Optical remote sensing imagery and orthophoto derived stem density were classified to map coniferous forest cover for a study area located in the Western Hunsrück, Germany. Our objectives were (i) to investigate if hyperspectral data contain more information relevant to classification of species and age classes than multispectral imagery and (ii) to test in what way classification results can b...

In the current study, 2002 AVIRIS and 2006 AISA high resolution imagery were applied to the spectroscopic investigation of spatio-temporal variations in forest chemistry. Focused primarily on the foliar biochemistry of Douglas-fir (Pseudotsuga menziesii) stands within the Greater Victoria Watershed, Victoria, BC, Canada, samples were collected and relationships between chemistry and reflectance...

This paper highlights SSC San Diego contributions to the research and development of hyperspectral technology. SSC San Diego developed the real-time, onboard hyperspectral data processor for automated cueing of high-resolution imagery as part of the Adaptive Spectral Reconnaissance Program (ASRP), which demonstrated a practical solution to broad area search by leveraging hyperspectral phenomeno...

High dimensional source vectors, such as occur in hyperspectral imagery, are partitioned into a number of subvectors of (possibly) different length and then each subvector is vector quantized (VQ) individually with an appropriate codebook. A locally adaptive partitioning algorithm is introduced that performs comparably in this application to a more expensive globally optimal one that employs dy...

The HumanID program is developing techniques and methods for identifying humans at a distance. Techniques being investigated are face recognition; recognition from body dynamics in video including gait; recognition from infrared, mulitispectral, and hyperspectral imagery. To support these activities a large database of imagery is being collected, and to assess performance advanced statistical m...

Knowledge of seedling regeneration in burn sites is a valuable aspect of ecosystem monitoring. Remote sensing methods are ideal for such monitoring since large spatial extents can be monitored at multiple time scales. The goal of this project was to asses how well hyperspectral AVIRIS data can discriminate the differences between seedling densities in postfire regenerating sites, as compared to...

Hyperspectral sensors observe hundreds or thousands of narrow contiguous spectral bands. The use of hyperspectral imagery for remote sensing applications is new and promising, yet the characterization and analysis of such data by exploiting both spectral and spatial information have not been extensively investigated thus far. A generic methodology is presented for detecting and compensating ano...