Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [THP-1 HIGH SPECTRAL RESOLUTION MEASUREMENT]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: The Ramifications of Sampling Methods for Imaging Spectrometers. J. Douglas Dunlop & John R. Miller

Document type:: Multivolume work

Structure type:: Chapter

578 spatial frequencies are blocked then the result is the cross shaped mask shown in figure 6(c). The inverse transform is shown in figure 6(0 with the difference image in figure 6(i). On the whole the differences between the original and the inverse transform are extremely small. The differences lie mostly along a single row. In the original image that row is a an atmospheric absorption feature which implies a high frequency component to the spectral direction of the signal. The small scale variations in the signal along that row imply high frequency in the spatial domain also. Thus the differ ences are the result of blocking the high-spectral/high-spatial frequencies, exactly as expected. Figures 7(a)-(i) show the exact same sequence of transforma tions using a 10 element wide Gaussian shaped transition rather than a sharp cut-off. In fact this is a much more realistic representation of how spectral or spatial mode sampling is actually be done. This reduces the ringing considerably in the spectral and spatial modes (figures 7(g) & (h)). When applied to the cross-shaped mask there appears to be shift in the ringing to lower frequencies and there is also a reduction in the RMS error from 10.5 digital-levels to 9.4. DISCUSSION An important consideration when using any sampling technique is how much of the signal is lost irretrievably. This can be computed from the full CCD array and expressed by the information density function. This calculation provides an accurate quantitative estimate of the size of the error expected in the reconstructed image (Huck, 1985). The total power in the signal from all elements can be found by integrating the power spectrum. The power in the signal under the cross can also be computed. The ratio of the power under the cross to the total power quantifies how much of the original CCD full frame signal is contained in the sample. If the fraction is very high then the sample can be reliably used to reconstruct the full CCD frame. Otherwise care must be taken in interpretation. The cross pattern sampling is the simplest method that makes sampling in Fourier domain worthwhile. The number of samples could be varied across the frequency domain so that areas of strong signal power could be sampled more densely than areas of of weak power, regardless of whether they fall along the axes. What is required is some model of where the power in the signal will fall within the power spectrum. CONCLUSIONS What implications does this have for imaging spectrometers and how their data should be sampled? The spatial domain should not be undersampled, in general, because the spatial frequency content of the image cannot be predicted and therefore the resulting image can have spurious values which can lead to incorrect image analysis using linear systems theory. In the spectral domain the image can be undersampled because the spectral correlations are much better behaved and are much more predictable. Undersampling should only be done when the behaviour of the intervening samples is known or predictable. This is often the case with the spectral signal and is the assumption that has been used (perhaps unwittingly) by analysts to interpret multispectral remote sensing imagery for years. It is apparent from Fourier transforming a typical imaging spectrometer CCD array that the spatial/spectral frequency content of the imaging spectrometer data is mainly one of low- spatial/low-spectral, low-spatial/high-spectral or high spatial/ low-spectral. There is very little power in the high-spatial/ high-spectral frequencies. If we are going to sample from the CCD array in the most efficient manner, then we must sample where we expect to find the power in the signal. In the imaging spectrometer the power in the signal is in the cross pattern that lies along the spatial and spectral frequency axes. The cross pattern can be most efficiently sampled in the Fourier domain and must be recorded there to realize the data reduction. Reconstructing the full spatial and spectral resolution is not possible in the most rigorous sense. The low-spatial and low- spectral frequencies will be faithfully represented. The low- spatial/high-spectral or high-spatial/low-spectral frequencies will be reconstructed with good results but aliasing from the high-spectral/high-spatial frequencies can occur. If high- spectral/high-spatial frequencies are present in the signal they will be aliased. This reconstruction method will give good results if the energy in the signal in the high-spectral/high- spatial frequencies is a small fraction of the total power. In general the accuracy of the reconstruction will depend on the fraction of the power, of the total signal, which falls into the high-spatial/high-spectral range. ACKNOWLEDGEMENTS We would like to thank the people at Moniteq Ltd. for allowing us to work with the FLI which lead to this paper and for letting us include the FLI imagery. We are indebted to Itres Research Ltd. for allowing us to use the CASI to collect the unique full frame spectrometer imagery. Thanks to Wayne Rasband of the National Institute of Health and Arlo Reeves of Dartmouth College who developed NIH Image and the FFT enhancements to it. Thanks also to Bill Connor who let us use a prelease version of Imagine. Both of these programs made the data processing so much easier on the Macintosh. We also wish to gratefully acknowledge the support of the Earth Observations Laboratory, personnel and its Director Ellsworth LeDrew. This research is supported, in part, by a Centre of Excellence grant from the Province of Ontario to the Institute for Space and Terrestrial Science and an NSERC operating grant to John R. Miller. REFERENCES Babey, S.K. & C.D. Anger, 1989. The Compact Airborne Spectrographic Imager, Proc. IGARSS'89, Vol. 2, pp. 1028- 31. Buxton, R.A.H., 1988. The FLI airborne imaging spectrometer: A highly versatile sensor for many applications, Proc. ESA Workshop, Frascati, pp. 7-16. Cliche, G., F. Bonn & P. Teillet, 1985. Integration of the SPOT panchromatic channel into its multispectral mode for image sharpness enhancement, Photogramm. Eng. & Remote Sensing, Vol. 51, pp. 311-316. Colvocoresses, A.P., 1977. Proposed parameters for and operational Landsat, Photogramm. Eng. & Remote Sensing, Vol. 43, pp. 1139-45. Dunlop, J.D., 1989. Combining spectral and spatial mode imagery from an imaging spectrometer, Proc. IGARSS'89, Vol 4, pp. 2077-80. Hallada, W.A. & S. Cox, 1983. Image sharpening for mixed spatial and spectral resolution satellite systems, Proc. 7th Int. Symp. Remote Sensing Envir., pp. 1023-32. Huck, F.O., C.L. Fales, N. Halyo, R.W. Samms & K. Stacy, 1985. Image gathering and processing: information and fidelity, J. Opt. Soc. Am. A., 2(10), pp. 1644-66. Miller, J.R., E.W. Hare, J. Wu, 1990. Quantitative characteri zation of the Vegetation Red Edge Reflectance: 1. An Inverted Gaussian reflectance model, Int. J. Remote Sensing, In press. Roller, N.E.G. & S. Cox, 1980. Comparison of Landsat MSS and merged MSS/RBV data for analysis for natural vegetation, Proc. 14th Int. Symp. Remote Sensing of Envir., pp. 1001-7. Schowengerdt, R.A., 1980. Reconstruction of multispatial, multispectral image data using spatial frequency content, Photogramm. Eng. & Remote Sensing, Vol. 46, pp. 1325-34. Simard, R., 1982. Improved spatial and altimétrie information from SPOT composite imagery, Proc. Int. Symp. Remote Sensing of Envir., pp. 433-44. Tom, V.T., M.J. Carlotto & D.K. Scholten, 1984. Spatial resolution improvement of TM thermal band data, SPIE 504, Applications of Digital Image Processing VII, pp. 384-390.

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