Band selection from hyperspectral data for conifer species identification

Geographic Information Sciences

Volumn 6, Issue 2, 2000, Pages 137-142

  Pu, Ruiliang ^a   Gong, Peng ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84983763356     PISSN: 10824006     EISSN: None     Source Type: Journal    
DOI: 10.1080/10824000009480542     Document Type: Article

References (29)

1. Boardman, J. W., 1989, Inversion of Imaging Spectrometry Data Using Singular Value Decomposition, Proceedings of the IGARSS’89, Vancouver, British Columbia, Canada, pp. 2069-2072.
2. Card, D. H., D. L. Peterson, and P. A Matson, 1988, Prediction of Leaf Chemistry by Use of Visible and Near Infrared Reflectance Spectroscopy, Remote Sensing of Environment, 26:123-147.
3. Chang, C.-L, Q. Du, T.-L. Sun, and Mark L. G. Althouse, 1999, A Joint Band Prioritization and Band-decorrelation Approach to Band selection for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 37 (6):2631-2641.
4. Chen, J., P. Gong, and J. A R. Blais, 1993, Application of Neural Networks in Forest Ecological Classification, Technical Papers of ASPRS/ACSM Annual Convention, New Orleans, Louisiana, Vol. 3. pp. 6571.
5. Coleman, T. L., P. A. Agbu, O. L. Montgomery, T. Gao, and S. Prasad, 1991, Spectral Band Selection for Quantifying Selected Properties in Highly Weathered soils, Soil Science, 151(5):355-361
6. Csillag, F., L. Pasztor, L. L. Biehl, 1993, Spectral Band Selection for the Characterization of Salinity Status cf Soils, Remote Sensing of Environment, 43:231-242.
7. Gong, P., 1996, Integrated Analysis of Spatial Data from Multiple Sources: Using Evidential Reasoning and Artificial Neural Network Techniques for Geological Mapping, P. E. & R. S., forthcoming.
8. Gong, P., J. R. Miller, and M. Spanner, 1994, Forest Canopy Closure from Classification and Spectral Unmixing of Scene Component-Multisensor Evaluation of an Open Canopy, IEEE Transactions on Geoscience and Remote Sensing, 32(5):1067-1080.
9. Gong, P., R. Pu, and J.R Miller, 1995, Coniferous Forest Leaf Area Index Estimation along the Oregon Transect Using Compact Airborne SpectrographicImager Data, P. E. & R. S., 61(9):1107-1117.
10. Gong, P., R Pu, and J.R. Miller, 1992, Correlating Leaf Area Index of Ponderosa Pine with HyperspectralCASI Data, Canadian Journal of Remote Sensing, 18:275-282.
11. Gong, P., R. Pu, and B. Yu, 1997, Conifer Species recognition: An Exploratory Analysis of In Situ Hyperspectral Data, Remote sensing cf Environment, 62:189-200.
12. Hardie, R. C., M. Vaidyanathan and P. F. McManamon, 1998, Spectral Band Selection and Classifier Design for a Multispectral Imaging Laser Radar, Optical Engineering, 37(3):752-762.
13. Henderson, T. L., A Szilagyi, M F. Baumgardner, C. T. Chen and David A. Landgrebe, 1989, Spectral Band Selection for Classification of Soil Organic Matter Content, Soil Sei. Soc. Am. J., 53:1778-1784.
14. Hepner, G. F., T. Logan, N. Ritter, and N. Bryant, 1990, Artificial Neural Network Classification Using Minimal Training Set, Photogrammetric Engineering & Remote Sensing, 56:469-473.
15. Jia, X. and J. A. Richards, 1994, Efficient Maximum Likelihood Classification for Imaging Spectrometer Data Sets, IEEE Transactions on Geoscience and Remote Sensing, 32:274-281.
16. Johnson, L. F., C. A Hlavka, and D. L. Peterson, 1994, Multivariate Analysis of AVIRIS Data for Canopy Biochemical Estimation along the Oregon Transect, Remote Sensing of Environment, 47:216-230.
17. Matson, P. A., L. F. Johnson, J. R Miller, C. R Billow, and R. Pu, 1994, Seasonal Patterns and remote Spectral Estimation of Canopy Chemistry across the Oregon Transect, Ecological Application, 4(2):280-298.
18. McCormack, M. D., D. E. Zaucha, and D. W. Dushek, 1993, First-break Refraction Event Picking and Seismic Data Trace Editing Using Neural Networks, Geophysics, 58:67-78.
19. Peterson, D. L., J. D. Aber, P. A Matson, D. H. Card, N. Swanberg, C. Wessman, and M. A. Spanner, 1988, Remote Sensing of Forest Canopy and Leaf Biochemical Contents, Remote Sensing of Environment, 2485-108.
20. Pao, Y., 1989, Adaptive Pattern Recognition andNeural Networks, Addison and Wesley: New York.
21. Pu, R. and P. Gong, 1996, Band Selection from Hyperspectral Data Using a Fuzzy Clustering Technique for Tree Species Identification, Geoinformatics’96 Proceedings, West Palm Beach, Florida, USA, April 26-2, 1996, pp.464-470.
22. Rumelhart, D. E., G. E. Hinton, and R. J. Williams, 1986, Learning Internal Representations by Error Propagation, In Parallel Distributed Processing-Explorations in the Microstructure of Cognition, Vol. 1: Edited by D. E. Rumelhart, J. L. McClelland, The MIT Press: Massachusetts, pp. 318-362.
23. Running, S. W., R. R. Nemani, D. L. Peterson, L. E. Band, D. F. Potts, L. L. Pierce, and M. A. Spanner, 1989, Mapping Regional Forest Evapotranspiration and Photosynthesis by Coupling Satellite Data with Ecosystem Simulation, Ecology, 70:1090-1101.
24. SAS institute Inc., 1991, SAS/ST AT User’s Guide, Release 6.03 Edition.
25. Wessman, C. A., J. D. Aber, and D. L. Peterson, 1989, An Evaluation of Imaging Spectrometryfor Estimating Forest Canopy Chemistry, International Journal of Remote Sensing, 10:1293-1316.
26. Yin, Y., and X. Xu, 1991.Applying Neural Net Technology for Multi-objective Land Use Planning, Journal of Environmental Management, 32:349-356.
27. Yu, B., I. M. Ostland, P. Gong, and R Pu, 1999, Penalized Discriminant Analysis of In Situ Hyperspectral Data for Conifer Species Recognition, IEEE Transactions on Geoscience and Remote Sensing, 37(5):2569-2577.
28. Zhang, A, P. Gong, and J. R. Miller, 1993, The Effect of Autocorrelation and Noise on Image Pixel Decomposition through Spectral Mixing Analysis, Proceedings of the 16th Canadian Symposium on Remote Sensing, pp. 847-850.
29. Zhang, A., 1994, Hyperspectral Image Analysis: A Study of Band Selection Methods and the Sensitivity of Linear Spectral Unmixing, A Thesis of Master Degree, The University of Calgary, Canada, 1-78.