We focus on the range migration (RM) and Doppler frequency migration (DFM) corrections in the long-time coherent integration, and a fast detection method based on two-dimensional trilinear autocorrelation function is proposed for the maneuvering target with jerk motion. This proposed method can integrate the echoes’ energy into peaks in a three-dimensional parameter space coherently and estimate the target’s radial range, acceleration, and jerk simultaneously by the peak detection technique. Then through the estimations of radial range, acceleration, and jerk, the radial velocity can be obtained through one-dimensional parameter searching. Finally, RM and DFM can be compensated simultaneously, and the target can be detected through the constant false alarm technique. This proposed method can strike a good balance between the computational complexity and detection performance. Experiments with the simulation and real measured radar data are conducted to verify the proposed method.
We propose a tensor representation for polarimetric synthetic aperture radar data and extend the usage of tensor learning technique for feature dimension reduction (DR) in image classification. Under the tensor algebra framework, each pixel is modeled as a third-order tensor object by combining multiple polarimetric features and incorporating neighborhood spatial information together. A set of training tensors are determined according to the prior knowledge of the ground truth. Then a tensor learning technique, i.e., multilinear principal component analysis, is applied on the training tensors set to find a tensor subspace that captures most of the variation in the original tensor objects. This process serves as a feature DR step, which is critical for improving the subsequent classification accuracy. Further, the projected tensor samples after DR are fed to the k-nearest neighbor classifier for supervised classification. The performance is verified in both simulated and real datasets. The extracted features are more discriminative in the feature space, and the classification accuracy is significantly improved by at least 10% compared with other existing matrix-based methods.
Multisensor image fusion can be used as an advanced technique for image enhancement. A fusion method based on region division strategy is proposed for synthetic aperture radar (SAR) and color visible images. The SAR target feature detection function is first constructed to divide the images into the SAR target region and non-SAR target region. Meanwhile, SAR image despeckling is considered during the generation of this detection function. Then, two different fusion rules are designed for these two regions. More specifically, the fusion rule adopted in the SAR target region is fusing the high-frequency feature within the SAR image, and the fusion rule employed in the non-SAR target region is maintaining the spectral and detail information of the color visible image. Experimental results on several pairs of SAR and color visible images demonstrate the effectiveness of the proposed method. Compared with the conventional image fusion methods, the proposed method provides better results for all evaluation criteria, including spectral distortions, feature similarity, mutual information, and peak signal-to-noise ratio.
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