Detection of Local Anomalies in High Resolution Hyperspectral Imagery using Geostatistical Filtering and Local Spatial Statistics
This paper describes a methodology to detect patches of disturbed soils in high resolution hyperspectral imagery, which involves successively a multivariate statistical analysis (principal component analysis, PCA) of all spectral bands, a geostatistical filtering of regional background in the first principal components using factorial kriging, and finally the computation of a local indicator of spatial autocorrelation to detect local clusters of high or low reflectance values as well as anomalies. The approach is illustrated using one meter resolution data collected in Yellowstone National Park. Ground validation data demonstrate the ability of the filtering procedure to reduce the proportion of false alarms, and its robustness under low signal to noise ratios. By leveraging both spectral and spatial information, the technique requires little or no input from the user, and hence can be readily automated.
KeywordsAutocorrelation Remote Sensing Kriging Sonar
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