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Investigation of nonlinearity in hyperspectral remotely sensed imagery

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dc.contributor.author Han, Tian
dc.date.accessioned 2009-09-10T20:13:23Z
dc.date.available 2009-09-10T20:13:23Z
dc.date.copyright 2009 en
dc.date.issued 2009-09-10T20:13:23Z
dc.identifier.uri http://hdl.handle.net/1828/1750
dc.description.abstract Hyperspectral remote sensing excels in its high spectral resolution, which enables the generation of contiguous spectral profiles covering the visible to shortwave infrared region (400 – 2500 nm) of the solar electromagnetic spectrum. The high spectral resolution has greatly stimulated the applications of hyperspectral remote sensing in different disciplines. The initial applications have been found in mineral exploration, followed by applications in environmental research, forest health evaluation, vegetation species mapping, precision farming, water pollution monitoring, and military target identification. It has been noticed, however, that there is an inconsistency between the statistical characteristics of hyperspectral remotely sensed data and the methods employed to model and process the data for information extraction. On the one hand, hyperspectral data are considered inherently nonlinear, due to the multiple nonlinear sources involved in the data formation. On the other hand, hyperspectral data has long been modeled and processed as realisations of some linear stochastic processes. What is the impact of this inconsistency on hyperspectral data analysis? This dissertation is prepared to address this question by firstly evaluating the significance of nonlinearity in hyperspectral data and secondly examining the influence of nonlinearity on dimensionality estimation, and noise reduction. This dissertation proved that nonlinearity existed in hyperspectral data and it was statistically significant. It was found that the dimension of hyperspectral data was substantially smaller when the nonlinearity was considered compared to estimations based on linear algorithms. It was demonstrated that improved noise reduction was achieved without compromising spectral absorption features if the nonlinearity was taken into consideration. The algorithms discussed in this dissertation were implemented, which provided a useful tool set for those who are interested in studying the nonlinear behaviours in hyperspectral data, which are not available in commercial remote sensing software packages. en
dc.language English eng
dc.language.iso en en
dc.rights Available to the World Wide Web en
dc.subject remote sensing en
dc.subject nonlinearity en
dc.subject.lcsh UVic Subject Index::Sciences and Engineering::Applied Sciences::Computer science en
dc.title Investigation of nonlinearity in hyperspectral remotely sensed imagery en
dc.type Thesis en
dc.contributor.supervisor Goodenough, D.
dc.contributor.supervisor Jahnke, Jens H.
dc.degree.department Dept. of Computer Science en
dc.degree.level Doctor of Philosophy Ph.D. en
dc.identifier.bibliographicCitation T. Han and D. G. Goodenough, “Investigation of nonlinearity in hyperspectral imagery using surrogate data methods,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 10, pp. 2840 – 2847, 2008. en
dc.identifier.bibliographicCitation T. Han and D. G. Goodenough, “Estimating dimensionality of hyperspectral data using false neighbour method,” Proceedings of IEEE International Geoscience and Remote Sensing Symposium, pp. 83-86, July 6–11, 2008, Boston, USA. en
dc.identifier.bibliographicCitation T. Han and D. G. Goodenough, “Investigation of nonlinearity in hyperspectral remotely sensed imagery – a nonlinear time series analysis approach,” Proceedings of IEEE International Geoscience and Remote Sensing Symposium, pp. 1556 – 1560, July 23–27, 2007, Barcelona, Spain. en
dc.identifier.bibliographicCitation T. Han and D. G. Goodenough, “Nonlinear dimensionality reduction of hyperspectral data based on Locally Linear Embedding,” Proceedings of IEEE International Geoscience and Remote Sensing Symposium, pp. 1237 – 1240, July 25–29, 2005, Seoul, Korea. en


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