Taking the advantages of optical camera and Synthetic Aperture Radar (SAR) to perform complementary imaging is a research hotspot in remote sensing image processing, which can be used for target recognition and detection after image registration and fusion. Optical/SAR images acquired by non-common aperture system have temporal and spatial inconsistencies, there are also significant differences in imaging and radiation mechanism, which make the image-domain alignment process complicated, the alignment effect and accuracy are also limited. Therefore, this thesis proposes to acquire synchronal optical/SAR heterogeneous data by optics/frequency separation technology under the airborne co-aperture system, and puts forward a consistent imaging alignment method based on data-domain, which unifies optical photos and SAR imaging results into the same pixel coordinate system, then establishes the theoretical relationship between pixel deviation of the two and the position deviation of SAR imaging, finally achieves consistent imaging alignment with higher resolution optical photos as reference through the deviation compensation during SAR imaging process. Experimental simulations verified the feasibility of achieving consistent imaging alignment of optical camera/SAR within one pixel in distance and two pixels in azimuth based on SAR imaging compensation, which opens up a new direction for the enhancement of imaging processing efficiency of heterogeneous data and high-precision image processing.

The interferometric imaging radar altimeter (InIRA), mounted on the Tiangong-2 space laboratory, utilizes a small incidence and a short interferometric baseline to achieve altimetry for wide swathes of ocean surface topography and inland water surface elevation. To obtain a high-precision digital elevation model (DEM), calibration of the interferometric system parameters is necessary. Because InIRA utilizes the small-incidence interference system design, serious coupling occurs between the interferometric parameters. Commonly used interferometric calibration methods tend to fall into the local optimal solution for InIRA. Because evolutionary algorithms have a stronger robustness and global search ability, they are better suited to handling the solution space structure under the coupling of complex interferometric parameters. This article establishes an interferometric calibration optimization model for InIRA by utilizing the relative flatness of the lake surface as an inequality constraint. Furthermore, an adaptive penalty coefficient constraint evolutionary algorithm is designed to solve the model. The proposed method was tested on actual InIRA data, and the results indicate that it efficiently adjusts interferometric parameters, enhancing the precision of measurements for Qinghai Lake elevation.

This paper presents a new two-stage approach for suppressing strong clutter and detecting moving targets using scatterers’ spatial structure and signal similarity. Compared with the traditional strong clutter suppression methods, the proposed method considers both the spatial similarity and the channel correlation of the scatterers, effectively alleviating the false alarm probability and avoiding the missed detection problem caused via identifying strong moving targets as strong stationary clutter. Additionally, a detector is presented based on the linear degree of the radial velocity interferometric phase (LDRVP) to eliminate false alarms from isolated strong scatter points and the edges of strong scatterers. The experimental results of the X-band radar indicate the presented approach’s lower false alarm probability and superior robustness.

Optical and SAR image registration and fusion are pivotal in the remote sensing field, as they leverage the complementary advantages of both modalities. However, achieving this with high accuracy and efficiency remains challenging. This challenge arises because traditional methods are confined to the image domain, applied after independent image formation. They attempt to correct geometric mismatches that are rooted in fundamental physical differences, an approach that inherently struggles to achieve both precision and speed. Therefore, this paper introduces a co-designed system and algorithm framework to overcome the fundamental challenges. At the system level, we pioneer an innovative airborne co-aperture system to ensure synchronous data acquisition. At the algorithmic level, we derive a theoretical model within the wavenumber domain imaging process, attributing optical/SAR pixel deviations to the deterministic phase errors introduced by its core Stolt interpolation operation. This model enables a signal-domain compensation technique, which employs high-order Fourier series fitting to correct these errors during the SAR image formation itself. This co-design yields a unified processing pipeline that achieves direct, sub-pixel co-registration, thereby establishing a foundational paradigm for real-time multi-source data processing. The experimental results on both multi-point and structural targets confirm that our method achieves sub-pixel registration accuracy across diverse scenarios, accompanied by a marked gain in computational efficiency over the time-domain approach.