Search

Compressed sensing has shown great potential and power in image representation, especially in image reconstruction by sparse representation. Due to complementary information and unavoidable noise existing in synthetic aperture radar (SAR) and other source images, joint sparse representation (JSR) is developed to separate redundancy and complementary information with different properties in source images and obtain a fused image, where image de-noising is done simultaneously owing to that noise is not sparse and cannot be represented by sparse representation. As a result, one noisy remote sensing image fusion method based on JSR is presented in this paper. After obtaining redundant and complementary sub-images by JSR, an improved fusion rule based on pulse coupled neural network (PCNN) is employed to fuse complementary sparse coefficients together. At the same time, because the types of noise in SAR and other source images are different, they can be treated as the complementary information in source images and suppressed at this step. Finally, a fused image can be reconstructed by adding the redundant and fused complementary sub-images. Quantitative and qualitative experimental results show that the proposed method outperforms most of other fusion methods and it is more robust to noise, having better visual effects and values of objective evaluation metrics.

This paper focuses on the range migration (RM) and Doppler frequency migration corrections of the long-time coherent integration, and a fast algorithm is proposed for maneuvering target detection and motion parameters estimation under a low signal-to-noise ratio (SNR). By utilizing the autocorrelation function with respect to the slow time, the newly defined extended keystone transform, scaled Fourier transform, and fast Fourier transform (FFT), the proposed algorithm coherently integrates the target energy into a peak in a three-dimensional (3-D) parameter space. Thereafter, the target’s radial velocity and acceleration are estimated by the peak’s coordinate. After compensating off the RM and DFM via the estimated motion parameters, the inverse FFT along the range frequency and azimuth FFT are used to realize the coherent integration for the target detection. The cross term characteristic is also analyzed and shows the applicability of the proposed algorithm in the scenario of multiple targets. Comparisons with representative algorithms in the computational complexity, motion parameters estimation, and target detection are presented in this paper, which leads us to conclude that the proposed algorithm can greatly reduce the computational complexity with an acceptable integration SNR gain loss. Finally, the experiments with the simulated and real measured radar data are conducted to verify the proposed algorithm.