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Remote Sensing : Understanding Hyperspectral Imaging

Remote Sensing : Understanding Hyperspectral Imaging. Christian Sánchez López Métodos de Investigación Bibliográfica Prof. Liz M. Págan. Hyperspectral images are constructed by sampling multiple spectral bands for each pixel or discrete spatial sampling location.

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Remote Sensing : Understanding Hyperspectral Imaging

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  1. Remote Sensing : Understanding Hyperspectral Imaging Christian SánchezLópez Métodos de InvestigaciónBibliográfica Prof. Liz M. Págan

  2. Hyperspectral images are constructed by sampling multiple spectral bands for each pixel or discrete spatial sampling location. Produces a set of images, each acquired over a relatively narrow electromagnetic bandwidth. Images contain large amounts of data. What is Hyperspectral Imaging (HSI) ?

  3. Technique • Collect image data simultaneously • Dozens or hundreds of narrow, adjacent spectral bands • Purpose • Obtain a complete reflectance spectrum for the region being analyzed • Image pixel • Spectral information over the hundreds of bands to generate a "data cube" Hyperspectral Imaging (HSI)

  4. Sampling the Spectrum

  5. Hyperspectral Imaging, also referred to as Imaging Spectrometry, combines: • conventional imaging, • spectroscopy, and • radiometry to produce images for which a spectral signature is associated with each spatial resolution element (pixel) Hyperspectral Imaging (HSI) [Picture taken from: http://www2.brgm.fr/mineo]

  6. Hyperspectral Imaging (HSI) • Hyperspectral sensors collect data to produce “data cubes”. These consist of the two spatial dimensions and a large spectral dimension. Data Cube [1]

  7. Hyperspectral Imaging (HSI) Conventional Image : Hyperspectral Image :

  8. In order to gather the necessary information about Hyperspectral Imaging we used the following tools: • Database searches – most of the articles where found using this tool. • Internet Portal searches – provide ways to search for books, newspaper articles and websites on the specific topic. • Research on published papers and thesis– peer reveiwed papers provide credible sources of information. They are a good way to get up to speed quickly and efficently on the topic at hand. Research Process

  9. Research Process

  10. The process of finding information relating to these specific topic was not very difficult. The world wide web provides means to find information on almost anything we need. Database searches provide excellent results with proven resources including thesis, published papers and peer-reviewed articles. These is just an example of how the research process has moved from just going into a Library and searching for books and materials on a specific topic. Difficulties confronted in this research was gaining the initial knowledge on resources; what are they and how to differentiate between good and bad resources. Conclusion

  11. El-Sheimy, Valeo, Habib. (2005). Digital terrain modeling: acquisition, manipulation, and applications. Norwood, MA : Artech House, Inc.  Goetz, A., Vane, G., Solomon, J., Rock, B. (1985, June 7). Imaging spectrometry for earth remote sensing. Science, p1147(7). Gonzalez, D., Sanchez, C., Veguilla, R., Santiago, N., Rosario-Torres, S; Velez-Reyes, M. (2008). Abundance estimation algorithms using NVIDIA [registered trademark] CUDA [trademark] technology. [Electronic Version]. Proceedings of SPIE - The International Society for Optical Engineering, v 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imager, 7, 2008, p 69661E. Masalmah, Y.M. Velez-Reyes, M., Rosario-Torres, S. (2005). An algorithm for unsupervised unmixing of hyperspectral imagery using positive matrix factorization[Electronic Version]. Proceedings of the SPIE - The International Society for Optical Engineering, v 5806, n 1, p 703-10. Morales-Morales, J.(2007). An FPGA implementation of the image space reconstruction algorithm for hyperspectral imaging analysis. Master thesis, Electrical and Computer Engineering Department, University of Puerto Rico, Mayaguez Campus. References [1] Rosario-Torres, Samuel, Velez-Reyes, Miguel, An algorithm for fully constrained abundance estimation in hyperspectralunmixing, Proceedings of SPIE - The International Society for Optical Engineering, v 5806, n PART II, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, 2005, p 711-719 [2] Javier Morales, Nayda G. Santiago, and Alejandro Fernández, An FPGA Implementation of Image Space Reconstruction Algorithm forHyperspectral Imaging Analysis, Proceedings of the SPIE, Vol. 6565 65651V (2007), Algorithms and Technologies for Multispectral,Hyperspectral, and Ultraspectral Imagery XIII, editors: Sylvia S. Shenand Paul E. Lewis, pp, V-1 to V-12. [3] http://www.nvidia.com/object/cuda [4]http://developer.download.nvidia.com/compute/cuda/0_8/NVIDIA_CUDA_ Programming Guide_0.8.pdf [5]http://www3.stat.sinica.edu.tw/statistica/password.asp?vol=5&num=1&art =5 –Introduction to the Iterative Image Space Restoration Algorithm [6] J. D. Owens, D. Luebke, N. Govindaraju, M. Harris, J. Krüger, A. E. Lefohn, T. J. Purcell ,A Survey of General-Purpose Computation on Graphics Hardware, In Proceedings in Eurographics 2005, Aug. 2005, Dublin, Ireland, Pages 21 – 51. [7] David González, Christian Sánchez, Ricardo Veguilla, Nayda Santiago, Samuel Rosario, and Miguel Vélez, An algorithm for fully constrained abundance estimation in hyperspectralunmixing, Proceedings of SPIE - The International Society for Optical Engineering , v6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 2008. [1] Rosario-Torres, Samuel, Velez-Reyes, Miguel, An algorithm for fully constrained abundance estimation in hyperspectralunmixing, Proceedings of SPIE - The International Society for Optical Engineering, v 5806, n PART II, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, 2005, p 711-719 [2] Javier Morales, Nayda G. Santiago, and Alejandro Fernández, An FPGA Implementation of Image Space Reconstruction Algorithm forHyperspectral Imaging Analysis, Proceedings of the SPIE, Vol. 6565 65651V (2007), Algorithms and Technologies for Multispectral,Hyperspectral, and Ultraspectral Imagery XIII, editors: Sylvia S. Shenand Paul E. Lewis, pp, V-1 to V-12. [3] http://www.nvidia.com/object/cuda [4]http://developer.download.nvidia.com/compute/cuda/0_8/NVIDIA_CUDA_ Programming Guide_0.8.pdf [5]http://www3.stat.sinica.edu.tw/statistica/password.asp?vol=5&num=1&art =5 –Introduction to the Iterative Image Space Restoration Algorithm [6] J. D. Owens, D. Luebke, N. Govindaraju, M. Harris, J. Krüger, A. E. Lefohn, T. J. Purcell ,A Survey of General-Purpose Computation on Graphics Hardware, In Proceedings in Eurographics 2005, Aug. 2005, Dublin, Ireland, Pages 21 – 51. [7] David González, Christian Sánchez, Ricardo Veguilla, Nayda Santiago, Samuel Rosario, and Miguel Vélez, An algorithm for fully constrained abundance estimation in hyperspectralunmixing, Proceedings of SPIE - The International Society for Optical Engineering , v6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 2008.

  12. Niemann, H. B., Atreya, S. K.,  Bauer, S. J.,  Carignan, G. R.,  Demick, J. E. ,  Frost R. L.,  Gautier, D.,  Haberman, J. A.,  Harpold, D. N.,  Hunten, D. M.,  Israel, G.,  Lunine, J. I.,  Plaza, Chang. (2008). High performance computing in remote sensing. Boca Raton, Florida: CRC Press.  Rosario Torres, S. (2004). Iterative algorithms for abundance estimation on unmixing of hyperspectral imagery. Master thesis, Electrical and Computer Engineering Department, University of Puerto Rico, Mayaguez Campus. Rosario-Torres, Velez-Reyes.(2005). An algorithm for fully constrained abundance estimation in hyperspectralunmixing[Electronic Version]. Proceedings of SPIE - The International Society for Optical Engineering, v 5806, n PART II, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery, 6, 711-719. Schowengerdt. (2007). Remote sensing. Burlington, MA: Academic Press. References [1] Rosario-Torres, Samuel, Velez-Reyes, Miguel, An algorithm for fully constrained abundance estimation in hyperspectralunmixing, Proceedings of SPIE - The International Society for Optical Engineering, v 5806, n PART II, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, 2005, p 711-719 [2] Javier Morales, Nayda G. Santiago, and Alejandro Fernández, An FPGA Implementation of Image Space Reconstruction Algorithm forHyperspectral Imaging Analysis, Proceedings of the SPIE, Vol. 6565 65651V (2007), Algorithms and Technologies for Multispectral,Hyperspectral, and Ultraspectral Imagery XIII, editors: Sylvia S. Shenand Paul E. Lewis, pp, V-1 to V-12. [3] http://www.nvidia.com/object/cuda [4]http://developer.download.nvidia.com/compute/cuda/0_8/NVIDIA_CUDA_ Programming Guide_0.8.pdf [5]http://www3.stat.sinica.edu.tw/statistica/password.asp?vol=5&num=1&art =5 –Introduction to the Iterative Image Space Restoration Algorithm [6] J. D. Owens, D. Luebke, N. Govindaraju, M. Harris, J. Krüger, A. E. Lefohn, T. J. Purcell ,A Survey of General-Purpose Computation on Graphics Hardware, In Proceedings in Eurographics 2005, Aug. 2005, Dublin, Ireland, Pages 21 – 51. [7] David González, Christian Sánchez, Ricardo Veguilla, Nayda Santiago, Samuel Rosario, and Miguel Vélez, An algorithm for fully constrained abundance estimation in hyperspectralunmixing, Proceedings of SPIE - The International Society for Optical Engineering , v6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 2008. [1] Rosario-Torres, Samuel, Velez-Reyes, Miguel, An algorithm for fully constrained abundance estimation in hyperspectralunmixing, Proceedings of SPIE - The International Society for Optical Engineering, v 5806, n PART II, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, 2005, p 711-719 [2] Javier Morales, Nayda G. Santiago, and Alejandro Fernández, An FPGA Implementation of Image Space Reconstruction Algorithm forHyperspectral Imaging Analysis, Proceedings of the SPIE, Vol. 6565 65651V (2007), Algorithms and Technologies for Multispectral,Hyperspectral, and Ultraspectral Imagery XIII, editors: Sylvia S. Shenand Paul E. Lewis, pp, V-1 to V-12. [3] http://www.nvidia.com/object/cuda [4]http://developer.download.nvidia.com/compute/cuda/0_8/NVIDIA_CUDA_ Programming Guide_0.8.pdf [5]http://www3.stat.sinica.edu.tw/statistica/password.asp?vol=5&num=1&art =5 –Introduction to the Iterative Image Space Restoration Algorithm [6] J. D. Owens, D. Luebke, N. Govindaraju, M. Harris, J. Krüger, A. E. Lefohn, T. J. Purcell ,A Survey of General-Purpose Computation on Graphics Hardware, In Proceedings in Eurographics 2005, Aug. 2005, Dublin, Ireland, Pages 21 – 51. [7] David González, Christian Sánchez, Ricardo Veguilla, Nayda Santiago, Samuel Rosario, and Miguel Vélez, An algorithm for fully constrained abundance estimation in hyperspectralunmixing, Proceedings of SPIE - The International Society for Optical Engineering , v6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 2008.

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