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DRPS : Course Catalogue : School of Geosciences : Postgraduate Courses (School of GeoSciences)

Postgraduate Course: Hyperspectral Remote Sensing (PGGE11040)

Course Outline
SchoolSchool of Geosciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe course aims to provide an introduction to hyperspectral remote sensing methods, systems for the collection of data at high spectral resolution and unique approaches and algorithms to the processing of such data. The case is made for the greater use of hyperspectral reflectance data. Taking a bottom-up approach the course will first visit spectral signatures and their collection at the Earth=s surface using instruments and techniques of field spectroscopy, and hyperspectral imaging instruments. Practical exercises will be undertaken in support of these techniques.
Course description Monday 16th January
Lecture: 1. Introduction to course, and the case for hyperspectral Earth observations followed by an introduction to course practicals/assessments
Tutorial: 1. Set seminar assessments (one practical assignment and one presentation)

Monday 23rd January
Lecture: 2. An introduction to near-ground hyperspectral measurements (field spectroscopy);
Lecture: 3. The analysis of field spectroscopy data and validation of hyperspectral Earth observations
Tutorial: 2. The analysis of hyperspectral image data cubes. Work on Practical Assignment

Monday 30th January
Lecture: 4. Introduction to hyperspectral imaging
Lecture: 5. Applications of hyperspectral remote sensing
Tutorial: 3. The analysis of hyperspectral image data cubes continued. Work on Practical Assignment

Monday 6th February
Tutorial: 4. The analysis of hyperspectral image data cubes continued and continue work on Practical Assignment

Monday 13th February
Student presentations session
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2016/17, Available to all students (SV1) Quota:  None
Course Start Block 3 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 24, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 74 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Practical assessment Submit Practical Assignment Report outline by 23rd January. Feedback from AM by 26th January
17th February Submit final Practical Assignment Report for assessment 10 for Outline plus 50 for final Assignment Report
Seminar assessment Submit 2 candidate papers by 23th January for selection by AM. Feedback from AM by 25th January)
Presentation outline to be submitted to AM by 30th January. Feedback from AM by 2nd February)
13th February - student seminar presentations 10 (paper selection and outline) plus 30 (for presentation)
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. know the basic principles of field spectroscopy and techniques for the collection and analysis of hyperspectral data, identify the most important attributes for airborne and hyperspectral sensors, evaluate their characteristics and potential performance, identify why calibration is critical and knowledge of approaches taken for the atmospheric correction of hyperspectral data
  2. appreciate how data extraction techniques and hyperspectral algorithms work
  3. locate, read and summarise relevant literature, from both traditional and electronic media, to extend your understanding of the topic. Get proficient in presenting scientific material
  4. develop reasoned arguments, firmly grounded in the available literature and plan and write assignments, within the specified parameters and to a professional standard
  5. take responsibility for your own learning through reading and the preparation of assignments, delivery of a seminar and reflect upon your learning experience
Reading List
Locate a tutorial online on Report Writing e.g « » but there are many others.

Selected References (Note Key texts in bold).
General hyperspectral texts
Asrar, G. (ed.), (1989) Theory and Applications of Optical Remote Sensing, Chapter 10, John Wiley and Sons, New York, NY, pp. 429-472
Curran, P.J. (1994). Imaging spectrometry. Progress in Physical Geography, 18:247-266.
Goetz, A.F.H., Curtiss, B. (1996). Hyperspectral imaging of the earth: remote analytical chemistry in an uncontrolled environment. Field Analytical Chemistry and Technology, 1:67-76.
Chang, Ch-I., (2003). Hyperspectral imaging: Techniques for spectral detection and classification. Kluwer Academic. New York. 370pp.
Schaepman-Strub, G., Schaepman, M.E., Painter, T.H., Dangel, S. and Martonchik, J.V. (2006). Reflectance quantities in optical remote sensing-definitions and case studies. Remote Sensing of Environment, 103, 27¿42.
Liang, S. (2004). Quantitative remote sensing of land surfaces. Wiley. New Jersey.
Lillesand , Kiefer and Chipman (2008) Remote Sensing and Image Interpretation. Chapter 5, John Wiley and Sons, New York, NY. Pp. 325-391
Van der Meer, F.D., de Jong, S.M. (2001). Imaging spectroscopy; Basic principles and prospective applications. Kluwer. 403pp.

Field spectroscopy:
Deering, D.W., 1989, Field measurements of bidirectional reflectance. In: Theory and Applications of Optical Remote Sensing, Asrar, G., ed. John Wiley & Sons Inc., New York, 14-65.
Duggin, M. J. (1980). The field measurement of reflectance factors. Photogrammetric Engineering and Remote Sensing 16: 643-7.
Duggin, M.J. and Philipson, W.R., 1982. Field measurement of reflectance: some major considerations. Applied Optics, 21, 2833-2840.
Jackson, R. D., M. S. Moran, et al. (1987). Field calibration of reference reflectance panels. Remote Sensing of Environment 22: 145-58.
Milton, E.J., 1987. Principles of field spectroscopy. International Journal of Remote Sensing, 8, 1807-1827.
Milton, E.J., Rollin, E.M. and Emery, D.R., 1995. Advances in field spectroscopy. In: Danson, F.M. and Plummer, S.E., ed. Advances in Environmental Remote Sensing, John Wiley & Sons Ltd, Chichester, 9-32.
Milton, E.J., Schaepman, M., Anderson, K., Kneubuhler, M. and Fox, N. (2009). Progress in field spectroscopy. Remote Sensing of Environment, 113, S92¿S109.
Peddle D.R., White H.P., Soffer R.J., Miller J.R., LeDrew E.F., 2001. Reflectance processing of remote sensing spectroradiometer data. Computers & Geosciences, 27:203-213.
Richardson, A. J. (1981). Measurement of reflectance factors under daily and intermittent irradiance variations. Applied Optics 20(19): 3336-3340.
Robinson, F. B. and L. L. Behl (1979). Calibration procedures for measurements of reflectance factors in remote sensing field research. Society of Photo-Optical Instrumentation Engineering 196: 16-26.

Field spectroscopy ¿ applications
Malthus, T.J., Madeira, A.C. (1993). High resolution spectroradiometry: spectral response of bean leaves infected by Botrytis fabae. Remote Sensing of Environment, 45:107-116.
Malthus, T.J., Andrieu, B., Danson, F.M., Jaggard, K.W., Steven, M.D. (1993). Candidate high spectral resolution infrared indices for the prediction of crop cover. Remote Sensing of Environment, 46:204-212.
Malthus, T.J., Dekker, A.G. (1995). First derivative indices for the remote sensing of inland water quality using high spectral resolution reflectance. Environment International, 23:221-232.
Mac Arthur A. and Malthus T. (2012) Calluna vulgaris foliar pigments and spectral reflectance modelling, International Journal of Remote Sensing, 33:16, 5214-5239
Shaw, D.T., Malthus, T.J., Kupiec, J.A. (1998). High spectral resolution data for monitoring Scots pine (Pinus sylvestris L.) regeneration. International Journal of Remote Sensing, 19:2601-2608.
Karpouzli, E., Malthus, T. (2003). The empirical line method for the atmospheric correction of IKONOS imagery. International Journal of Remote Sensing, 24(5):1143-1150.
Karpouzli, E., Malthus, T.J., Place, C.J. (2004). Hyperspectral discrimination of coral reef benthic communities in western Caribbean. Coral Reefs, 23:141-151.
Shaw, D. T., T. J. Malthus, et al. (1998). High-spectral resolution data for monitoring Scots pine (Pinus sylvestris L.) regeneration. International Journal of Remote Sensing 19(13): 2601-2608.
Steven, M.D, Malthus, T.J., Baret, F., Xu, H., Chopping, M.J. (2003). Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment, 88(4):412-422.

Analytical techniques
Demetriades-Shah, T. H., M. D. Steven, et al. (1990). High resolution derivative spectra in remote sensing. Remote Sensing of Environment 33: 55-64.
Malthus, T.J., Andrieu, B., Baret, F., Clark, J.A., Danson, F.M., Jaggard, K.W., Madeira, A.C., Steven, M.D. (1991) Candidate high spectral resolution derivative indices for the prediction of crop cover. Proceedings of the 5th International Colloquium on Physical Measurement and Signatures in Remote Sensing, Courchevel, France, 14 - 18 January, 1991 (ESA SP-319), pp. 205-208.
Malthus, T.J., Dekker, A.G. (1995). First derivative indices for the remote sensing of inland water quality using high spectral resolution reflectance. Environment International, 23:221-232.
Tsai, F., Philpot, W. (1998). Derivative analysis of Hyperspectral data. Remote Sensing of Environment, 66:41-51.

Spectral mixture modelling:
Asner, G. P. and K. B. Heidebrecht (2002). Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations. International Journal of Remote Sensing 23(19): 3939-3958.
Borel, C.C., Gerstl, S.A.W. (1994). Nonlinear spectral mixing models for vegetative and soil surfaces. Remote Sensing of Environment, 47:403-416.
Foody, G.M. (1997). Non ¿linear mixture modelling without end-members using an artificical neural network. International Journal of Remote Sensing, 18:659-671.
Gillespie, A.R. (1992). Spectral mixture analysis of multispectral thermal infrared images. Remote Sensing of Environment, 42:137-145.
Hochberg, E. J. and M. J. Atkinson (2003). Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra. Remote Sensing of Environment 85(2): 174-189.
Nadeau, C., R. A. Neville, K. Staenz, N. T. O'Neill and A. Royer (2002). Atmospheric effects on the classification of surface minerals in an arid region using Short-Wave Infrared (SWIR) hyperspectral imagery and a spectral unmixing technique. Canadian Journal of Remote Sensing 28(6): 738-749.
Settle, JJ., Drake, N.A. (1993). Linear mixing and the estimation of ground cover proportions. International Journal of Remote Sensing:14:1159-1171.

Spectral angle mapping
Kruse, F. A., Lefkoff, A. B., Boardman, J. W., et al. (1993), The interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44:145¿163.
Crosta, A.P. Sabine, C., Taranik, J.V (1998). Hydrothermal Alteration Mapping at Bodie, California, Using AVIRIS Hyperspectral Data, Remote Sensing of Environment, 65:309¿319.

Feature space modelling approaches:
Hoffbeck, J.P., Landgrebe, D.A. (1996). Classification of remote sensing images having high spectral resolution. Remote Sensing of Environment, 57:119-126.
Landgrebe, D. (1998). Information extraction principles and methods for multispectral and hyperspectral image data. 30pp. Available on-line.
Landgrebe, D. (1999). On information extraction principles for hyperspectral data. 14pp. Available online.

Atmospheric correction
Berk, A. Bernstein, L.S., Anderson, G.P. et al. (1998). MODTRAN Cloud and multiple Scattering Upgrades with Application to AVIRIS. RSE, 65:367¿375
Clark, R. N., Gallagher, A. J., and Swayze, G. A. (1990), Material absorption band depth mapping of imaging spectrometer data using a complete band shape least-square fit with library reference spectra. In Proc. Second Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, 4¿5 June, JPL Publ. 90-54, Jet Propulsion Laboratory, Pasadena, CA, pp. 176¿186.
Kruse, F.A. (2004). Comparison of ATREM, ACORN, and FLAASH atmospheric corrections using low-altitude AVIRIS data of Boulder, CO.
Rodger, A. and M. J. Lynch (2001). Determining atmospheric column water vapour in the 0.4-2.5¿m spectral region. Proceedings of the Tenth JPL Airborne Earth Science Workshop, Pasadena, California.

Spectral smoothing
Savitzky, A. and M. J. E. Golay (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36:1627-1639.
Schmidt, K. S. and Skidmore, A. K., 2002. Smoothing vegetation spectra with wavelets. International Journal of Remote Sensing.

Aspinall, R. J., W. A. Marcus and J. W. Boardman (2002). Considerations in collecting, processing, and analysing high spatial resolution hyperspectral data for environmental investigations. Journal of Geographical Systems 4: 15-29.
Jacquemoud, S., C. Bacour, H. Poilve and J. P. Frangi (2000). Comparison of four radiative transfer models to simulate plant canopies reflectance-direct and inverse mode. Remote Sensing of Environment 74: 741-781.
Kruse, F. A., J. W. Boardman and J. F. Huntington (2003). Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping. Ieee Transactions on Geoscience and Remote Sensing 41(6): 1388-1400.
Peterson, D.L., J.D. Aber, P.A. Matson, D.H. Card, N.A. Swanberg, C.A. Wessman and M.A. Spanner, 1988, Remote sensing of forest canopy and leaf biochemical contents, Remote Sensing of Environment 24:85-108, 1988.
Peterson, D.L. and S.W. Running, 1989, Applications to Forest Science and Management.. In: Asrar, G. (ed.), Theory and Applications of Optical Remote Sensing, Chapter 10, John Wiley and Sons, New York, NY, pp. 429-472.
Schmidt, K. S. and A. K. Skidmore (2003). Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment 85(1): 92-108.
Treitz, P. M. and P. J. Howarth (1999). Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems. Progress in Physical Geography 23(3): 359-390.
Ustin, S.L., Roberts, D.A., Gamon, J.A., Asner, G.P., Green, R.O. (2004). Using Imaging spectroscopy to study ecosystem processes and properties. BioScience, 54 523-534
Ustin, S. L., P. J. Zarco-Tejada and G. P. Asner (2001). The role of hyperspectral data in understanding the global carbon cycle. Proceedings of the Tenth JPL Airborne Earth Science Workshop, Pasadena, California, JPL.
van der Meer, F., de Jong, S. (). Spectral mapping methods: many problems, some solutions. Proceedings of 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, 13-16 May 2003.
van der Meer, F., (2000). Spectral curve shape matching with a continuum removed CCSM algorithm. International Journal of Remote Sensing, 21:3179¿3185.

Pearlman, J., Carman, S., Lee, P., Liao, L. Segal, C. (1999). Hyperion imaging spectrometer on the new millennium program Earth Orbiter-1 system. Report No. Hyp_00-600.002. Space and Electronics Group, 1 Science Park, Redondo Beach CA 90278. (available online).
Green, R.O., Eastwood, M.L., Sarture, C.M. Chrien, T.G., Aronsson, M. Chippendale, B. Faust, J.A. Pavri, B.E. Chovit, C.J. Solis, M., Olah, M.R., Williams, O. (1998). Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sensing of Environment. 65:227¿248
Sellar, R.G., Boreman, G.D. (2005). Classification of imaging spectrometers for remote sensing applications. Optical Engineering, 44: 013602,1-3
Additional Information
Course URL
Graduate Attributes and Skills Not entered
Additional Class Delivery Information G10 Drummond Library
KeywordsPGGE11040 Hyperspectral,field spectroscopy,remote sensing
Course organiserDr Alasdair Macarthur
Tel: (0131 6)50 5926
Course secretaryMrs Karolina Galera
Tel: (0131 6)50 2572
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