Hyperspectral data processing chain development perspectives for vegetation studies
DOI:
https://doi.org/10.56617/tl.4153Keywords:
Remote Sensing, Hyperspectral, Vegetation, Image processing, Digital ImageryAbstract
In this paper the possibilities of hyperspectral data processing is investigated regarding to the application of these images in natural and ecological applications. A short overview is given of the available methods for interpretation purposes and special attention is paid on how the unique properties of hyperspectral data are affecting the choice from available methods for data processing. A broad identification of possible processing chain is discussed with the aim of developing more modular and application driven way of processing of the large data volumes. Automatic or semi-automatic procedures are proposed and key steps are to be identified that could lead to high quality mapping products by means of digital signal processing. Some experimental results are published and the broad methodology is presented that is aimed to use for identify data processing chain for vegetation mapping purposes. This work is to be continued with testing the performance at different stages of interpretation while different techniques are used. Furthermore over the HYPER-I-NET research network a document is to be supplied with the collection of results and application specific suggestions regarding to hyperspectral data processing for vegetation monitoring purposes.
References
Benediktsson J. A., Pesaresi M., Arnason K. 2003: Classification and Feature Extraction of Remote Sensing Images from Urban Area based on Morphological Approaches, IEEE Trans. On Geoscience and Remote Sensing, 41: 1940-1949. https://doi.org/10.1109/TGRS.2003.814625
Chang C. I. 2007: Hyperspectral data exploitation. Theory and applications. John Willey and Sons Inc. Fukunaga K. 1990: Introduction to Statistical Pattern Recognition. Academic Press, Second Edition
Gong P., Howarth P. J. 1992: Land-use classification of SPOT HRV data using a cover-frequency method. International Journal of Remote Sensing, 13: 1459-1471. https://doi.org/10.1080/01431169208904202
Gualitieri J. A., Cromp R. F. 1998: Support vector machines for hyperspectral remote sensing classification. In: Merisko, R. J. (ed.), 1998: Proc. SPIE-27th AIPR Work-shop Advances in Computer Assisted Recognition, 3584: 221-232.
Haralick R. M., Shanmugam K., Dinstein I. 1973: Texture features for image classification. IEEE Trans. Systems Man Cybernet. 3: 610-621. https://doi.org/10.1109/TSMC.1973.4309314
Hyvarinen A., Karhunen J., Oja, E. 2001: Independent Component Analysis, John Wiley and Sons, New York, https://doi.org/10.1002/0471221317
Hyressa SWOT and User Needs workshop report, accessed on line at http://www.hyressa.net/documents/, April 2007.
Kruse F. A., Lefkoff A. B., Boardman J. B., Heidebrecht K. B., Shapiro A. T., Barloon P. J., Goetz A. F. H. 1993: The Spectral Image Processing System SIPS - Interactive Visualization and Analysis of Imaging spectrometer Data. Remote Sensing of the Environment, 44: 145-163. https://doi.org/10.1016/0034-4257(93)90013-N
Landgrebe D. A. 2003: Signal Theory Methods in Multispectral Remote Sensing. John Wiley and Sons, Hoboken, New Jersey https://doi.org/10.1002/0471723800
Lee C., Landgrebe D. A. 1997: Decision Boundary Feature Extraction for Neural Networks, IEEE Trans. on Geoscience and Remote Sensing, 8: 75-83. https://doi.org/10.1109/72.554193
Lillesand T. M., Kiefer R. W. 2004: Remote Sensing and Image Interpretation. Wiley & Sons, Fifth Edition. Matheron G. 1997: Principles of geostatistics. Econ. Geol. 58: 1246-1266. https://doi.org/10.2113/gsecongeo.58.8.1246
Marino C. M., Panigada C., Busetto L., Galli A., Boschetti M. 2000: Environmental applications of airborne hyperspectral remote sensing: asbestos concrete sheeting identification and mapping, Proc. of the 14th International Conference and Workshops on Applied Geologic Remote Sensing
Quattrochi D. A., Goodchild, M. F. (eds.) 1997: Scale in remote sensing and GIS. Boca Raton: CRC Lewis Publishers
Ramstein G., Raffy, M. 1989: Analysis of the structure of radiometric remotely-sensed images. International Journal of Remote Sensing 10: 1049-1073 https://doi.org/10.1080/01431168908903944
Richards J. A. 2005: Analysis of remotely sensed data: the formative decades and the future. IEEE Trans. Geosci. Remote Sensing, 43: 422-432. https://doi.org/10.1109/TGRS.2004.837326
Richards J. A., Xiuping, J. 2006: Remote Sensing Digital Image Analysis: an introduction. Springer Verlag, Berlin (4th edition) https://doi.org/10.1007/3-540-29711-1
Serra J. P. 1989: Image analysis and mathematica morphology. International Journal of Remote Sensing 10: 1049-1073. https://doi.org/10.1080/01431168908903944
Soille P. 2003: Morphological Image Analysis: Principles and Applications. Springer Verlag, Berlin (2nd edition) https://doi.org/10.1007/978-3-662-05088-0
Srinivasan A. 1991: An artificial intelligence approach to the analysis of multiple information sources in remote sensing. Ph.D. thesis, Univ. New South Wales, School of Elect. Eng., Kensington, Australia
Srinivasan A., Richards J. A. 1993: Analysis of GIS spatial data using knowledge-based methods. Int. J. Geograph. Inf. Syst., 7: 479-500. https://doi.org/10.1080/02693799308901978
Swain P. H., Dawis S. M. (eds) 1978: Remote Sensing. The Quantitative Approach. New York McGraw Hill Woodcock C. E., Strahler A. H., Jupp D. L. B. 1988: The use of semivariogram in remote sensing and simulated images: Real digital images. Remote Sensing Environments 25: 349-379. https://doi.org/10.1016/0034-4257(88)90109-5
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