A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters is proposed to avoid the error that is caused by the difference between the real data distribution and the hypothetic Gaussian distribution and avoid the computational burden working in the logistic regression classification directly for hyperspectral data. The multi-fractal spectra and parameters are calculated firstly with training samples along the spectral dimension of hyperspectral data. Secondly, the logistic regression model is employed in our work because the logistic regression classification model is a distribution-free nonlinear model which is based on the conditional probability without the Gaussian distribution assumption of the random variables, and the obtained multi-fractal parameters are applied to establish the multi-class logistic regression classification model. Finally, the Newton-Raphson method is applied to estimate the model parameters via the maximum likelihood algorithm. The classification results of the proposed model are compared with the logistic regression classification model based on an adaptive bands selection method by using theAirborne Visible/Infrared Imaging Spectrometer and airborne Push Hyperspectral Imager data. The results illuminate that the proposed approach achieves better accuracy with lower computational cost simultaneously.

The hyperspectral data and 3D structural data are highly useful in botanical research. But, the two types of information are often acquired separately and hard to be combined. In this work, a novel dual-path configuration based on acousto-optical tunable filter (AOTF) is proposed to acquire an image, structural and hyperspectral information within one acquisition process by a combination of laser triangulation. Under the configuration, the hyperspectral data and the 3D structure can be matched to subpixel level after geometrical calibration. Finally, the obtainment of 3D hyperspectral information in field experiment verifies the feasibility of this imaging system.

To solve the problem caused by jamming, an acousto-optic tunable filter (AOTF)-based imaging spectrometer and a corresponding spatial–spectral discrimination method are proposed for aerial targets. The system has the capability of staring imaging and is electronically tunable, which provides the spatial location and a distinguishable spectral feature in a few images. Since AOTF operates in a frame mode, the spectral brightness of the targets can be predicted by Kalman filtering, like with the motion model. The final target state is updated by using synthetic spatial–spectral information to realize fast decision-making. The results show that the proposed method is more targeted to solve the problem caused by jamming, compared with the traditional energy discrimination method.

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