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.

• Type: Journal
• Year: 2009
• Where: IGARSS
• Authors: James E. Fowler, Qian Du, Wei Zhu, Nicolas H. Younan
• Keywords: Alternative Dimensionality Reduction, Conventional Dimensionality Reduction, Dimensionality Reduction, IGARSS 2009, Remote Sensing