High Performance Algorithm and Architecture for Hyper Spectral Data Processing

Hyperspectral remote sensing sensors are capable of collecting remote sensing imagery at several hundred bands over the spectrum. Under these advanced observation tools, the observed phenomena can be identified with unique signatures, however, the resulting data volumes are quite massive. Processing hyperspectral data using new efficient techniques and architectures is therefore very critical.

Our advanced hyperspectral processing falls into three different areas;

  1. Investigating high-performance algorithms for dimension reduction of hypersepctral data based on methods such as Principal Component Analysis (PCA), Projection Pursuit, and Wavelet analysis.
  2. Investigating high-performance algorithms for remote sending data fusion.
  3. Investigating reconfigurable computer architectures for satellite onboard processing of hyperspectral imagery for dimension reduction and cloud detection