With the increasing number of remote sensing (RS) data sources, the joint utilization of multimodal data in Earth observation tasks has become a crucial research topic. As a typical representative of remote sensing data, Hyperspectral images (HSI) provide accurate spectral information, while rich elevation information can be obtained from Light Detection and Ranging (LiDAR) data. However, due to the significant differences in multimodal heterogeneous features, how to efficiently fuse HSI and LiDAR data, which remains one of the challenges faced by existing research. In addition, the edge contour information of images is not fully considered by existing methods, which can easily lead to performance bottlenecks. Thus, a joint classification network of HSI and LiDAR data based on Mamba (HLMamba), is proposed. Specifically, a gradient joint calculation (GJA) is first performed on LiDAR data to obtain the edge contour data of the land distribution. Subsequently, a multimodal feature extraction module (MFEM) was proposed to capture the semantic features of HSI, LiDAR, and edge contour data. Then, to efficiently fuse multimodal features, a novel deep learning (DL) framework called Mamba, was introduced, and a multimodal Mamba fusion module (MMFM) was constructed. By efficiently modeling the long-distance dependencies of multimodal sequences, the MMFM can better explore the internal features of multimodal data and the interrelationships between modalities, thereby enhancing fusion performance. Finally, to validate the effectiveness of HLMamba, a series of experiments were conducted on three common HSI and LiDAR datasets. The results indicate that HLMamba has superior classification performance compared to other the state-of-the-art DL methods. The source code of the proposed method will be available publicly at https://github.com/Dilingliao/HLMamba.

Most of the existing synthetic aperture radar (SAR) image superpixel generation methods are designed based on the raw SAR images or artificially designed features. However, such methods have the following limitations: (1) SAR images are severely affected by speckle noise, resulting in unstable pixel distance estimation. (2) Artificially designed features cannot be well-adapted to complex SAR image scenes, such as the building regions. Aiming to overcome these shortcomings, we propose a multitask learning-based superpixel generation network (ML-SGN) for SAR images. ML-SGN firstly utilizes a multitask feature extractor to extract deep features, and constructs a high-dimensional feature space containing intensity information, deep semantic informantion, and spatial information. Then, we define an effective pixel distance measure based on the high-dimensional feature space. In addition, we design a differentiable soft assignment operation instead of the non-differentiable nearest neighbor operation, so that the differentiable Simple Linear Iterative Clustering (SLIC) and multitask feature extractor can be combined into an end-to-end superpixel generation network. Comprehensive evaluations are performed on two real SAR images with different bands, which demonstrate that our proposed method outperforms other state-of-the-art methods.

The traditional two-dimensional multiplication modulation jamming (2D-MMJ) method is widely used in synthetic aperture radar (SAR) deception jamming due to its simple hardware implementation and convenient template preparation. However, it suffers from serious defocusing in high-resolution spaceborne SAR deception jamming. To improve the fidelity of deception jamming, this paper proposes an improved 2D-MMJ method based on defocus correction (2D-MMJDC). First, the defocus correction phase is designed by analyzing the range cell migration (RCM) curves between the real and jamming signals. Next, considering the false point target azimuth deviation caused by the defocus correction, the azimuth deviation correction phase is designed. Finally, two phases are added to the 2D-MMJ method, and a template preparation process based on the two-dimensional Fast Fourier Transform (2D-FFT) is designed. Simulation results show that the 2D-MMJDC method effectively solves the problem of defocusing on the 2D-MMJ method and greatly improves the fidelity of the deception jamming.

The existing single-frequency TomoSAR three-dimensional (3D) reconstruction method suffers from insufficient observation information due to the high cost and long period of acquisition of the required multi-pass data. To effectively alleviate these problems, it is a feasible solution that a sensor carries dual-frequency radars that can work simultaneously and acquire dual-frequency data for 3D imaging. This paper systematically introduces the theory and model of dual-frequency TomoSAR 3D imaging system, and proposes a dual-frequency distributed compressive sensing (DF-DCS) TomoSAR method for reducing flight requirements. Firstly, this method combines the neighborhood pixel information to effectively improve the signal-to-noise ratio. Then, the reflectivity profiles of the dual-frequency data are jointed to increase the unambiguous height and the accuracy of the target position estimation. Finally, the point-target simulation experiments and surface-target experiments based on SARSIM system verify the effectiveness of the proposed method. With the reconstruction accuracy maintained, the DF-DCS method can effectively improve the ability to resolve ambiguity and reduce the required flights.

The geometric positioning accuracy of synthetic aperture radar (SAR) images is affected by the radar system measurement and processing parameters, platform ephemeris data and auxiliary DEM data. Geometric calibration can compensate for the SAR systematic parameter errors to produce high-precision SAR products. SAR images of mountainous areas are difficult to be used for geometric calibration due to severe geometric distortion and difficulty in deploying and extracting control points. In this paper, a novel and effective geometric calibration method for rugged terrain is proposed. The proposed method exploits the unique geometric and radiometric distortion features of SAR images. Based on the energy variation of SAR orthophoto, the calibration of range and azimuth parameters is achieved by searching for the fast time and slow time offsets that can compensate for the systematic errors. Experiments based on real SAR images from LuTan-1 satellite were conducted. The proposed method shows good performance in the experiments. After geometric calibration, the root mean square error of LuTan-1 L1A images is reduced from 80.77 m to 6.39 m, which verifies that the proposed method can improve the positioning accuracy of SAR images. The proposed method is simple to realize and does not rely on additional control point data, which provides a new solution for the maintenance and parameter calibration of SAR satellites.

This paper aims to perform imaging and detect moving targets in a 3D scene for space-borne air moving target indication (AMTI). Specifically, we propose a feasible framework for distributed LEO space-borne SAR air moving target 3D imaging via spectral estimation. This framework contains four subsystems: the distributed LEO satellite and radar modeling, moving target information processing, baseline design framework, and spectrum estimation 3D imaging. Firstly in our method, we develop a relative motion model between the satellite platform and the 3D moving target for satellite and radar modeling. In a very short time, the relative motion between the platform and the target is approximated as a uniform motion. We then establish the space-borne distributed SAR moving target 3D imaging model based on the motion model. After that, we analyze the influencing factors, including the Doppler parameters, the three-dimensional velocity, acceleration, and baseline intervals, and further investigate the performance of the 3D imaging of the moving target. The moving target spectrum estimation 3D imaging finally obtains the 3D imaging results of the target, which preliminarily solves the imaging and resolution problems of slow air moving targets. Simulations are conducted to verify the effectiveness of the proposed distributed LEO space-borne SAR moving target 3D imaging framework.