calculus and Natural Selection", Expert Systems with Application, 39 (2012) 12407-12417.
http://www.sciencedirect.com/science/article/pii/S0957417412006756

[J3] Efficient Adaptive Lossless Compression of Hyperspectral Data Using Enhanced DPCM

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.
http://research.ijcaonline.org/volume35/number4/pxc3976078.pdf

[J2] A Novel Method for Segmentation of Remote Sensing Images based on Hybrid GA-PSO

[J2] 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.
http://research.ijcaonline.org/volume29/number2/pxc3874846.pdf

[J1] A Novel Real Time Algorithm for Remote Sensing Lossless Data Compression based on Enhanced DPCM

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.
http://www.ijcaonline.org/volume27/number1/pxc3874402.pdf




Conference papers

[C19] J. Hu, P. Ghamisi, A. Schmitt, and X. X. Zhu, "Object based fusion of polarimetric SAR and hyperspectral imaging for land use classification"

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.

[C18] N. Yokoya and P. Ghamisi, "Land-Cover monitoring using time-series hyperspectral data via fractional-order Darwinian particle swarm optimization Segmentation"

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.

[C17] P. Ghamisi, R. Souza, J. A. Benediktsson, X. X. Zhu, L. Rittner and R. Lotufo, "Extended extinction profile for the classification of hyperspectral images"

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.

[C16] 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,"

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.

[C15] Y. Chen, C. Li, P. Ghamisi, C. Shi, "Convolutional neural network fusion of hyperspectral and LiDAR data for thematic classification,"

Y. Chen, C. Li, P. Ghamisi, C. Shi, "Convolutional neural network fusion of hyperspectral and LiDAR data for thematic classification," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.

[C14] P. Ghamisi, D. Wu, G. Cavallaro, J. A. Benediktsson, S. Phinn and N. Falco, "An advanced classifier for the joint use of LiDAR and hyperspectral data: Case study in Queensland, Australia,"

P. Ghamisi, D. Wu, G. Cavallaro, J. A. Benediktsson, S. Phinn and N. Falco, "An advanced classifier 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.

[C13] P. Ghamisi and J. A. Benediktsson, "Feature Selection of Hyperspectral Data by Considering the Integration of Genetic Algorithms and Particle Swarm Optimization,"

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.

[C12] Fusion of Hyperspectral and LiDAR Data in Classification of Urban Areas

P. Ghamisi, J. A. Benediktsson, S. Phinn, Fusion of Hyperspectral and LiDAR Data in Classification of Urban Areas, IGARSS, Canada, 2014, [Invited paper].

[C11] FODSPO Based Feature Selection for Hyperspectral Remote Sensing Data

P. Ghamisi, M. S. Couceiro and J. A. Benediktsson, FODSPO Based Feature Selection for Hyperspectral Remote Sensing Data, WHISPERS, Switzerland, 2014.

[C10] Classification of hyperspectral images with binary fractional order Darwinian PSO and random forests

P. Ghamisi, M. S. Couceiro, J. A. Benediktsson, Classification of hyperspectral images with binary fractional order Darwinian PSO and random forests . Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 88920S (October 17, 2013); doi:10.1117/12.2027641.

[C9] The Spectral Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Field and its Expectation-Maximization

P. Ghamisi, J. A. Benediktsson, M. O. Ulfarsson, THE SPECTRAL SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGES BASED ON HIDDEN MARKOV RANDOM FIELD AND ITS EXPECTATION-MAXIMIZATION, IGARSS 2013, Melbourne, JULY 2013 (As the best paper in the student paper competition in IGARSS 2013)

[C8] Spectral-Spatial Classification Based on Integrated segmentation

P. Ghamisi, M. S. Couceiro, M. Fauvel, J. A. Benediktsson, SPECTRAL-SPATIAL CLASSIFICATION BASED ON INTEGRATED SEGMENTATION, IGARSS 2013, Melbourne, JULY 2013

[C7] Extending the fractional order Darwinian particle swarm optimization to segmentation of hyperspectral images

P. Ghamisi, M. S. Couceiro and J. A. Benediktsson "Extending the fractional order Darwinian particle swarm optimization to segmentation of hyperspectral images", Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85370F (November 8, 2012);
http://dx.doi.org/10.1117/12.978776

[C6] Use of Darwinian Particle Swarm Optimization technique for the segmentation of Remote Sensing images

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," Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International , vol., no., pp.4295-4298, 22-27 July 2012,
http://dx.doi.org/10.1109/IGARSS.2012.6351718

[C5] Binary Hybrid GA-PSO based algorithm for compression of hyperspectral data

P. Ghamisi, F. Sepehrband, J. Choupan, M. Mortazavi, "Binary Hybrid GA-PSO based algorithm for compression of hyperspectral data," Signal Processing and Communication Systems (ICSPCS), 2011 5th International Conference on , vol., no., pp.1-8, 12-14 Dec. 2011
http://dx.doi.org/10.1117/12.904727

[C4] Binary Hybrid GA-PSO based algorithm for compression of hyperspectral data

P. Ghamisi, F. Sepehrband, J. Choupan, M. Mortazavi, "Binary Hybrid GA-PSO based algorithm for compression of hyperspectral data," 2011 5th International Conference on Signal Processing and Communication Systems (ICSPCS), vol., no., pp.1-8, 12-14 Dec. 2011
http://dx.doi.org/10.1109/ICSPCS.2011.6140839

[C3] Simple and Efficient Remote Sensing Image Transformation for Lossless Compression

F. Sepehrband, P. Ghamisi, M. Mortazavi, J. Choupan, "Simple and Efficient Remote Sensing Image Transformation for Lossless Compression". International Conference on Signal and InformationProcessing (ICSIP'10), Changsha, China, December, 2010. (Published).

[C2] Simple and efficient remote sensing image transformation for lossless compression

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);
http://dx.doi.org/10.1117/12.913262

[C1] Fast and Efficient Algorithm for Real Time Lossless Compression of LiDAR rasterized data Based on Improving Energy Compaction

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. (Published).