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.

Repositioning error (RE) compensation is one of the key steps in discontinuous ground-based synthetic aperture radar (D-GBSAR) monitoring. The traditional RE compensation is to perform 2-D phase unwrapping, and then remove the RE based on the least squares method, thereby introducing an extra unwrapping error. Specifically, due to the phase wrapped of the discrete permanent scatterers (PS), the least squares method is intractable to be performed directly. Hence, the core idea of this paper is to propose a new likelihood function model, and straightforwardly estimate the baseline parameters to compensate for the RE, avoiding the phase unwrapping. Firstly, we transform the discrete PS phase wrapped into a continuous function model, reducing the complexity of mathematical analysis. Then, based on the novel RE model in the context of Gaussian white noise, we obtain a concise mathematical expression of the Cramer-Rao lower bound (CRLB) for maximum likelihood estimation, which serves as the performance indicator for baseline estimation. Afterward, by introducing the Newton iteration method, we obtain the baseline estimation results and integrate a novel RE compensation deformation inversion processing methodology for D-GBSAR, named maximum likelihood-Newton iteration-RE compensation algorithm (MLNIRECA). Last but not least, the effectiveness of the proposed method is verified through simulation and real data experiments, where the root mean square error is constantly close to the CRLB with the increase of signal-to-noise ratio (SNR) when the SNR is greater than -10 dB. Particularly, we can extend the spatial baseline to 100 mm under the condition of accuracy requirements, and employ the proposed methodology to achieve sub-millimeter deformation monitoring accuracy over actual scenarios in time and space.

In order to compensate for the repositioning error introduced by the radar position offset in discontinuous GBSAR monitoring, a new mathematical framework for modeling the baseline error based on the Taylor expansion is developed in this letter. And then, a novel three-dimensional model called Multiparameter Nonlinear Trigonometric Model (MNTM) is proposed to accurately compensate for the repositioning error. Furthermore, to improve the compensating efficiency, we further develop an efficient two-dimensional model called Linear Trigonometric Model (LTM). Both simulation and field experiments verify the superiority and feasibility of the proposed methods, which measure the displacement with sub-millimeter accuracy.

In this paper, we propose a dual-frequency Synthetic Aperture Radar Tomography (TomoSAR) method with fewer flights for efficient urban building reconstruction. By utilizing dual-frequency signals, our method overcomes the limitations of traditional single-frequency TomoSAR in terms of data acquisition time and temporal decoherence. The proposed dual-frequency TomoSAR can achieve imaging results comparable to single-frequency methods with only half the number of flights required. We present an imaging model for this method and derive the theoretical accuracy of the imaging model. Theoretical analysis shows that the proposed dual-frequency TomoSAR can achieve imaging results that are approximately comparable to single-frequency methods with half the number of flights, significantly reducing data acquisition time. Furthermore, the dual-frequency approach mitigates the impact of temporal decoherence, enhancing the practical applicability of TomoSAR for urban building reconstruction. Simulations at 7.2 GHz and 9.6 GHz show our method achieves results comparable to single-frequency methods with half the flights. Furthermore, our method attains 8.002-meter accuracy due to its deblurring capability, surpassing the 8.882-meter and 14.054-meter accuracies of single-frequency methods. These numerical results highlight the effectiveness of the proposed dual-frequency TomoSAR in delivering high-quality reconstructions with reduced flight requirements.

MIMO ground-based synthetic aperture radar (GB-SAR) have experienced rapid development in recent years because they can rapidly acquire echo data from observed scenes neglecting mechanical motion and thus can monitor the entire geological deformation process ranging from slow changes to rapid instabilities. However, the unique alternating transceiver configuration of MIMO GB-SAR systems presents rapidly varying periodic characteristics of the channel amplitude and phase errors, leading to severe paired false targets in the imaging results. Hence, their estimation and compensation accuracy requirements are extremely high due to the energy concentration properties of periodic amplitude and phase errors. In response to these concerns, this study develops a parameter model for the amplitude and phase errors of the transceiver channels and proposes an amplitude and phase errors parameter estimation method based on the minimizing of image entropy. The proposed algorithm involves imaging, pre-correction, and fine correction. Specifically, a non-interpolated sub-aperture imaging algorithm is proposed, then the amplitude and phase error parameter model is established, and pre-correction is performed using the model to reduce the number of iterations required for parameter estimation optimization. Finally, based on the parameter model, the amplitude and phase errors are estimated according to the entropy minimization criterion. Experimental results demonstrate that the proposed method can accurately estimates the channel phase cycle errors, significantly suppresses azimuth ambiguities, and achieves appealing focusing performance for the images.

The accurate interpretation of ocean waves in synthetic aperture radar (SAR) images is challenging because the scattering of moving waves causes various complex mechanisms. Moreover, SAR imaging is based on a unique nonlinear mapping relationship. This study proposed a full-link simulator to simulate the main parts of ocean surface backscattering, ocean surface motion, platform motion, echo generation and acquisition, SAR imaging, etc. The proposed simulator demonstrated the dynamic characteristics of the ocean surface by simulating the movement and decoherence effect of scattering cells within each radar pulse. Subsequently, a method for retrieving the wave direction spectrum from the SAR image spectrum based on the simulator was developed. The simulator, which can accurately reflect the modulation of backscattering, velocity bunching effect, and decoherence effect, was used as the forward transformation from the ocean wave spectrum to SAR image spectrum. In addition, the Levenberg-Marquardt iterative method was applied to determine the optimal solution through adjustment of several key parameters of the wave spectrum and ocean surface coherence time. Further, the Sentinel-1 SAR data were used to verify the accuracy of the full-link simulator and the effectiveness of the retrieval scheme. The results showed the following. 1) The SAR image with the simulation of both the moving scene and decoherence effect was highly consistent with the SAR image observed by Sentinel-1. 2) The retrieval scheme exhibited strong correction ability, thereby effectively improving the consistency between the retrieved and observed SAR spectra. In addition, the wave parameters (significant wave height, mean wave period, and dominant wave propagation direction) calculated considering the retrieved wave spectrum were consistent with the buoy measurements.