ICA-based fusion for colour display of hyperspectral images

References

• Cai , S. , Du , Q. and Moorhead , R.J. 2007 . Hyperspectral imagery visualization using double layers . IEEE Transactions on Geoscience and Remote Sensing , 45 : 3028 – 3036 .
   Google Scholar
• Canny , J. 1986 . A computational approach to edge detection . IEEE Transactions on Pattern Analysis and Machine Intelligence , PAMI-8 : 679 – 698 .
   Web of Science ®Google Scholar
• Chang , C.-I. 2003 . Hyperspectral Imaging: Techniques for Spectral Detection and Classification , Berlin : Springer .
   Google Scholar
• Chen , H. and Varshney , P.K. 2007 . A human perception inspired quality metric for image fusion based on regional information . Information Fusion , 8 : 193 – 207 .
   Google Scholar
• Chiang , S.-S. , Chang , C.-I. and Ginsberg , I.W. 2000 . Unsupervised hyperspectral image analysis using independent component analysis . Proceedings of International Geoscience and Remote Sensing Symposium , 7 : 3136 – 3138 .
   Google Scholar
• Comon , P. 1994 . Independent component analysis, a new concept? . Signal Processing , 36 : 287 – 314 .
   Web of Science ®Google Scholar
• Demir , B. , Gelebi , A. and Ertuik , S. 2009 . A low-complexity approach for color display of hyperspectral remote-sensing images using one-bit transform based band selection . IEEE Transactions on Geoscience and Remote Sensing , 47 : 97 – 105 .
   Google Scholar
• Du , H. , Qi , H. , Wang , X. , Ramanath , R. and Snyder , W.E. 2003 . “ Band selection using independent component analysis for hyperspectral image processing ” . In Proceedings of Applied Imagery Pattern Recognition Workshop , 93 – 98 . Piscataway, NJ : IEEE .
   Google Scholar
• Gävert, H., Hurri, J., Särelä, J. and Hyvärinen, A., 2005, FastICA. Available online at http://www.cis.hut.fi/projects/ica/fastica/ (http://www.cis.hut.fi/projects/ica/fastica/)
   Google Scholar
• Healey , C.G. , Booth , K.S. and Enns , J.T. 1995 . Visualizing real-time multivariate data using preattentive processing . ACM Transactions on Modeling and Computer Simulation , 5 : 190 – 221 .
   Google Scholar
• Guo , B. , Gunn , S.R. , Damper , R.I. and Nelson , J.D.B. 2006 . Band selection for hyperspectral image classification using mutual information . IEEE Transactions on Geoscience and Remote Sensing Letters , 3 : 522 – 526 .
   Google Scholar
• Huang , R. and He , M. 2005 . Band selection based on feature weighting for classification of hyperspectral data . IEEE Transactions on Geoscience and Remote Sensing Letters , 2 : 156 – 159 .
   Web of Science ®Google Scholar
• Hyvärinen , A. 1999 . Survey on independent component analysis . Neural Computing Surveys , 2 : 94 – 128 .
   Google Scholar
• Hyvärinen , A. and Oja , E. 2000 . Independent component analysis: algorithms and applications . Neural Networks , 13 : 411 – 430 .
   PubMed Web of Science ®Google Scholar
• Jacobson , N.P. and Gupta , M.R. 2005 . Design goals and solutions for display of hyperspectral images . IEEE Transactions on Geoscience and Remote Sensing , 43 : 2684 – 2692 .
   Google Scholar
• Jacobson , N.P. and Gupta , M.R. SNR-adaptive linear fusion of hyperspectral images for color display . IEEE International Conference of Image Processing . 2007 , San Antonio, TX. September . pp. 477 – 480 . Piscataway, NJ : IEEE .
   Google Scholar
• Jacobson , N.P. , Gupta , M.R. and Cole , J.B. 2007 . Linear fusion of image sets for display . IEEE Transactions on Geoscience and Remote Sensing , 45 : 3277 – 3288 .
   Google Scholar
• Jensen , A.C. , Berge , A. and Solberg , A.S. 2008 . Regression approaches to small sample inverse covariance matrix estimation for hyperspectral image classification . IEEE Transactions on Geoscience and Remote Sensing , 46 : 2814 – 2822 .
   Google Scholar
• Jia , X. and Richard , J.A. 1999 . Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification . IEEE Transactions on Geoscience and Remote Sensing , 37 : 538 – 542 .
   Web of Science ®Google Scholar
• Karoui , M.S. , Deville , Y. , Hosseini , S. , Quamri , A. and Ducrot , D. Improvement of remote sensing multispectral image classification by using independent component analysis . First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing . August 26–28 2009 , Grenoble, France. pp. 1 – 4 . Piscataway, NJ : IEEE .
   Google Scholar
• Lennon , M. , Mercier , G. , Mouchot , M.C. and Hubert-Moy , L. 2001 . Independent component analysis as a tool for dimensionality reduction and the representation of hyperspectral images . International Geoscience and Remote Sensing Symposium , 6 : 2893 – 2895 .
   Google Scholar
• Piella , G. and Heijmans , H. A new quality metric for image fusion . Proceedings of IEEE International Conference of Image Processing . 2003 , Barcelona, Spain. September . pp. 173 – 176 . Piscataway, NJ : IEEE .
   Google Scholar
• Phillips , R.D. , Blinn , C.E. , Watson , L.T. and Wynne , R.H. An adaptive noise reduction technique for improving the utility of hyperspectral data . Proceedings of the 17th William T. Pecora Memorial Remote Sensing Symposium . November 16–20 2008 , Denver, CO. Bethesda, MD : American Society for Photogrammetry and Remote Sensing .
   Google Scholar
• Prieto , M.S. and Allen , A.R. 2003 . A similarity metric for edge images . IEEE Transactions on Pattern Analysis and Machine Intelligence , 25 : 1265 – 1273 .
   Google Scholar
• Polder , G. and Van Der Heijden , G.W.A.M . 2001 . Visualization of spectral images . Proceedings of SPIE , 4553 : 132 – 137 .
   Google Scholar
• Roberts , S. and Everson , R. 2001 . Independent Component Analysis: Principles and Practice , Cambridge : Cambridge University Press .
   Google Scholar
• Robertson , P.K. and O'Callaghan , J.F. 1988 . The application of perceptual color spaces to the display of remotely sensed imagery . IEEE Transactions on Geoscience and Remote Sensing , 26 : 49 – 59 .
   Web of Science ®Google Scholar
• Robila , S.A. and Varshney , P.K. 2002 . Target detection in hyperspectral images based on independent component analysis . Proceeding of SPIE Automatic Target Recognition XII , 4726
   Google Scholar
• Robila , S.A. and Varshney , P.K. 2003 . Further results in the use of independent component analysis for target detection in hyperspectral images . Proceedings of SPIE Automatic Target Recognition XIII , 5094
   Google Scholar
• Robila , S.A. and Varshney , P.K. 2004 . “ Feature extraction from hyperspectral data using ICA ” . In Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data , Edited by: Varshney , P.K. and Arora , M.K. 199 – 216 . Berlin : Springer .
   Google Scholar
• Rummel , R.J. 1976 . Understanding Correlation , Hawaii : University of Hawaii .
   Google Scholar
• Shah , C.A. , Arora , M.K. and Varshney , P. K . 2004 . Unsupervised classification of hyperspectral data: an ICA mixture model based approach . International Journal of Remote Sensing , 25 : 481 – 487 .
   Web of Science ®Google Scholar
• Shah , C.A. , Varshney , P. K and Arora , M.K. 2007 . ICA mixture model algorithm for unsupervised classification of remote sensing imagery . International Journal of Remote Sensing , 28 : 1711 – 1731 .
   Web of Science ®Google Scholar
• Sheikh , H.R. and Bovik , A.C. 2006 . Image information and visual quality . IEEE Transactions on Image Processing , 15 : 430 – 444 .
   PubMed Web of Science ®Google Scholar
• Stone , J.V. 2003 . Independent Component Analysis: a Tutorial Introduction , Cambridge, MA : MIT Press .
   Google Scholar
• Toet , A. and Lucassen , M.P. 2003 . A universal color image quality metric . Proceedings of SPIE Visual Information Process, XII , 5108 : 13 – 23 .
   Google Scholar
• Tsagaris , V. , Anastassopoulos , V. and Lampropoulos , G.A. 2005 . Fusion of hyperspectral data using segmented PCT for color representation and classification . IEEE Transactions on Geoscience and Remote Sensing , 26 : 2365 – 2375 .
   Google Scholar
• Tyo , J.S. , Konsolakis , A. , Diersen , D.I. and Olsen , R.C. 2003 . Principal-components-based display strategy for spectral imagery . IEEE Transactions on Geoscience and Remote Sensing , 41 : 708 – 718 .
   Web of Science ®Google Scholar
• Varshney , P.K. and Arora , M.K. 2004 . Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data , Berlin : Springer .
   Google Scholar
• Wang , J. and Chang , C.-I. 2006a . Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis . IEEE Transactions on Geoscience and Remote Sensing , 44 : 1586 – 1600 .
   Web of Science ®Google Scholar
• Wang , J. and Chang , C.-I. 2006b . Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery . IEEE Transactions on Geoscience and Remote Sensing , 44 : 2601 – 2616 .
   Web of Science ®Google Scholar
• Wang , Z. and Bovik , A.C. 2002 . A universal image quality index . IEEE Signal Processing Letter , 9 : 81 – 84 .
   Web of Science ®Google Scholar
• Wang , Q. , Shen , Y. , Zhang , Y. and Zhang , J. 2004a . Fast quantitative correlation analysis and information deviation analysis for evaluating the performances of image fusion techniques . IEEE Transactions on Instrumentation and Measurement , 53 : 1441 – 1447 .
   Google Scholar
• Wang , Z. , Bovik , A.C. , Sheikh , H.R. and Simoncelli , E.P. 2004b . Image quality assessment: from error visibility to structural similarity . IEEE Transactions on Image Processing , 13 : 600 – 612 .
   PubMed Web of Science ®Google Scholar
• Wilson , T.A. , Rogers , S.K. and Kabrisky , M. 1997 . Perceptual-based image fusion for hyperspectral data . IEEE Transactions on Geoscience and Remote Sensing , 35 : 1007 – 1017 .
   Web of Science ®Google Scholar
• Zampolo , R. and Seara , R. A comparison of image quality metric performances under practical condition . Proceedings of IEEE International Conference of Image Processing . 2005 , Genoa, Italy. September . pp. 1192 – 1195 . Piscataway, NJ : IEEE .
   Google Scholar
• Zhang , H. , Messinger , D. and Montag , E. Perceptual display strategies of hyperspectral imagery based on PCA and ICA . Proceedings of SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII . May 2006 , Orlando, FL. Bellingham, WA : International Society for Optical Engineering .
   Google Scholar
• Zhu , Y. , Varshney , P.K. and Chen , H. Evaluation of ICA based fusion of hyperspectral images for color display . IEEE International Conference of Information Fusion . 2007 , Quebec, Canada. July . Piscataway, NJ : IEEE .
   Google Scholar
• Zhu , Y. , Varshney , P.K. and Chen , H. Dimensionality reduction of hyperspectral images for colour display using segmented independent component analysis . IEEE International Conference of Image Processing . September 2007 , San Antonio, TX. pp. 97 – 100 . Piscataway, NJ : IEEE .
   Google Scholar