An automatic SAR and optical image registration approach based on linear features and neural network is proposed. First, fuzzy linear feature extraction algorithm is used and common straight line segments between SAR and optical images are kept for matching primitives. Then discrete relaxation method is adopted to get acceptable matched primitives of two images and the crossing points of these matched line segments are taken as control points of image registration. Lastly, neural network is employed to realize theimage transformation and resampling. The experimental results are given and show that the proposed image registration approach can resolve the registration of SAR and optical images including long and thin objects effectively.
Scene matching navigation based on multisensor image fusion is studied in this paper. Pixel and low feature -level fusion images of optical and IR images are used as real time images matching with optical satellite images as base images, in which linear superposition, nonlinear operators and multiresolution image fusion approaches are adopted to acquire the fused gray and edge strength images. Gray and low feature -level scene matching schemes are also employed to execute the scene matcing simulation experiments on the real flight image data, in which scene matching methods are general CCF and MAD algorithms. The experimental results are given to compare the matching performance when taking different fusion images as real time images under certain matching schemes. The scene matching results based on single sensor images are also given for comparison with the results based on multisensor fusion images.
Hierarchical image fusion approaches for CCD/IR images are studied and the performance analysis of these fusion approaches is completed in this paper. The Hierarchical fusion methods include FSD pyramid, Laplacian pyramid, ration pyramid, contrast pyramid, gradient pyramid, morphological pyramid and discrete wavelet transform. The performance measures of evaluating fusion images deal with standard deviation, entropy, cross entropy and spatial frequency. Experimental results show that morphology pyramid method is more suitable for CCD/IR image fusion than other ones proposed in this paper.
In this paper, an approach to linear feature extraction for infrared image is presented, which consists of three major modules: image preprocessing by using fuzzy feature transformation and fuzzy enhancement, edge strength map and direction map generation by using low pass filter and multi- dimension edge detector and linear feature extraction by using gradient profile maximum method. Comparison with some other edge detection methods, accuracy and robust experiments are done to testify its better position accuracy and reliability. Computational simulation and experiment results show that the proposed algorithm can solve the linear feature extraction problem for infrared image.
A linear feature extraction method for infrared image is presented in this paper, which includes three major steps: image preprocessing using fuzzy feature transformation and fuzzy enhancement, edge strength map and direction map profile maximum method. Comparison with other edge detection methods, accuracy and robust experiments are made to prove its better position accuracy and reliability. Results show that the proposed algorithm can solve the linear feature extraction problem for infrared image.
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