A PSO-based End-Member Selection Method for Spectral Unmixing of Multispectral Satellite Images
An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm has been successfully applied to test image sets from various platforms such as LANDSAT 5 MSS and NOAA's AVHRR. The experimental results of the proposed algorithm are encouraging. The influence of different values of the algorithm control parameters on performance is studied. Furthermore, the performance of different versions of PSO is also investigated.
[1] J. Saghri, A. Tescher, M. Omran, Class-Prioritized Compression of
Multispectral Imagery Data, Journal of Electronic Imaging, vol. 11 (2),
246-256, 2002.
[2] J. Kennedy, R. Eberhart, Swarm Intelligence, Morgan Kaufmann, 2001.
[3] J. J. Settle, N. A. Drake, Linear Mixing and Estimation of Ground Cover
Proportions, International Journal in Remote Sensing, vol. 14 (6), 1159-
1177, 1993.
[4] J. Antoniades, D. Haas, P. Palmadesso, M. Baumback, L. J. Rickard, Use
of Filter Vectors in Hyperspectral Data Analysis, In Proceedings of
SPIE, vol. 2553, 128-139, 1995.
[5] A. Hlavka, M. A. Spanner, Unmixing AVHRR Imagery to Access
Clearcuts and Forest Regrowth on Oregon, IEEE Transactions on
Geoscience and Remote Sensing, vol. 33, 788-795, 1995.
[6] A. Bateson, B. Curtiss, A Method for Manual Endmember Selection and
Spectral Unmixing, Remote Sensing of Enviornment, vol. 55, 229-243,
1996.
[7] F. Maselli, Multiclass Spectral Decomposition of Remotely Sensed
Scenes by Selective Pixel Unmixing, IEEE Transactions on Geoscience
and Remote Sensing, vol. 36 (5), 1809-1819, 1998.
[8] L. Parra, C. Spence, P. Sajda, A. Ziehe, K. M├╝ller, Unmixing
Hyperspectral Data, In Advances in Neural Information Processing
Systems 12, MIT Press, 942-948, 2000.
[9] J. Saghri, A. Tescher, F. Jaradi, M. Omran, A Viable End-Member
Selection Scheme for Spectral Unmixing of Multispectral Satellite
Imagery Data, Journal of Imaging Science and Technology, vol. 44 (3),
196-203, 2000.
[10] A. Plaza, P. Martinez, R. Perez, J. Plaza, A Quantitative and
Comparative Analysis of Endmember Extraction Algorithms from
Hyperspectral Data, IEEE Transactions on Geoscience and Remote
Sensing, vol. 42(3), 650-663, 2004.
[11] J. Crespo, R. Duro, F. Lopez-Pena, Spectral Unmixing Through
Gaussian Synapse ANNs in Hyperspecteal Images, Proceedings of the 8th
International Conference on Knowledge-Based Intelligent Information
and Engineering Systems, Wellington, New Zealand, 661-668, 2004.
[12] M. Grana, A. D'Anjou, Feature Extraction by Linear Spectral Unmixing,
Proceedings of the 8th International Conference on Knowledge-Based
Intelligent Information and Engineering Systems, Wellington, New
Zealand, 692-698, 2004.
[13] J. Zhang, B. Rivard, A. Sanchez-Azofeifa, Derivative Spectral Unmixing
of Hyperspectral Data Applied to Mixtures of Lichen and Rock, IEEE
Transactions on Geoscience and Remote Sensing, vol. 42(9), 1934-1940,
2004.
[14] S. McDonald, K. Niemann, D. Goodenough, Development of
Hyperspectral Biochemistry through the use of Statistical Modeling and
Spectral Unmixing, Proceedings of the IEEE International Geoscince
and Remote Sensing Symposium, vol. 2, 1007-1009, 2004.
[15] M. Cauguy, M. Roggemann, T. Schulz, Spectral Unmixing Methods to
Estimate Materials on Satellite Surface, Proceedings of the 36th
Southeastern Symposium on System Theory, 11-15, 2004.
[16] J. Settle, On the Residual Term in Linear Mixture Model and its
Dependence on the Point Spread Function, IEEE Transactions on
Geoscience and Remote Sensing, vol. 43(2), 398-401, 2005.
[17] J. Broadwater, R. Meth and R. Chellappa, A hybrid Algorithm for
Subpixel Detection in Hyperspectral Imagery, Proceedings of the IEEE
International Geoscince and Remote Sensing Symposium, vol. 3, 1601-
1604, 2004.
[18] C. Shah, P. Varshney, A Higher Order Statistical Appraoch to Spectral
Unmixing of Remote Sensing Imagery, Proceedings of the IEEE
International Geoscince and Remote Sensing Symposium, vol. 2, 1065-
1068, 2004.
[19] G. Ball and D. Hall, A Clustering Technique for Summarizing
Multivariate Data, Behavioral Science, vol. 12, 153-155, 1967.
[20] J. Kennedy, R. Eberhart, Particle Swarm Optimization, Proceedings of
IEEE International Conference on Neural Networks, Perth, Australia,
vol. 4, 1942-1948, 1995.
[21] A. Engelbrecht, Computational Intelligence: An Introduction, John
Wiley and Sons, 2002.
[22] Y. Shi, R. Eberhart, Parameter Selection in Particle Swarm
Optimization, Evolutionary Programming VII: Proceedings of EP 98,
591-600, 1998.
[23] P. Suganthan, Particle Swarm Optimizer with Neighborhood Optimizer,
Proceedings of the Congress on Evolutionary Computation, 1958-1962,
1999.
[24] Y. Shi, R. Eberhart, A Modified Particle Swarm Optimizer, Proceedings
of the IEEE International Conference on Evolutionary Computation,
Piscataway, NJ, 69-73, 1998.
[25] J. Kennedy, Small Worlds and Mega-Minds: Effects of Neighborhood
Topology on Particle Swarm Performance, Proceedings of the Congress
on Evolutionary Computation, 1931-1938, 1999.
[26] J. Kennedy, R. Mendes, Population Structure and Particle Performance,
Proceedings of the IEEE Congress on Evolutionary Computation,
Honolulu, Hawaii, 2002.
[27] F. Van den Bergh, An Analysis of Particle Swarm Optimizers, PhD
Thesis, Department of Computer Science, University of Pretoria, 2002.
[28] F. van den Bergh, A.P. Engelbrecht, A New Locally Convergent Particle
Swarm Optimizer, Proceedings of the IEEE Conference on Systems,
Man, and Cybernetics, Hammamet, Tunisia, 2002.
[1] J. Saghri, A. Tescher, M. Omran, Class-Prioritized Compression of
Multispectral Imagery Data, Journal of Electronic Imaging, vol. 11 (2),
246-256, 2002.
[2] J. Kennedy, R. Eberhart, Swarm Intelligence, Morgan Kaufmann, 2001.
[3] J. J. Settle, N. A. Drake, Linear Mixing and Estimation of Ground Cover
Proportions, International Journal in Remote Sensing, vol. 14 (6), 1159-
1177, 1993.
[4] J. Antoniades, D. Haas, P. Palmadesso, M. Baumback, L. J. Rickard, Use
of Filter Vectors in Hyperspectral Data Analysis, In Proceedings of
SPIE, vol. 2553, 128-139, 1995.
[5] A. Hlavka, M. A. Spanner, Unmixing AVHRR Imagery to Access
Clearcuts and Forest Regrowth on Oregon, IEEE Transactions on
Geoscience and Remote Sensing, vol. 33, 788-795, 1995.
[6] A. Bateson, B. Curtiss, A Method for Manual Endmember Selection and
Spectral Unmixing, Remote Sensing of Enviornment, vol. 55, 229-243,
1996.
[7] F. Maselli, Multiclass Spectral Decomposition of Remotely Sensed
Scenes by Selective Pixel Unmixing, IEEE Transactions on Geoscience
and Remote Sensing, vol. 36 (5), 1809-1819, 1998.
[8] L. Parra, C. Spence, P. Sajda, A. Ziehe, K. M├╝ller, Unmixing
Hyperspectral Data, In Advances in Neural Information Processing
Systems 12, MIT Press, 942-948, 2000.
[9] J. Saghri, A. Tescher, F. Jaradi, M. Omran, A Viable End-Member
Selection Scheme for Spectral Unmixing of Multispectral Satellite
Imagery Data, Journal of Imaging Science and Technology, vol. 44 (3),
196-203, 2000.
[10] A. Plaza, P. Martinez, R. Perez, J. Plaza, A Quantitative and
Comparative Analysis of Endmember Extraction Algorithms from
Hyperspectral Data, IEEE Transactions on Geoscience and Remote
Sensing, vol. 42(3), 650-663, 2004.
[11] J. Crespo, R. Duro, F. Lopez-Pena, Spectral Unmixing Through
Gaussian Synapse ANNs in Hyperspecteal Images, Proceedings of the 8th
International Conference on Knowledge-Based Intelligent Information
and Engineering Systems, Wellington, New Zealand, 661-668, 2004.
[12] M. Grana, A. D'Anjou, Feature Extraction by Linear Spectral Unmixing,
Proceedings of the 8th International Conference on Knowledge-Based
Intelligent Information and Engineering Systems, Wellington, New
Zealand, 692-698, 2004.
[13] J. Zhang, B. Rivard, A. Sanchez-Azofeifa, Derivative Spectral Unmixing
of Hyperspectral Data Applied to Mixtures of Lichen and Rock, IEEE
Transactions on Geoscience and Remote Sensing, vol. 42(9), 1934-1940,
2004.
[14] S. McDonald, K. Niemann, D. Goodenough, Development of
Hyperspectral Biochemistry through the use of Statistical Modeling and
Spectral Unmixing, Proceedings of the IEEE International Geoscince
and Remote Sensing Symposium, vol. 2, 1007-1009, 2004.
[15] M. Cauguy, M. Roggemann, T. Schulz, Spectral Unmixing Methods to
Estimate Materials on Satellite Surface, Proceedings of the 36th
Southeastern Symposium on System Theory, 11-15, 2004.
[16] J. Settle, On the Residual Term in Linear Mixture Model and its
Dependence on the Point Spread Function, IEEE Transactions on
Geoscience and Remote Sensing, vol. 43(2), 398-401, 2005.
[17] J. Broadwater, R. Meth and R. Chellappa, A hybrid Algorithm for
Subpixel Detection in Hyperspectral Imagery, Proceedings of the IEEE
International Geoscince and Remote Sensing Symposium, vol. 3, 1601-
1604, 2004.
[18] C. Shah, P. Varshney, A Higher Order Statistical Appraoch to Spectral
Unmixing of Remote Sensing Imagery, Proceedings of the IEEE
International Geoscince and Remote Sensing Symposium, vol. 2, 1065-
1068, 2004.
[19] G. Ball and D. Hall, A Clustering Technique for Summarizing
Multivariate Data, Behavioral Science, vol. 12, 153-155, 1967.
[20] J. Kennedy, R. Eberhart, Particle Swarm Optimization, Proceedings of
IEEE International Conference on Neural Networks, Perth, Australia,
vol. 4, 1942-1948, 1995.
[21] A. Engelbrecht, Computational Intelligence: An Introduction, John
Wiley and Sons, 2002.
[22] Y. Shi, R. Eberhart, Parameter Selection in Particle Swarm
Optimization, Evolutionary Programming VII: Proceedings of EP 98,
591-600, 1998.
[23] P. Suganthan, Particle Swarm Optimizer with Neighborhood Optimizer,
Proceedings of the Congress on Evolutionary Computation, 1958-1962,
1999.
[24] Y. Shi, R. Eberhart, A Modified Particle Swarm Optimizer, Proceedings
of the IEEE International Conference on Evolutionary Computation,
Piscataway, NJ, 69-73, 1998.
[25] J. Kennedy, Small Worlds and Mega-Minds: Effects of Neighborhood
Topology on Particle Swarm Performance, Proceedings of the Congress
on Evolutionary Computation, 1931-1938, 1999.
[26] J. Kennedy, R. Mendes, Population Structure and Particle Performance,
Proceedings of the IEEE Congress on Evolutionary Computation,
Honolulu, Hawaii, 2002.
[27] F. Van den Bergh, An Analysis of Particle Swarm Optimizers, PhD
Thesis, Department of Computer Science, University of Pretoria, 2002.
[28] F. van den Bergh, A.P. Engelbrecht, A New Locally Convergent Particle
Swarm Optimizer, Proceedings of the IEEE Conference on Systems,
Man, and Cybernetics, Hammamet, Tunisia, 2002.
@article{"International Journal of Information, Control and Computer Sciences:56172", author = "Mahamed G.H. Omran and Andries P Engelbrecht and Ayed Salman", title = "A PSO-based End-Member Selection Method for Spectral Unmixing of Multispectral Satellite Images", abstract = "An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm has been successfully applied to test image sets from various platforms such as LANDSAT 5 MSS and NOAA's AVHRR. The experimental results of the proposed algorithm are encouraging. The influence of different values of the algorithm control parameters on performance is studied. Furthermore, the performance of different versions of PSO is also investigated.
", keywords = "End-members selection, multispectral satellite imagery, particle swarm optimization, spectral unmixing.", volume = "2", number = "6", pages = "1972-9", }
