Band selection for viewing underwater objects using hyperspectral sensors

Multispectral and hyperspectral sensors are being used for remote sensing and imaging of ocean waters. Many applications require the compression of hyperspectral data to achieve real-time transmission or exploitation. Hyperspectral data compression or reduction has been accomplished using techniques based upon principal component analysis or linear unmixing. Alternatively, data compression (reduction) may be performed by band selection, or band selection may be preliminary to either of the other compression techniques. Band selection also has implications for sensor design and the stability of estimates of processing parameters. In this study, we address the question of which bands are the most efficacious for imaging submerged objects, such as whales, using an anomaly detector, or a matched filter. Bands are selected by optimizing a detection criterion subject to a constraint on the number of bands. The technique is applied to give hyperspectral data sets, and the optimum bandwidths and centers are determined. The loss in performance from selecting reduced numbers of bands is tabulated and the need for adaptively selecting reduced numbers of bands is demonstrated.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.