CLASSIFICATION PERFORMANCE OF RANDOM-PROJECTION-BASED DIMENSIONALITY REDUCTION OF HYPERSPECTRAL IMAGERY

High-dimensional data such as hyperspectral imagery is traditionally acquired in full dimensionality before being reduced in dimension prior to processing. Conventional dimensionality reduction on-board remote devices is often prohibitive due to limited computational resources: on the other hand, integrating random projections directly into signal acquisition offers alternative dimensionality reduction without sender-side computational cost Effective receiver-side reconstruction from such random projections has been demonstrated previously using compressive-projection principal component analysis (CPPCA) While this prior work has focused on squared-error quality measures, the present work reports experimental results illustrating preservation of statistical class separation and anomaly-detection performance for CPPCA reconstruction following random-projection-based dimensionality reduction