A novel approach for measuring hyperspectral similarity

Applied Soft Computing Journal

Volumn 12, Issue 10, 2012, Pages 3115-3123

  Galal, Abdulrahman ^a   Hassan, Hesham ^b   Imam, Ibrahim F ^c  

^a Arab Administrative Development Organization   (Egypt)

^b Cairo University   (Egypt)

^c VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

Adaptive similarity threshold; Hyperspectral measures; Support vector machine

Indexed keywords

BENCHMARK DATASETS; CORRELATION COEFFICIENT; COSINE SIMILARITY; DEGREE OF SIMILARITY; GEOMETRICAL FEATURES; HYPERSPECTRAL; ORTHOGONAL PROJECTION; SIMILARITY MEASURE; SIMILARITY PATTERNS; SIMILARITY THRESHOLD; SPECTRAL ANGLE MAPPERS; SPECTRAL REGION; SPECTRAL SIGNATURE; TECHNIQUES USED;

SOFT COMPUTING; SOFTWARE ENGINEERING; SUPPORT VECTOR MACHINES;

PROBABILITY DISTRIBUTIONS;

EID: 84864767147     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2012.06.018     Document Type: Article

References (22)

1. W. Li, S. Prasad, J. Fowler, and L. Bruce Class dependent compressive-projection principal component analysis for hyperspectral image reconstruction Proceedings of the Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing 2011
2. Z. Denghui, and Y. Le Support vector machine based classification for hyperspectral remote sensing images after minimum noise fraction rotation transformation Proceedings of the Internet Computing and Information Services Conference September 2011
3. M. Dalla Mura, A.J. Villa, A. Benediktsson, J. Chanussot, and L. Bruzzone Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis IEEE Geoscience and Remote Sensing Letters 8 3 2011 541 545
4. S. Hemissi, K. Ettabaa, I. Farah, and B. Soulaiman Towards multi-temporal hyperspectral images classification based on 3D signature model and matching Proceedings of the Hyperspectral 2010 Workshop 2010
5. M. Bilenko, and R. Mooney Adaptive duplicate detection using learnable string similarity measures Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2003
6. F. Chen, A. Farahat, and T. Brants Multiple similarity measures and source-pair information in story link detection Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, May 2004
7. F. Tsai, and W. Philpot Derivative analysis of hyperspectral data Remote Sensing of Environment 66 1 1998 41 51 (Pubitemid 29360427)
8. K. Castro-Esau, A. Sa'chez-Azofeifa, B. Rivard, J. Wright, and M. Quesada Variability in leaf optical properties of mesoamerican trees and the potential for species classification American Journal of Botany 4 2006 517 530
9. F. Van der Meer The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery International Journal of Applied Earth Observations and Geoinformation 8 1 2006 3 17
10. C. Wu, and C. Chang Soft decision-made hyperspectral measures for target discrimination and classification Proceedings of the Society of Photo-Optical Instrumentation Engineers 7457 2009 74570S
11. O.A. De Carvalho, and P.R. Meneses Spectral correlation mapper (SCM): an improvement on the spectral angle mapper (SAM) Proceedings of the 9th Annual JPL Airborne Earth Science Workshop 2000
12. Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J.O. Jensen, and F.M. D'Amico New hyperspectral discrimination measure for spectral characterization Journal of Optical Engineering 43 8 2004 1777 1786
13. J. Farifteh, F. Van der Meer, and E.J.M. Carranza Similarity measures for spectral discrimination of saline soils International Journal of Remote Sensing 28 23 2007 5273 5293 (Pubitemid 351444932)
14. E. Lo, and J. Ingram Hyperspectral anomaly detection based on minimum generalized variance method Proceedings of the Society of Photo-Optical Instrumentation Engineers 6966 2008 696603
15. B. Demir, and S. Ertürk Spectral magnitude and spectral derivative feature fusion for improved classification of hyperspectral Images Proceedings of the International Conference on Geoscience and Remote Sensing Symposium, July 2008
16. L. Weizman, and J. Goldberger A classification based linear-projection of labeled hyperspectral data Proceedings of the IEEE Geoscience and Remote Sensing Symposium, July 2007
17. C. Chih-Chung, and L. Chih-Jen LIBSVM: a library for support vector machines ACM Transactions on Intelligent Systems and Technology Journal 2 3 2011 1 27
18. P. Watanachaturaporn, P. Varshney, and M. Arora Evaluation of factors affecting support vector machines for hyperspectral classification Proceedings of the American Society for Photogrammetry and Remote Sensing Conference, May 2004
19. https://gridsphere.rcac.purdue.edu:10443/irodsPortal/FileDownload? filename=av920612-NS-line.lan&homedir=/rcacZone/home/lars/DVR-021/ av920612-NS-line&username=biehl
20. http://cobweb.ecn.purdue.edu/∼biehl/av920612-NS-line-gr.zip
21. O. Muresan, F. Pop, D. Gorgan, and V. Cristea Satellite image processing applications in MedioGRID Proceedings of the 5th International Symposium on Parallel and Distributed Computing IEEE Computer Press 2006 253 260 (Pubitemid 47179300)
22. D. Gorgan, V. Bacu, D. Rodila, F. Pop, and D. Petcu Experiments on ESIP - environment oriented satellite data processing platform Journal of Earth Science Informatics 2010 297 308