Research on Target Detection Algorithm of Radar and Visible Image Fusion Based on Wavelet Transform

The target detection rate of unmanned surface vehicle is low because of waves, fog, background clutter and other environmental factors on the interference. Therefore, the paper studies the target detection algorithm of radar and visible image fusion based on wavelet transform. The visible image is preprocessed to ensure the detection effect. The multi-scale fractal model is used to extract the target features, and the difference between the fractal features of the target and the background is used to detect the target. The radar image is denoised by a combination of median filtering and wavelet transform. The processed visible light and radar image are fused with wavelet transform strategy. The coefficients of the low frequency sub-band are processed by the average fusion strategy. The coefficients of the high frequency sub-band are processed using a strategy with a higher absolute value. The standard deviation, the spatial frequency and the contrast resolution of the image fusion result are compared. The simulation results show that the processed image is better than the unprocessed image after the fusion

Target Recognition in Radar Images Using Weighted Statistical Dictionary-Based Sparse Representation

International audienceIn this letter, we present a novel generic approach for radar automatic target recognition in either inverse synthetic aperture radar (ISAR) or synthetic aperture radar (SAR) images. For this purpose, the radar image is described by a statistical modeling in the complex wavelet domain. Thus, the radar image is transformed into a complex wavelet domain using the dual-tree complex wavelet transform. Afterward, the magnitudes of the complex sub-bands are modeled by Weibull or Gamma distributions. The estimated parameters of these models are stacked together to create a statistical dictionary in training step. For the recognition task, we use the weighted sparse representation-based classification method that captures the linearity and locality information of image features. In this context, we propose to use the Kullback-Leibler divergence between the parametric statistical models of training and test sets in order to assign a weight for each training sample. Experiments conducted on both ISAR and SAR images' databases demonstrate that the proposed approach leads to an improvement in the recognition rate