Spatial autocorrelation analysis of hyperspectral imagery for feature selection
- West Virginia Univ., Morgantown, WV (United States). Dept. of Geology and Geography
The spatial information in a single spectral image can be estimated from the spatial autocorrelation, which is a measure of how the local variation compares with the overall variance in a scene. In images of random noise, the local variation tends to be similar to the overall variance. In contrast, scenes in which large features can be discerned have clusters of pixels with similar values, which cause the local variation to be much smaller on average than the overall scene variance. Feature selection is the process of finding a subset of the original bands that provides an optimal trade-off between probability of error and classification cost. Three feature selection problems are addressed in this paper: (1) narrow band feature selection, which is the selection of a subset of individual bands; (2) broad band feature selection, in which groups of adjacent bands are selected, and (3) nonadjacent multiple band feature selection, in which selection of the groups of bands is not limited to adjacent bands. Spatial autocorrelation is useful in all three feature selection problems. Tests with simulated data indicate that the spatial autocorrelation based methods consistently identify the best bands or groups of bands. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data of eastern Washington state are used to illustrate the technique on real data.
- Sponsoring Organization:
- Pacific Northwest Lab., Richland, WA (United States)
- OSTI ID:
- 323791
- Journal Information:
- Remote Sensing of Environment, Vol. 60, Issue 1; Other Information: PBD: Apr 1997
- Country of Publication:
- United States
- Language:
- English
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