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A visible, hyperspectral imager using chromotomography CT) has been built, with the goal of extending the technology to spatially extended sources with quickly varying (> 10 Hz) features, such as bomb detonations and muzzle flashes. Even with a low dispersion, ~0.7 mrad/nm, direct vision prism with undeviated wavelength near λ = 548 nm, spectral resolution of better than Δ λ < 10 nm across the λ = 400 – 600 nm band is demonstrated with spatial resolution of better than 0.5 mm. The primary objective of this paper is to show empirically that the spatial and spectral resolution of data obtained by a simple CT instrument is unchanged in projection space and reconstructed object space.

The Defence Research and Development Canada Agency has successfully completed a Technology Demonstration Program to assess the military utility of airborne hyperspectral Imagery. This required developing a sensor, the Airborne Infrared Imaging Spectrometer (AIRIS), and collecting in-flight imagery data. AIRIS was designed as a flexible instrument using a Fourier Transform spectrometer with a spectral resolution ranging from 1 to 16 cm⁻¹, wide spectral coverage (2 to 12 microns), and different optical configurations. This paper provides a description of AIRIS and discusses examples of the spectral images collected during one air-trial. Emphasis is put on images of sub-pixel targets. Processing AIRIS data is labor intensive and can only be performed during post-trial analysis. Hardware and software modifications to AIRIS will implement a real-time processing capability over the next three years. These modifications will enable the instrument to output radiometrically calibrated digital spectrograms. These spectrograms will then be processed in real-time to output target detection and identification for selected target types.

In this paper, a novel super-resolution method for hyperspectral imagery is proposed by using complex ridgelet transform. A Radon transform is first applied to each band image of a datacube to be enhanced to obtain the Radon slices, and then a 1D dual-tree complex wavelet transform is conducted along each Radon slice to generate coefficients of the complex ridgelet transform. The ordinary ridgelet transform or the finite ridgelet transform (FRIT), however, uses the 1D scalar wavelet transform instead of the dual-tree complex wavelet transform along each Radon slice. The reason why the dual-tree complex wavelet is adopted in this paper is because it has the property of approximate shift invariance, which is very important in image super-resolution. Experiments are conducted in this paper to demonstrate the advantages of the proposed method over the wavelet super-resolution, the FRIT image fusion, and the principal component analysis fusion.

Band selection plays a key role in the hyperspectral image classification since it helps to reduce the expensive cost of computation and storage. In this paper, we propose a supervised hyperspectral band selection method based on differential weights, which depict the contribution degree of each band for classification. The differential weights are obtained in the training stage by calculating the sum of weight differences between positive and negative classes. Using the effective one-class Support Vector Machine (SVM), the bands corresponding to large differential weights are extracted as discriminative features to make the classification decision. Moreover, label information from training data is further exploited to enhance the classification performance. Finally, experiments on three public datasets, as well as comparison with other popular feature selection methods, are carried out to validate the proposed method.

A visible, hyperspectral imager using chromotomography CT) has been built, with the goal of extending the technology to spatially extended sources with quickly varying (> 10 Hz) features, such as bomb detonations and muzzle flashes. Even with a low dispersion, ∼0.7 mrad/nm, direct vision prism with undeviated wavelength near λ = 548 nm, spectral resolution of better than Δ λ< 10 nm across the λ = 400 − 600 nm band is demonstrated with spatial resolution of better than 0.5 mm. The primary objective of this paper is to show empirically that the spatial and spectral resolution of data obtained by a simple CT instrument is unchanged in projection space and reconstructed object space.

The Defence Research and Development Canada Agency has successfully completed a Technology Demonstration Program to assess the military utility of airborne hyperspectral Imagery. This required developing a sensor, the Airborne Infrared Imaging Spectrometer (AIRIS), and collecting in-flight imagery data. AIRIS was designed as a flexible instrument using a Fourier Transform spectrometer with a spectral resolution ranging from 1 to 16 cm⁻¹, wide spectral coverage (2 to 12 microns), and different optical configurations. This paper provides a description of AIRIS and discusses examples of the spectral images collected during one air-trial. Emphasis is put on images of sub-pixel targets. Processing AIRIS data is labor intensive and can only be performed during post-trial analysis. Hardware and software modifications to AIRIS will implement a real-time processing capability over the next three years. These modifications will enable the instrument to output radiometrically calibrated digital spectrograms. These spectrograms will then be processed in real-time to output target detection and identification for selected target types.