We have developed a methodology for wavelength band selection. This methodology can be used in system design studies to provide an optimal sensor cost, data reduction, and data utility trade-off relative to a specific application. The methodology combines an information theorybased criterion for band selection with a genetic algorithm to search for a near-optimal solution.We have applied this methodology to 612 material spectra from a combined database to determine the band locations for 6, 9, 15, 30, and 60-band sets in the 0.42 to 2.5 microns spectral region that permit the best material separation. These optimal band sets were then evaluated in terms of their utility related to anomaly detection and material identification using multi-band data cubes generated from two HYDICE cubes. The optimal band locations and their corresponding entropies are given in this paper. Our optimal band locations for the 6, 9, and 15-band sets are compared to the bands of existing multi-band systems such as Landsat 7, Multispectral Thermal Imager, Advanced Land Imager, Daedalus, and M7. Also presented are the anomaly detection and material identification results obtained from our generated multi-band data cubes. Comparisons are made between these exploitation results with those obtained from the original 210-band HYDICE data cubes.

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JPEG-2000 is the new image compression standard currently under development by ISOIIEC. Part I of this standard provides a "baseline" compression technology appropriate for grayscale and color imagery. Part II of the standard will provide extensions that allow for more advanced coding options, including the compression of multiple component imagery. Several different multiple component compression techniques are currently being investigated for inclusion in the JPEG-2000 standard. In this paper we apply some of these techniques toward the compression of HYDICE data. Two decorrelation techniques, 3D wavelet and Karhunen-Loeve Transform (KLT), were used along with two quantization techniques, scalar and trellis-coded (TCQ), to encode two HYDICE scenes at five different bit rates (4.0, 2.0, 1 .0, 0.5, 0.25 bits/pixel/band). The chosen decorrelation and quantization techniques span the range from the simplest to the most complex multiple component compression systems being considered for inclusion in JPEG-2000. This paper reports root-mean-square-error (RMSE) and peak signal-to-noise ratio (PSNR) metrics for the compressed data. A companion paper [1] that follows reports on the effects of these compression techniques on exploitation of the HYDICE scenes.

This paper describes a methodology we have developed for wavelength band selection. This methodology combines an information theory-based criterion for selection with a genetic algorithm for searching for a nearoptima1 solution. We have applied this methodology to 302 material spectra in the Nonconventional Exploitation Factors (NEF) database to determine the band locations for 7, 15, 30, and 60-band sets that permit the best material separation. These optimal band sets were also evaluated in terms of their utility related to anomaly/target detection using multiband images generated from a Hyperspectral Digital Imagery Collection Experiment (HYDICE) image cube. The optimal band locations and their corresponding entropies are given in this paper. Also presented are the anomaly/target detection results obtained from using these optimal band sets.

As hyperspectral remote sensing technology migrates into operational systems, there is an urgent need to understand the phenomenology associated with the collection parameters and how they relate to the quality of the information extracted from the spectral data for different applications. If such relationships can be established, data collection requirements and tasking strategies can then be formulated for these applications. This paper describes a functional expression or spectral quality equation that has been established for object/anomaly detection in the reflective region (0.4 to 2.5 microns) of the spectrum. This spectral quality equation relates the collection parameters (i.e. spatial resolution, spectral resolution, signal-to-noise ratio, and scene complexity) to the probability of correct detection (Pd) for object/anomaly detection at a given probability of false alarms (Pfa). Follow-on work will be performed to establish a spectral quality equation for material identification.