PARENT SESSION
Organized Oral Session 9: Multiscale advanced raster map analysis for ecosystem health monitoring, assessment, and management in the 21st Century
Organizer(s): GP Patil and W Myers
Monday, August 8, 1:30 PM - 5:00 PM, Meeting Room 511a, Level 5, Palais des congrès de Montréal

Geostatistical and local cluster analysis of high resolution hyperspectral imagery for detection of patches of disturbed soils.

Jacquez, Geoffrey^*,1, Goovaerts, Pierre¹, Marcus, Andrew¹, ¹ BioMedware, Ann Arbor, MI, USA

ABSTRACT- This presentation describes a methodology to detect patches of disturbed soils on high resolution hyperspectral imagery, which involves successively a multivariate statistical analysis (principal component analysis, PCA) of all spectral bands, a geostatistical filtering of noise and 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. In almost all scenarios, the proposed approach outperforms traditional anomaly detectors (i.e. RXD) and fewer false alarms were obtained when using statistic S2 (average absolute deviation of p-values from 0.5 through all spectral bands) to summarize information across bands. Image degradation through addition of noise or reduction of spectral resolution tends to blur the detection of anomalies, leading to more false alarms, in particular for the identification of the least pure pixels. Results from a tailings site demonstrated that the approach still performs reasonably well for highly complex landscape with multiple targets of various sizes and shapes. By leveraging both spectral and spatial information, the technique requires little or no input from the user, and hence can be readily automated.

Key words: hyperspectral imagery, geostatistics, spatial statistics, landscape classification