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:: AIRBORNE IMAGING SPECTROMETER DATA ANALYSIS APPLIED TO AN AGRICULTURAL DATA SET. K. Staenz and D. G. Goodenough

Document type:: Multivolume work

Structure type:: Chapter

For comparison, a second eight-band data set was generated from the spectral data using band averaging to match the PMI spatial band set #3 (GEOBOTANY) given in Table 2. The GEOBOTANY band set #3 is well positioned for crop discrimination. The same feature selection criteria were applied to this new data set for the same classes. The best four-band subset was (1,4,5,6); that is, 491.42 nm, 675.75 nm, 708.26 nm, and 734.22 nm. The maximum likelihood classification results for each band combination selected by feature selection are given in Table 3. The classification results are also summarized in Figure 6 for the two different data sets. It can be seen that the weighted mean classification accuracy using the GEOBOTANY data set varies between 57.03 ± 10.59 (s.e.m.)% for the best one- band subset (band 4, 675.75 nm) and 98.59 ± 0.45% for the eight-band combination. For the band moments the classification accuracy varied between 61.38 ± 12.49% for band 6 (skewness) and 99.41 ± 0.27% for all bands. With band moments, an excellent result of 94.86 ± 1.74% was already achieved with the three-band subset (1,7,8) where (1) is the mean, (7) the kurtosis, and (8) the band-concentrated moment. A similar accuracy level was reached with the GEOBOTANY four-band subset (1,4,5,6) leading to a result of 94.54 ± 1.50%. The band-moments method produces slightly higher accuracies than the GEOBOTANY band set by a margin up to 4% depending upon the different band combinations. The standard error of the mean classification accuracy is generally lower for the band moments indicating slightly less confusion between classes, especially between barley, wheat, and fallow. Confusion between these three targets was resolved for the four-band subset using the band moments while for the corresponding GEOBOTANY subset a 5% confusion with wheat and fallow remains. These classification results are promising for further application of the band- moments analysis concept to other imaging spectro meter data sets. CONCLUSIONS This paper describes the methodologies necessary to bring the PMI data to an acceptable data quality level for classification purposes, as well as preliminary classification results. Preprocessing steps include assessment and correction techniques for bad data in the spectral and spatial domains and radiometric alignment of the five different PMI cameras. The camera adjustment procedure involves a polynomial regression approach which removes part of the viewing angle effect. This effect is quite substantial due to the large PMI scan angle range of 75.5 degrees. Data variations within a specific camera (a field of view of 14.5 degrees) caused by the viewing angle and other sensor related effects were encountered, especially in camera 4. Future software development will take this phenomenon into account. Further data assessments with respect to noise and effective wavelength position of the sensor indicate satis factory results. The random noise shows, for example, a signal-to-noise ratio of 600:1 for an average signal level of 803.4 x 10 B W cm -2 nm 1 sr' 1 in band 247 (751.21 nm) for the different cameras. The radiometric calibration data for normalization purposes was found to be suspect because of noise and the (erroneous) magnitude of the calibration values. Despite the problems encountered during the data evaluation, preliminary classification results involving data reduction techniques followed by maximum likelihood classification are encouraging. Band-moment analysis in the spectral domain and band averaging to match the PMI spatial band set GEOBOTANY were used to create two eight-band data sets. Feature selection, with a branch and bound algorithm, was applied to these data in order to establish the best band subsets for the eight classes considered. Similar classification accuracies were achieved with both methods for the agricultural data set considered. The best four- band subset, consisting of the moments mean (1), skewness (6), kurtosis (7), and band-concentrated moment (8), resulted in an overall accuracy of 97% compared to 95% for the best four-band GEOBOTANY subset (1,4,5, and 6). Band-moment analysis is an automated procedure for data reduction in order to achieve similar classification results as with a carefully selected band set derived from a detailed spectroscopic analysis. Further inves tigations will be concentrated on the application of the band-moment analysis concept to other imaging spectrometer data sets (e.g. AVIRIS), multispectral imagers, and ground-based spectrometer data involving not only agricultural objects but also different forest types. ACKNOWLEDGEMENTS The authors would like to thank Dr. R. Buxton, L. H. Gray, and R. Gordon from Moniteq Ltd. for their technical advice with respect to PMI, as well as Dr. K.I. Itten and P. Meyer from the University of Zurich-Irchel for their support during the data acquisition. The valuable discussions with Dr. P. M. Teillet and the assistance of B. Thibault and A. Boisvert in analyzing the data are gratefully acknowledged. Our thanks go to A. Kalil for the excellent typing of this manuscript. REFERENCES Borstad, G.A., Edel, H.R., Gower, J.F.R. and Hollinger, A.B., 1985. Analysis of Test and Flight Data from the Fluorescence Line Imager. Canadian Special Publication of Fisheries and Aquatic Sciences 83, Department of Fisheries and Oceans, Ottawa, Ontario, Canada, 38 pages. Brown, R.J., Ahern, F.J., Ryerson, R.A., Thomson, K.P.B., Goodenough, D.G., McCormick, J.A. and Teillet, P.M., 1980. Rapeseed: Guidelines for Operational Monitoring. Proceedings of the 6th Canadian Symposium on Remote Sensing, Canadian Aeronautics and Space Institute, Ottawa, Ontario, Canada, pp.321-333. Buechel, S.W., Philipson, W.R. and Philpot, W.D., 1989. The Effects of a Complex Environment on Crop Separability with Landsat TM. Remote Sensing of Environment, Vol.27, pp.261-272. Curran, P.J. and Dungan, J.L., 1988. Estimating the Signal-to-Noise Ratio of AVIRIS Data. NASA Technical Memorandum 101035, NASA/AMES Research Center, Moffet Field, California, 21 pages. Goodenough, D.G., Narendra, P.M. and O'Neill, K., 1978. Feature Subset Selection in Remote Sensing. Canadian Journal of Remote Sensing, Vol.4, No.2, pp.143-148. Hlavka, C., 1986. Destriping AIS Data Using Fourier Filtering Techniques. Proceedings of the 2nd Airborne Imaging Spectrometer Data Analysis Workshop, JPL 86-35, Pasadena, California, pp.74-

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