ELM-based spectral–spatial classification of hyperspectral images using extended morphological profiles and composite feature mappings

References

• AVIRIS Indian Pine Test Site 3 dataset (2 × 2 mile portion of Northwest Tippecanoe County, Indiana). 1992. Accessed April 3, 2014. ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C.tif.zip (220-band hyperspectral image) and ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/ThyFiles.zip (ground truth)
   Google Scholar
• Baldi, P., C. Azencott, and S. J. Swamidass. 2011. “Bridging the Gap between Neural Network and Kernel Methods: Applications to Drug Discovery.” In Frontiers in Artificial Intelligence and Applications, edited by B. Apolloni, S. Bassis, A. Esposito, and C. F. Morabito, Vol. 226, 3–13. IOS Press.
   Google Scholar
• Benediktsson, J. A., and I. Kanellopoulos. 1999. “Classification of Multisource and Hyperspectral Data Based on Decision Fusion.” IEEE Transactions on Geoscience and Remote Sensing 37: 1367–1377. doi:10.1109/36.763301.
   Web of Science ®Google Scholar
• Benediktsson, J. A., J. A. Palmason, and J. R. Sveinsson. 2005. “Classification of Hyperspectral Data from Urban Areas Based on Morphological Profiles.” IEEE Transactions on Geoscience and Remote Sensing 43 (3): 480–491. doi:10.1109/TGRS.2004.842478.
   Web of Science ®Google Scholar
• Benediktsson, J. A., M. Pesaresi, and K. Arnason. 2003. “Classification and Feature Extraction for Remote Sensing Images from Urban Areas Based on Morphological Transformations.” IEEE Transactions on Geoscience and Remote Sensing 41 (9): 1940–1949. doi:10.1109/TGRS.2003.814625.
   Web of Science ®Google Scholar
• Broomhead, D. S., and D. Lowe. 1988. “Multivariable Functional Interpolation and Adaptive Networks.” Complex Systems 6 (2): 321–355.
   Google Scholar
• Camps-Valls, G., and L. Bruzzone. 2005. “Kernel-Based Methods for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 43 (6): 1351–1362. doi:10.1109/TGRS.2005.846154.
   Web of Science ®Google Scholar
• Camps-Valls, G., L. Gomez-Chova, J. Munoz-Mari, J. Vila-Francés, and J. Calpe-Maravilla. 2006. “Composite Kernels for Hyperspectral Image Classification.” IEEE Geoscience Remote Sensing Letters 3 (1): 93–97.
   Web of Science ®Google Scholar
• Cao, F., B. Liu, and D. S. Park. 2013. “Image Classification Based on Effective Extreme Learning Machine.” Neurocomputing 102: 90–97. doi:10.1016/j.neucom.2012.02.042.
   Web of Science ®Google Scholar
• Cao, J., Z. Lin, G.-B. Huang, and N. Liub. 2012. “Voting Based Extreme Learning Machine.” Information Sciences 185 (1): 66–77. doi:10.1016/j.ins.2011.09.015.
   Web of Science ®Google Scholar
• Chang, N.-B. 2011. “Satellite-Based Multitemporal-Change Detection in Urban Environments.” SPIE Newsroom, February 23. doi:10.1117/2.1201101.003502.
   Google Scholar
• Chen, Z. X., H. Y. Zhu, and Y. G. Wang. 2013. “A Modified Extreme Learning Machine with Sigmoidal Activation Functions.” Neural Computing & Applications 22 (3–4): 541–550. doi:10.1007/s00521-012-0860-2.
   Web of Science ®Google Scholar
• Courrieu, P. 2005. “Fast Computation of Moore–Penrose Inverse Matrices.” Neural Information Processing 8 (2): 25–29.
   Google Scholar
• Crespo, J., J. Serra, and R. W. Schafer. 1995. “Theoretical Aspects of Morphological Filters by Reconstruction.” Signal Processing 47 (2): 201–225. doi:10.1016/0165-1684(95)00108-5.
   Web of Science ®Google Scholar
• Fauvel, M., J. Chanussot, and J. A. Benediktsson. 2006. “Evaluation of Kernels for Multiclass Classification of Hyperspectral Remote Sensing Data.” In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), II813–II816.
   Google Scholar
• Fauvel, M., J. Chanussot, and J. A. Benediktsson. 2012. “A Spatial–Spectral Kernel-Based Approach for the Classification of Remote-Sensing Images.” Pattern Recognition 45 (1): 381–392. doi:10.1016/j.patcog.2011.03.035.
   Web of Science ®Google Scholar
• Fauvel, M., Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton. 2013. “Advances in Spectral–Spatial Classification of Hyperspectral Images.” Proceedings of the IEEE 101 (3): 652–675. doi:10.1109/JPROC.2012.2197589.
   Web of Science ®Google Scholar
• Heras, D. B., F. Argüello, J. L. Gomez, J. A. Becerra, and R. J. Duro. 2012. “Chap. 9. Towards Real-Time Hyperspectral Image Processing, a GP-GPU Implementation of Target Identification.” In Digital Image and Signal Processing for Measurement Systems, edited by R. J. Duro and F. Lopez Peña, 235–265. River Publishers.
   Google Scholar
• Heras, D. B., F. Argüello, and P. Quesada-Barriuso. 2014. “Exploring ELM-Based Spatial–Spectral Classification of Hyperspectral Images.” International Journal of Remote Sensing 35 (2): 401–423. doi:10.1080/01431161.2013.869633.
   Web of Science ®Google Scholar
• Huang, G.-B. 2014. “An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels.” Cognitive Computation 6 (3): 376–390. doi:10.1007/s12559-014-9255-2.
   Web of Science ®Google Scholar
• Huang, G.-B., and L. Chen. 2008. “Enhanced Random Search Based Incremental Extreme Learning Machine.” Neurocomputing 71 (16–18): 3460–3468. doi:10.1016/j.neucom.2007.10.008.
   Web of Science ®Google Scholar
• Huang, G.-B., L. Chen, and C.-K. Siew. 2006. “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes.” IEEE Transactions on Neural Networks 17 (4): 879–892. doi:10.1109/TNN.2006.875977.
   PubMed Web of Science ®Google Scholar
• Huang, G.-B., D. H. Wang, and Y. Lan. 2011. “Extreme Learning Machines: A Survey.” International Journal of Machine Learning and Cybernetics 2 (2): 107–122. doi:10.1007/s13042-011-0019-y.
   Web of Science ®Google Scholar
• Huang, G.-B., H. Zhou, X. Ding, and R. Zhang. 2012. “Extreme Learning Machine for Regression and Multiclass Classification.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42 (2): 513–529. doi:10.1109/TSMCB.2011.2168604.
   PubMed Web of Science ®Google Scholar
• Huang, G.-B., Q.-Y. Zhu, and C.-K. Siew. 2004. “Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks.” In Proceedings of International Joint Conference on Neural Networks (IJCNN), 985–990. IEEE.
   Google Scholar
• Huang, G.-B., Q.-Y. Zhu, and C.-K. Siew. 2006. “Extreme Learning Machine: Theory and Applications.” Neurocomputing 70 (1–3): 489–501. doi:10.1016/j.neucom.2005.12.126.
   Web of Science ®Google Scholar
• Igelnik, B., and Y.-H. Pao. 1995. “Stochastic Choice of Basis Functions in Adaptive Function Approximation and the Functional-Link Net.” IEEE Transactions on Neural Networks 6 (6): 1320–1329. doi:10.1109/72.471375.
   PubMed Web of Science ®Google Scholar
• Landgrebe, D. 2002. “Hyperspectral Image Data Analysis.” IEEE Signal Processing Magazine 19 (1): 17–28. doi:10.1109/79.974718.
   Web of Science ®Google Scholar
• Licciardi, G., P. R. Marpu, J. A. Benediktsson, and J. Chanussot. 2011. “Extended Morphological Profiles Using Auto-Associative Neural Networks for Hyperspectral Data Classification.” In Proceedings of IEEE Workshop Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1–4.
   Google Scholar
• Lowe, D. 1989. “Adaptive Radial Basis Function Nonlinearities, and the Problem of Generalisation.” In Proceedings of 1st Institution of Electrical Engineers International Conference on Artificial Neural Networks, 171–175. IET.
   Google Scholar
• Marconcini, M., G. Camps-Valls, and L. Bruzzone. 2009. “A Composite Semisupervised SVM for Classification of Hyperspectral Images.” IEEE Geoscience and Remote Sensing Letters 6 (2): 234–238. doi:10.1109/LGRS.2008.2009324.
   Web of Science ®Google Scholar
• Marpu, P. R., M. Pedergnana, M. D. Mura, S. Peeters, J. A. Benediktsson, and L. Bruzzone. 2012. “Classification of Hyperspectral Data Using Extended Attribute Profiles Based on Supervised and Unsupervised Feature Extraction Techniques.” International Journal of Image and Data Fusion 3 (3): 269–298. doi:10.1080/19479832.2012.702687.
   Google Scholar
• Melgani, F., and L. Bruzzone. 2004. “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42 (8): 1778–1790. doi:10.1109/TGRS.2004.831865.
   Web of Science ®Google Scholar
• Mercier, G., and M. Lennon. 2003. “Support Vector Machines for Hyperspectral Image Classification with Spectral-Based Kernels.” In Proceedings of IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), 288–290.
   Google Scholar
• Mountrakis, G., J. Im, and C. Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
   Web of Science ®Google Scholar
• Mura, M. D., J. A. Benediktsson, B. Waske, and L. Bruzzone. 2010. “Morphological Attribute Profiles for the Analysis of Very High Resolution Images.” IEEE Transactions on Geoscience and Remote Sensing 48 (10): 3747–3762. doi:10.1109/TGRS.2010.2048116.
   Web of Science ®Google Scholar
• Mura, D. M., A. Villa, J. A. Benediktsson, J. Chanussot, and L. Bruzzone. 2011. “Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis.” IEEE Geoscience and Remote Sensing Letters 8 (3): 542–546. doi:10.1109/LGRS.2010.2091253.
   Web of Science ®Google Scholar
• OpenBLAS. 2011. Accessed April 3, 2014. http://xianyi.github.com/OpenBLAS
   Google Scholar
• Pal, M. 2009. “Extreme-Learning-Machine-Based Land Cover Classification.” International Journal of Remote Sensing 30 (14): 3835–3841. doi:10.1080/01431160902788636.
   Web of Science ®Google Scholar
• Pal, M., A. E. Maxwell, and T. A. Warner. 2013. “Kernel-Based Extreme Learning Machine for Remote-Sensing Image Classification.” Remote Sensing Letters 4 (9): 853–862. doi:10.1080/2150704X.2013.805279.
   Web of Science ®Google Scholar
• Palmason, J. A., J. A. Benediktsson, J. R. Sveinsson, and J. Chanussot. 2005. “Classification of Hyperspectral Data from Urban Areas Using Morphological Preprocessing and Independent Component Analysis.” In Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 176–179.
   Google Scholar
• Pao, Y.-H., G.-H. Park, and D. J. Sobajic. 1994. “Learning and Generalization Characteristics of the Random Vector Functional-Link Net.” Neurocomputing 6 (2): 163–180. doi:10.1016/0925-2312(94)90053-1.
   Web of Science ®Google Scholar
• Pesaresi, M., and J. A. Benediktsson. 2001. “A New Approach for the Morphological Segmentation of High-Resolution Satellite Imagery.” IEEE Transactions on Geoscience and Remote Sensing 39 (2): 309–320. doi:10.1109/36.905239.
   Web of Science ®Google Scholar
• Plaza, A., J. A. Benediktsson, J. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni. 2009. “Recent Advances in Techniques for Hyperspectral Image Processing.” Remote Sensing of Environment 113: S110–S122. doi:10.1016/j.rse.2007.07.028.
   Web of Science ®Google Scholar
• Plaza, J., A. J. Plaza, and C. Barra. 2009. “Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines.” Sensors 9 (1): 196–218. doi:10.3390/s90100196.
   PubMed Web of Science ®Google Scholar
• Pontius, R. G., Jr, and M. Millones. 2011. “Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment.” International Journal of Remote Sensing 32 (15): 4407–4429. doi:10.1080/01431161.2011.552923.
   Web of Science ®Google Scholar
• Quesada-Barriuso, P., F. Argüello, and D. B. Heras. 2014. “Spectral-Spatial Classification of Hyperspectral Images Using Wavelets and Extended Morphological Profiles.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (4): 1177–1185. doi:10.1109/JSTARS.2014.2308425.
   Web of Science ®Google Scholar
• ROSIS Pavia Centre dataset. 2002. Accessed April 3, 2014. http://www.ehu.es/ccwintco/uploads/e/e3/Pavia.mat (102-band hyperspectral image) and http://www.ehu.es/ccwintco/uploads/5/53/Pavia_gt.mat (ground truth)
   Google Scholar
• ROSIS University of Pavia dataset. 2002. Accessed April 3, 2014. http://www.ehu.es/ccwintco/uploads/e/ee/PaviaU.mat (103-band hyperspectral image) and http://www.ehu.es/ccwintco/uploads/5/50/PaviaU_gt.mat (ground truth)
   Google Scholar
• Samat, A., P. Du, S. Liu, J. Li, and L. Cheng. 2014. “E2LMs: Ensemble Extreme Learning Machines for Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (4): 1060–1069. doi:10.1109/JSTARS.2014.2301775.
   Web of Science ®Google Scholar
• Schmidt, W. F., M. A. Kraaijveld, and R. P. W. Duin. 1992. “Feed Forward Neural Networks with Random Weights.” In Proceedings of 11th IAPR International Conference on Pattern Recognition, Vol. II. Conference B: Pattern Recognition Methodology and Systems, 1–4. IEEE.
   Google Scholar
• Scholkopf, B., and A. J. Smola. 2002. Learning with Kernels. Support Vector Machines Regularization Optimization and Beyond. Cambridge, MA: MIT Press.
   Google Scholar
• Soille, P. 2005. “Beyond Self-Duality in Morphological Image Analysis.” Image and Vision Computing 23 (2): 249–257. doi:10.1016/j.imavis.2004.06.002.
   Web of Science ®Google Scholar
• Soille, P., and M. Pesaresi. 2002. “Advances in Mathematical Morphology Applied to Geoscience and Remote Sensing.” IEEE Transactions on Geoscience and Remote Sensing 40 (9): 2042–2055. doi:10.1109/TGRS.2002.804618.
   Web of Science ®Google Scholar
• Tarabalka, Y., J. A. Benediktsson, and J. Chanussot. 2009. “Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques.” IEEE Transactions on Geoscience and Remote Sensing 47 (8): 2973–2987. doi:10.1109/TGRS.2009.2016214.
   Web of Science ®Google Scholar
• Tarabalka, Y., J. Chanussot, and J. A. Benediktsson. 2010a. “Segmentation and Classification of Hyperspectral Images Using Watershed Transformation.” Pattern Recognition 43 (7): 2367–2379. doi:10.1016/j.patcog.2010.01.016.
   Web of Science ®Google Scholar
• Tarabalka, Y., J. Chanussot, and J. A. Benediktsson. 2010b. “Segmentation and Classification of Hyperspectral Images Using Minimum Spanning Forest Grown from Automatically Selected Markers.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40 (5): 1267–1279. doi:10.1109/TSMCB.2009.2037132.
   PubMed Web of Science ®Google Scholar
• Tarabalka, Y., M. Fauvel, J. Chanussot, and J. A. Benediktsson. 2010. “SVM and MRF-based Method for Accurate Classification of Hyperspectral Images.” IEEE Geoscience Remote Sensing Letters 7 (4): 736–740. doi:10.1109/LGRS.2010.2047711.
   Web of Science ®Google Scholar
• Wang, L., and C. Wan. 2008. “Comments on “The Extreme Learning Machine”.” IEEE Transactions on Neural Networks 19 (8): 1494–1495. doi:10.1109/TNN.2008.2002273.
   PubMed Web of Science ®Google Scholar
• Wang, Y., F. Cao, and Y. Yuan. 2011. “A Study on Effectiveness of Extreme Learning Machine.” Neurocomputing 74 (16): 2483–2490. doi:10.1016/j.neucom.2010.11.030.
   Web of Science ®Google Scholar
• Zhang, Y., X. Feng, and X. Le. 2008. “Segmentation on Multispectral Remote Sensing Image Using Watershed Transformation.” In Proceedings of International Congress on Image and Signal Processing (CISP), 773–777. IEEE.
   Google Scholar