Synthetic aperture radar (SAR) is an important tool for observing the oceanic internal wave phenomenon. However, owing to the unstable imaging quality of SAR on oceanic internal waves, the texture details of internal wave images are usually unclear, which is not conducive to the subsequent applications of the images. To cope with this problem, a texture enhancement method for oceanic internal wave SAR images is proposed in this paper, which is based on non-local mean (NLM) filtering and texture layer enhancement (TLE). Since the strong speckle noise commonly present in internal wave images is simultaneously enhanced during texture enhancement, resulting in degraded image quality, NLM filtering is first performed to suppress speckle noise. Then, the denoised image is decomposed into the structure layer and the texture layer, and a texture layer enhancement method oriented to the texture characteristics of oceanic internal waves is proposed and applied. Finally, the enhanced texture layer and the structure layer are combined to reconstruct the final enhanced image. Experiments are conducted based on the Gaofen-3 real SAR data, and the results demonstrate that the proposed method performs well in suppressing speckle noise, maintaining overall image brightness, and enhancing internal wave texture details.

Spaceborne bistatic Frequency Modulated Continuous Wave (FMCW) Synthetic Aperture Radar (SAR) faces several challenges, including the nonlinear motion trajectory of the satellite platform, intra-pulse motion of the transmitting and receiving satellites, spacevariant characteristics of echo signals, and the breakdown of azimuth shift invariance. The “stop-and-go” hypothesis is no longer applicable in spaceborne bistatic FMCW SAR. In this paper, an extended ground Cartesian back-projection (EGCBP) algorithm for bistatic FMCW SAR is proposed under the “non-stop-and-go” hypothesis. First, to address the peak positioning deviation of the range migration curve caused by intra-pulse modulation effects, the least squares method is employed to perform range-dimensional linear fitting of the signal propagation delay, thereby obtaining the theoretical peak position of the range migration curve under the “non-stop-and-go” hypothesis. Second, since the traditional GCBP algorithm is applicable only to mono-static systems, the spectrum compression function is enhanced to accommodate bistatic systems. Prior to this, to overcome the high computational cost of echo simulation using the iterative method under the “non-stop-and-go” hypothesis, a signal propagation delay approximation method (SPDAM) is introduced. This method not only meets accuracy requirements but also significantly im-proves simulation efficiency, providing a foundation for subsequent verification of the imaging algorithm. Simulation experiments involving both point and scene targets are provided to demonstrate the effectiveness and potential of the proposed algorithms for practical applications.

Spaceborne interrupted frequency-modulated continuous-wave synthetic aperture radar (iFMCW SAR) employs a single antenna on a single spacecraft operating in a time-division transmit/receive mode, effectively avoiding mutual interference between transmitted and received signals and thereby overturning the design paradigm of spaceborne FMCW SAR systems. However, the periodic switching of the antenna between transmit and receive states results in periodic data gaps along the azimuth direction in the echo signal, leading to spurious artifacts in the reconstructed images and severely degrading image quality. Sparse signal recovery techniques based on compressive sensing models have been shown to effectively suppress such spurious targets. Nevertheless, the generalized orthogonal matching pursuit (GOMP) algorithm requires prior knowledge of the signal sparsity, a condition that is often impractical in real-world scenarios. To address this limitation, this paper investigates the variation pattern of the residual norm with respect to sparsity in the GOMP algorithm and proposes a modified GOMP algorithm based on binary search. This approach enables rapid and accurate determination of the true sparsity level without prior knowledge, thereby achieving sparsity-adaptive reconstruction with GOMP and significantly enhancing the imaging quality of iFMCW SAR. Simulation experiments involving both point and scene targets are provided to demonstrate the effectiveness and potential of the proposed algorithms for practical applications.