Nowadays, various types of foundational models have emerged, showcasing remarkable performance across a multitude of downstream tasks. However, in the domain of hyperspectral image classification (HSIC), substantial research is still required to effectively leverage the advantages of foundational models and adapt them to hyperspectral data. Consequently, we propose a HSIC algorithm based on a fixed-parameter SAM encoder. Specifically, the global feature extraction subnetwork integrates global patch information to obtain processed features. Subsequently, the semantic information extraction subnetwork is trained using cross-entropy to extract semantic features of categories, culminating in pixel-level classification. Experiments on two HSI datasets indicate that the proposed method can obtain better classification performance when compared with seven state-of-the-art methods.

Spaceborne inverse synthetic aperture ladar (ISAL) is an extension of inverse synthetic aperture radar (ISAR) in laser band. It can provide ultrahigh-resolution distance imaging of noncooperative satellites. However, the image quality will be severely degraded by radial and angular vibration phase errors, which are common in satellites. This article proposes an iterative method to jointly compensate for both types of vibration phase errors. Unlike traditional methods relying on prominent points on the target, our approach identifies prominent regions to reduce noise impact, enhancing ISAL imaging suitability. We use short-time Fourier transform (STFT) to generate time-frequency (TF) distributions of these regions. Doppler centroid tracking (DCT) yields instantaneous Doppler curves, independent of strong scattering points. Phase error estimations are derived using a weighted least-squares algorithm. An iteration process is designed to improve the estimation accuracy by adjusting the STFT window length matching with the residual phase errors during iterations. Extensive experiments on simulation and real data confirm the effectiveness and noise robustness of our proposed method.

Accurate range model with acceleration, the coupling phase terms, and spatial-variant (SV) Doppler parameters are the main issues to be solved in high-speed-high-squint SAR (HSHS-SAR) with a curved trajectory. For these issues, an extended Omega-K (EOK) algorithm is developed in this paper. The proposed EOK algorithm mainly includes the following four aspects. Firstly, a modified range model (AMRM) considering three-dimension acceleration for a curved trajectory is established. Then, the coupling between the range and azimuth direction is removed by the modified Stolt mapping (MSM). Subsequently, an improved high-order spatial-variant (SV) phase correction approach is derived to eliminate the azimuth dependence of Doppler parameters. Finally, in order to avoid zeros-padding operation, the proposed method focuses on the sub-aperture data in the range time and azimuth frequency domain through data aligning processing. The experimental results of both simulation and real data verify the effectiveness of the proposed method.

This letter considers the coherent integration problem for a maneuvering target in low signal-to-noise-ratio (SNR) circumstances. Focusing on the range migration (RM) and Doppler frequency migration (DFM) problems caused by the motion of the target, we propose a new method called Radon-general linear chirplet transform (RGLCT). Jointly motion parameters search is employed to obtain the trajectory of the maneuvering target and the coherent integration is achieved via general linear chirplet transform (GLCT). Because of the non-sensitive-to-noise feature of the GLCT, RGLCT can realize weak target coherent integration in very low SNR environments. Multi-target detection can be achieved successfully because the GLCT is not influenced by the cross-term components. Finally, simulations and real data experiments are performed to demonstrate the effectiveness of the method. The results show that the proposed method has superior detection ability than methods including Radon-Fourier transform (RFT), and Radon-Lv’s distribution (RLVD). Both theory and experiments have fully proved that the proposed method can effectively realize coherent integration in low SNR environments.

Deconvolution technique can be utilized in the forward-looking radar (FLR). However, the forward-looking imaging performance degenerates greatly due to the effect of high-speed movement of the platform. In this article, an efficient Bayesian forward-looking superresolution imaging algorithm based on Doppler deconvolution in expanded beam space is proposed. First, the Doppler phase information caused by the high-speed platform is fully exploited and the Doppler matrix is integrated with the antenna pattern. The Doppler convolution model of the echo signal for forward-looking is derived in this article. Then, the Doppler phase information is adopted to perform the Doppler deconvolution. Moreover, an expanded beam space is constructed to enhance the sparsity of the imaging scene. The complex Gaussian distribution and the Laplace distribution have been used to model the distribution characteristics of noise and targets in the imaging scene, respectively. Finally, based on the Bayesian framework, the forward-looking imaging problem is converted into the convex optimization problem. The performance assessment based on simulated and experimental data, also in comparison to the conventional real beam, truncated singular value decomposition (TSVD), iterative adaptive approach (IAA) methods, has demonstrated the effectiveness of our proposed algorithm under high-speed platform scenarios.

Inverse synthetic aperture ladar (ISAL) can perform high-resolution imaging for satellites. However, due to the short wavelength of the laser, satellite micro-vibration will introduce space-variant vibration phase error (SVVPE) and space-invariant vibration phase error (SIVVPE) in the echoes, which seriously blur the ISAL image. In this paper, we propose a noise-robust vibration phase compensation algorithm to accurately estimate and correct these two types of vibration phase errors by frequency descent minimum entropy optimization. Firstly, considering the characteristics of the micro-vibration of satellites, we establish a novel phase error model based on the Fourier series theory, which only contains low-frequency vibration components. The estimation of the phase errors is then translated into the estimation of the model’s Fourier coefficients, which can be achieved by a multi-dimensional minimum entropy optimization. After that, a frequency descent method (FD) is proposed to transform the multi-dimensional optimization into a group of two-dimensional optimizations so that the proposed algorithm can achieve monotonic iterative convergence. In addition, we introduce a solution space adaptive reduction operation to reduce the computational burden when solving the two-dimensional minimum entropy optimizations by the genetic algorithm (GA) to obtain the global optimal solution. Finally, experiments based on the simulated data and the real measured data confirm the effectiveness of the proposed algorithm. Compared with the traditional methods, the proposed algorithm achieves higher phase error estimation accuracy and better image quality.

The microwave photonic (MWP) radar has the capability of generating ultrawideband (UWB) signals. It is a challenge to realize accurate extraction of attributed scattering centers (ASCs) from MWP signals. This manuscript presents a scattering parameter estimation method in the image domain for UWB MWP signals. The polar-to-rectangular resampling is required for UWB MWP signals. Therefore, a range-azimuth decoupled representation based on the ASC model is formed. The model parameter estimation is converted into an optimization problem, where the statistics of the target signal and the features of interest are modeled to provide prior information. The distribution spread maximization (DSM) and peak magnitude maximization (PMM) principles in the optimization embody this prior information. The particle swarm optimization (PSO) is utilized to search for the parameters of each ASC in the image domain. Moreover, the orthogonal matching pursuit (OMP) algorithm is introduced to avoid repeated computation. Experimental results conducted on the simulated data, XPATCH data, and real data confirm the effectiveness of the proposed method. The proposed method takes into account the specific features of UWB MWP signals, which are neglected in the existing studies. Therefore, the proposed method performs better in extracting ASC parameters from UWB MWP signals in terms of accuracy and more complete sets.

A long synthetic aperture passive localization method for two Frequency shift keying (2FSK) signal via azimuth chirp-rate contour is proposed in this paper. By introducing synthetic aperture radar (SAR) imaging technology into passive localization, Doppler frequency change rate of received signal, which is called as azimuth chirp-rate in this paper, is estimated by azimuth focusing. Then, a grid map is formed on the ground and azimuth chirp-rate of each point is calculated to get an azimuth chirp-rate contour map. In the contour map, signal emitter is located in an azimuth chirp-rate curve in which the azimuth chirp-rate value is equal to its estimate. The azimuth chirp-rate contour map of a ground area varies with position of sensor. Therefore, two different azimuth chirp-rate curves can be obtained through different periods of a trajectory and the intersection of the two curves gives estimate of the emitter location.

This paper presents a new method for estimating the baseline error of an azimuth multi-channel SAR antenna in the SAR image domain. In this paper, the expressions of the image domain of multi-channel SAR signals with azimuth baseline errors are derived. The covariance matrix of the image domain signals is obtained by using the joint pixel method. Finally, the least-squares method of image domain is deduced to estimate the azimuth baseline of multi-channel SAR error. Simulation experiments verify the effectiveness of the proposed method.

Traditional clutter suppression methods are mainly studied under the condition that the pulse repetition frequency (PRF) of the system is not less than the Nyquist frequency. Whereas in the high-resolution and wide-swath (HRWS) multichannel synthetic aperture radar (SAR) system, a low PRF is used to break through the minimum antenna area constraint. The low PRF case brings new challenges to the traditional clutter suppression methods. In this letter, a subspace projection clutter suppression method is proposed based on the fact that moving targets and the clutter consist in different signal subspaces. This method can be directly applied to the HRWS multichannel SAR system, and it shows better performance compared to the space-time adaptive processing (STAP) when the moving target components cannot be ignored in the clutter covariance matrix calculation. Simulated data and airborne measured data are processed to verify its effectiveness.

This paper investigates the imaging problems in squint beam-steering synthetic aperture radar (SBS-SAR) mounted on high-speed platforms with constant acceleration. The cross-range-dependent range cell migration (RCM) is compensated by keystone transform (KT) and time domain RCM correction (RCMC). By derotation and phase compensation, the KT of Doppler folded signal is achieved without zero-padding. For azimuth processing, the signal is reconstructed by the nonlinear phase and range-dependent derotation. Then, the space-variant (SV) Doppler chirp rate is corrected by time domain azimuth nonlinear chirp scaling (ANCS). After frequency domain matched filtering, the full aperture signal is focused in the 2-D time domain. The algorithm is validated by simulated SAR data, including the evaluation of RCMC with KT, geometric correction, and the focusing performance.

Due to the very high orbit, the apparent features of geosynchronous synthetic aperture radar (GEOSAR) are the curved trajectory and long integration time, which can lead to severe coupling between the azimuth and the range directions and, therefore, complicates the resolution evaluation. The traditional analytical approach based on the 2-D division may produce large resolution error, and the numerical approach may suffer from huge computation burden. Therefore, an analytical resolution evaluation approach for GEOSAR based on the local feature of the ambiguity function is studied in this paper. The proposed approach is validated with simulation data to be of high efficiency and accuracy. In addition, the proposed approach is also demonstrated to be capable of evaluating the resolution for other complex platforms, and of evaluating the 3-D resolution of a SAR system.

The available focusing algorithms for medium-Earth-orbit (MEO) SAR are all based on the complex nonhyperbolic range equation, which may make it more difficult in imaging processing. In this paper, we model the range equation as the standard hyperbolic form based on the motion compensation (MOCO) principle. However, the conventional two-step MOCO may introduce azimuth spectrum expansion due to the potential large motion error, which can lead to severe azimuth ambiguity. To resolve this problem, we develop an omega-K algorithm based on a modified two-step MOCO and an adaptively straight equivalent (ASE)-velocity model. The algorithm is implemented through three-step processing: 1) the modified two-step MOCO does not compensate for the quadratic motion error (the main factor for the spectrum expansion); 2) an ASE-velocity model is introduced to compensate for the quadratic motion error; and 3) an extended Stolt mapping is proposed to perform the accurate range cell migration correction, and the tandem singular value decomposition-nonlinear chirp scaling algorithm is to correct the azimuth-variant phase error and to perform the azimuth compression. Processing of simulated data and airborne SAR real data validates the effectiveness of the proposed algorithm.

Image formation of large scenes is still challenging in medium-earth-orbit (MEO) synthetic aperture radar (SAR) due to the existence of severe 2-D space variance. In this paper, the properties of space variance are analyzed in detail, and then a variable-coefficient fourth-order range model is adopted to model the space-variant range history of every target in a large scene accurately. A method integrating a modified chirp scaling algorithm with joint time-Doppler resampling is proposed to address the range-variant range cell migration, as well as the azimuth-variant frequency-modulation rate and higher order Doppler parameters. The computational burden and alternative implementation approaches are also discussed. Finally, processing of simulated data for MEO SAR with 2-m resolution is presented to validate the proposed algorithm.

A novel autofocus method for synthetic aperture radar (SAR) image is studied. Based on a quadratic model for the phase error within each sub-area (narrow strip × sub-aperture) after a wide range swath is subdivided into narrow range strips and long azimuth aperture into sub-apertures, an objective function for estimation of the error is derived through the principle of minimum entropy. There is only one unknown variable in the function. With the Chebyshev approximation, the function is approximated as a polynomial, and the unknown is then solved using the method of series reversion. Curve-fitting methods are applied to estimate phase error for an entire scene of the full-swath by full-aperture. Through simulations, the proposed method is applied to restore the defocused SAR imagery that is well focused. The restored and original images are almost identical qualitatively and quantitatively. Next, the method is implemented into an existing SAR data processor. Two sets of SAR raw data at X- and Ku-bands are processed and two images are formed. Well-focused and high-resolution images from plain and rugged terrain are obtained even without the use of ancillary attitude data of the airborne SAR platform. Thus, the studied method is verified.

The high-resolution imaging of a highly squinted synthetic aperture radar remains difficult because of the severe coupling between the range and the azimuth. “Squint minimization” compensates for the range walking in the azimuth time domain, which efficiently increases the orthogonality between the range and the azimuth. However, this “squint minimization” introduces the azimuth space-variant phases, which can be compensated by the azimuth nonlinear chirp-scaling (ANCS) algorithm using large computational loads. In this letter, an azimuth overlapped subaperture algorithm (AOSA) is proposed to compensate for these phases in the Doppler frequency domain. The validity constraint of this algorithm is then analyzed. The AOSA has an advantage over ANCS in terms of the computational load and is considerably more suitable for real-time processing.

In highly squinted synthetic aperture radar (SAR) motion compensation, the map-drift (MD) algorithm cannot estimate the Doppler chirp rate accurately when the range walk is compensated in the azimuthal time domain. This problem stems from the influence of the azimuthal position-dependent (i.e., space-variant) phases, which are introduced by the compensation of the range walk in the azimuthal time domain on the Doppler chirp rate estimation. This letter proposes a space-variant phase-error matching MD algorithm that can improve the precision of estimating the Doppler chirp rate for the highly squinted SAR by removing the influence of the azimuthal position-dependent phases.