Terrain classification in urban wetlands with high-spatial-resolution multispectral imagery

Richard Chris Olsen; Jamada J. Garner; Eric J. Van Dyke

doi:10.1117/12.465161

8 April 2003 Terrain classification in urban wetlands with high-spatial-resolution multispectral imagery

Richard Chris Olsen, Jamada J. Garner, Eric J. Van Dyke

Proceedings Volume 4881, Sensors, Systems, and Next-Generation Satellites VI; (2003) https://doi.org/10.1117/12.465161
Event: International Symposium on Remote Sensing, 2002, Crete, Greece

Abstract

The Elkhorn Slough area is a major wildlife reserve, and important wetlands area along the central coast of California. As part of the ongoing study of this area, accurate classifications of the areas within the slough are needed. The objective of this work was to use the 4-m spatial resolution, 4-color sensor on the IKONOS satellite. A manually constructed classification map was used to train the spectral classifiers. As the work evolved, a problem emerged due to the relatively high spatial resolution. Regions of interest such as oak woodlands have highly variable spectral elements when observed at high spatial resolution. As a consequence, the refinement of the regions of interest obtained from the original classification map required not only the elimination of erroneous spectral elements; but also required inclusion of a range of spectra, as opposed to the traditional approach of selecting pure exemplars. Modest success was obtained from the classification effort. As a consequence, additional virtual bands were created by constructing texture features from the higher spatial resolution panchromatic band. This enabled spectrally similar classes such as trees and cultivated fields to be distinguished.

Citation Download Citation

Richard Chris Olsen, Jamada J. Garner, and Eric J. Van Dyke "Terrain classification in urban wetlands with high-spatial-resolution multispectral imagery", Proc. SPIE 4881, Sensors, Systems, and Next-Generation Satellites VI, (8 April 2003); https://doi.org/10.1117/12.465161

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