Aiming at the nonlinear and non-Gaussian features of the real infrared scenes, an optimal nonlinear filtering based algorithm for the infrared dim target tracking-before-detecting application is proposed. It uses the nonlinear theory to construct the state and observation models and uses the spectral separation scheme based Wiener chaos expansion method to resolve the stochastic differential equation of the constructed models. In order to improve computation efficiency, the most time-consuming operations independent of observation data are processed on the fore observation stage. The other observation data related rapid computations are implemented subsequently. Simulation results show that the algorithm possesses excellent detection performance and is more suitable for real-time processing.
To meet the requirement of fine vegetation classification in hyperspectral remote sensing applications, an improved method based on C5.0 decision tree of multiple combined classifiers is proposed. It consists of 2 classification stages: rough classification and fine classification. During the first stage, experimental model is used to estimate vegetation biochemistry parameters. Then 3 supervised classifiers, namely Spectral Angle Mapping, Minimum Distance, and Maximum Likelihood, combined by C5.0 decision tree, are used to realize the final fine classification. Experiments show that comparing with the traditional mono-classification algorithms, the proposed method can reduce the classification error effectively and more suitable for the vegetation investigation in the hyperspectral remote sensing applications.
Aiming at the infrared object detection applications, a novel generalized cumulative sum processing is presented. Since in a typical IRST application system, object appearing and vanishing can be regarded as the change-point detection problem in Statistics. One of the effective solutions is the generalized cumulative sum processing (GCUSUM). Analyses are focused on the detection threshold value selection of GCUSUM algorithm and relations among the threshold value and false alarm rate, detection probability and signal-noise rate. The further researches extend a uniform band IRST system into the multiple band IRST system and improve the realization of GCUSUM algorithm. Results of theoretical analysis and simulation show that our modified algorithm has excellent object detection performance in an infrared image sequences from a real IRST system.
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