Remote Sensing : Understanding Hyperspectral Imaging

1
Remote Sensing Understanding Hyperspectral
Imaging

• Christian Sánchez López
• Métodos de Investigación Bibliográfica
• Prof. Liz M. Págan

2
What is Hyperspectral Imaging (HSI) ?

• 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.

3
Hyperspectral Imaging (HSI)

• 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"

4
Sampling the Spectrum
5
Hyperspectral Imaging (HSI)

• 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)

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
Research Process

• 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.

9
Research Process
10
Conclusion

• 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.

11
References

• 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 factorizationElectronic 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.

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 4http//develo
per.download.nvidia.com/compute/cuda/0_8/NVIDIA_CU
DA_ Programming Guide_0.8.pdf 5http//www3.stat.
sinica.edu.tw/statistica/password.asp?vol5num1
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 4http//develo
per.download.nvidia.com/compute/cuda/0_8/NVIDIA_CU
DA_ Programming Guide_0.8.pdf 5http//www3.stat.
sinica.edu.tw/statistica/password.asp?vol5num1
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
References

• 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
  hyperspectral unmixingElectronic 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.

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 4http//develo
per.download.nvidia.com/compute/cuda/0_8/NVIDIA_CU
DA_ Programming Guide_0.8.pdf 5http//www3.stat.
sinica.edu.tw/statistica/password.asp?vol5num1
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 4http//develo
per.download.nvidia.com/compute/cuda/0_8/NVIDIA_CU
DA_ Programming Guide_0.8.pdf 5http//www3.stat.
sinica.edu.tw/statistica/password.asp?vol5num1
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.