Hongcheng Zeng

Squinted multi-channel spaceborne synthetic aperture radar (SAR) offers wide-swath coverage and flexible observation geometry. However, imaging algorithms based on chirp scaling (CS) may broaden the range spectrum beyond the available sampling bandwidth, causing range spectrum aliasing and degrading the imaging quality of targets at the range edges of wide-swath scenes. To address this problem, this paper proposes a range-subband imaging method. First, a range spectrum broadening model is established to identify the main factors affecting spectral broadening. A subband division criterion is then derived to determine the minimum number of subbands required to avoid aliasing. Finally, a processing procedure comprising range-subband division, individual subband focusing, and coherent fusion is developed to recover the full-band range-focused signal. Point-target and distributed-target simulations verify that the proposed method suppresses range-spectrum aliasing and preserves full-band range resolution, while a broadside airborne experiment confirms that it introduces no appreciable image-quality degradation.

One-bit quantization technology effectively simplifies the hardware architecture of synthetic aperture radar (SAR) and significantly reduces the volume of echo data, making it a pivotal research direction in spaceborne SAR systems. However, the high computational cost and latency associated with one-bit SAR signal reconstruction have limited the efficiency of downstream target detection applications. To address the demand for efficient ship target detection in one-bit SAR imagery, a label self-supervised ship detection training method on one-bit non-reconstructed SAR images is proposed in this paper, which enables generic deep learning models to perform detection directly in one-bit SAR images based on the chirp scaling algorithm (CSA) without signal recovery during inference. First, a training framework based on curriculum learning is designed, aiming to discriminate between real ship targets and false targets. Second, a one-bit reconstruction and training label self-supervised correction branch is incorporated into the training framework to autonomously generate false target labels and progressively guide the model to learn false target features. Third, a one-bit non-reconstructed image dataset is proposed to validate the trained model, which maps only reliable ship target annotations from full-precision echo imaging results. Finally, experiments conducted on real SAR data demonstrate that the detection model derived from the proposed training method can directly detect ship targets in the non-reconstructed images. Compared with the classic “reconstruction-and-detection” workflow, the trained model runs inference, bypassing the reconstruction process, and achieves a 20-fold improvement in efficiency without compromising detection accuracy. This method provides a practical solution for the rapid acquisition of target information in large-scale ship monitoring using one-bit SAR.

With the advancement of Earth observation technology and the improvement of multisource data acquisition capabilities, heterogeneous remote sensing image change detection technology has become increasingly important. However, existing heterogeneous remote sensing image change detection methods rely largely on fixed network architectures, which makes adapting to complex modal differences and severe noise interference difficult. To address these issues, in this paper, a decoupled differential search-based graph change detection network (DDS-Net) is proposed. First, the model designs a decoupled differential search-based dual-stream graph encoder (DDSGE). By decoupling the search space from the optimization strategy, it automatically optimizes feature extraction operators and graph topologies for different modalities, thereby significantly increasing feature adaptability while reducing computational complexity. Second, to address nonlinear geometric distortions between heterogeneous images, in this paper, a heterogeneous spatiotemporal alignment module that is based on differential localization search (HSTAM) is proposed. This module uses a local soft attention mechanism to dynamically correct registration errors in the feature space. Furthermore, to suppress erroneous graph connections caused by noise, structural consistency and smooth denoising (SCSD) loss is introduced, and deep semantic feedback and graph smoothing regularization constraints are collaboratively used to dynamically generate graphs, thereby effectively increasing the internal consistency of the transformed graph and suppressing misconnection noise. Extensive experimental results demonstrate that this method significantly improves the robustness and accuracy of the model in complex registration error scenarios while maintaining computational efficiency.

Synthetic aperture radar (SAR) is a key all-weather, day-and-night remote sensing technology. However, the growing volume of SAR data from increasing satellite deployments challenges acquisition, storage, and transmission. One-bit quantization can mitigate these issues, yet one-bit imaging algorithms often yield high computational complexity and residual false targets. Even advanced methods like BIHT, despite achieving acceptable quality, remain constrained by these very bottlenecks, which critically limit their utility in large ocean scenes. To address these challenges, this paper proposes a one-bit SAR imaging method based on sparse prior and matrix completion, designed for ocean scenes where targets such as ships present as sparsely distributed isolated scatterers. By exploiting scene sparsity, we construct a two-dimensional matrix observation model and reformulate imaging as a sparse optimization problem solved via iterative matrix completion. Compared to traditional compressed sensing-based one-bit imaging method, the proposed approach effectively reduces the dimensionality of the observation matrix, leading to a significant reduction in computation time. Experimental results demonstrate that the proposed method successfully suppresses false targets in simulated and measured data, achieving imaging quality close to the full precision imaging results with indicators such as peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR). This work provides a practical solution for SAR applications in sparse scenes, such as large-scale maritime ship monitoring.

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.

Synthetic aperture radar (SAR) products are mostly presented as grayscale images, where the scattering characteristics of ground objects lack intuitive visualization, limiting the performance of downstream interpretation tasks. To address this issue, this paper proposes AnisoColSAR, a physics-informed SAR image colorization method. Based on the physical scattering mechanism of ground objects, AnisoColSAR takes the scattering anisotropy index (SAI) as a proxy for physical anisotropy. By quantifying and encoding the SAI into color features, it generates pseudo-color images with explicit physical significance without full-polarization or multi-source auxiliary data. Experiments on the AIR-PolSAR-Seg-2.0 dataset demonstrate that the proposed pseudo-color images stably improve semantic segmentation performance across all polarization modes (HH, VV, HV, VH), with particularly significant gains in cross-polarization modes. This study provides a novel physics-guided approach for SAR image interpretation, and the generated pseudo-color images are promising for various remote sensing tasks such as classification and detection, showing strong practical value.

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.

With the rapid increase in the observation swath of spaceborne synthetic aperture radar (SAR) systems, the volume of echo data has grown dramatically, making the conventional processing paradigm of full-scene imaging followed by target detection highly inefficient in wide-area open-ocean scenarios characterized by strong target sparsity. To address the limitations of existing range-compressed domain detection methods in terms of real data support and target signal integrity, this paper proposes a fast ship detection method in the range-compressed domain based on the ResNet-ID42 network. A Physics Informed Feature Enhancement (PIFE) module is first introduced to strengthen the discriminative representation of range-compressed domain data. Ship targets are then detected through slice-level classification, position localization, and boundary fusion, enabling rapid detection and complete target signal extraction without requiring full-scene imaging. Experimental results on the self-constructed real dataset WORCship-1.0 demonstrate that, under sparse target conditions in wide-area open-ocean scenes, the proposed method achieves a recall of 92.31% while reducing the overall processing time to only 48.1% of that required by representative object detection models. Moreover, more than 98% of non-target regions can be effectively excluded, significantly reducing the data volume for subsequent imaging and processing. Further validation using region-based imaging and image-domain detection shows that, compared with conventional full-scene imaging and detection pipelines, the overall processing efficiency is improved by approximately 14.1×. These results indicate that the proposed method achieves an effective balance among detection efficiency, target coverage capability, and signal integrity in wide-area large-scale oceanic scenarios, providing a practical and efficient solution for spaceborne SAR ship detection and onboard applications.