Forest height is a critical component of ecological and environmental assessments, playing a vital role in modern forest management, biomass estimation, and climate modeling. This manuscript presents a novel single polarization forest height inversion (SPFI) method that uses only single polarization interferometric synthetic aperture radar (InSAR) data without the assistance of auxiliary information. In the SPFI method, the probability density functions (pdfs) of forest height and terrain phases are constructed, followed by the estimation of the differences between adjacent forest heights using the phase gradient, and then the absolute forest heights are obtained by the integration of the relative forest heights. The effectiveness of the SPFI method is validated through experiments conducted at three distinct test sites, using airborne P-band InSAR data and spaceborne L-band InSAR data.

The differential interferometric synthetic aperture radar (DInSAR) technique can estimate the Earth’s surface displacement theoretically at centimeter- or millimeter-level accuracy, where the 2-D phase unwrapping (PU) is ineluctable, so that improper PU algorithm would deteriorate the accuracy. Conventional single-baseline (SB) PU-based DInSAR cannot accurately monitor high-gradient deformation due to the limitation of the phase continuity assumption. The conventional multibaseline (MB) PU-based DInSAR, including multifrequency-based two-pass DInSAR and two-stage programming approach (TSPA)-based three-pass DInSAR, is capable of delineating high-gradient deformation but imposes stringent requirements on InSAR data. Under this condition, we present a multitemporal baseline PU approach (MTBA) for two-pass DInSAR from single radar system such that even high-gradient deformation can be delineated at centimeter- or millimeter-level accuracy. The MTBA demonstrates to be valid through a simulation experiment and three strike-slip earthquake events. Furthermore, the MTBA expands the perpendicular baseline-based MB PU for topography mapping into the temporal baseline-based MB PU domain for deformation monitoring, potentially enriching the MB PU theory and extending the application of DInSAR technique.

The hybrid along-track (AT)/cross-track (XT) synthetic aperture radar interferometry (InSAR) system enables the measurement of radial sea surface velocity, where solving it through phase unwrapping (PU) is intrinsically ill-posed due to the coupling of velocity-induced and height-induced phase components. Conventional hardware-based methods impose stringent and costly system requirements, while auxiliary-data-dependent approaches are error susceptible. Under this condition, we innovatively propose an along-track two-stage programming approach (AT-TSPA) multibaseline (MB) PU method within the three-pass differential InSAR (DInSAR) framework. AT-TSPA addresses the ill-posedness by making use of the normal baseline diversity, thus enabling accurate and robust retrieval of sea surface velocity without relying on hardware upgrades or external auxiliary data. Both theoretical analysis and experimental results demonstrate that AT-TSPA is effective and practical for accurately measuring sea surface velocity under complex oceanic conditions. AT-TSPA is also applicable to other fields involving coupled velocity and height phase components, such as sea ice velocity estimation. Furthermore, AT-TSPA extends the theory of TSPA-based MB InSAR from terrestrial to oceanic domains, thereby advancing the applicability and practicability of well-posed InSAR techniques.

Accuracy assessment of interferometric synthetic aperture radar (InSAR) products without external reference data (ERD) has been a long-standing challenge because traditional phase unwrapping (PU) is an ill-posed problem. The limitations in accuracy assessment make it impossible to verify the precision of the PU results under actual observation conditions, so that the reliability of InSAR products in practical applications is unknowable. However, multibaseline (MB) PU is well-posed, so its accuracy can be evaluated in a statistical sense without using ERD, i.e., even if there are no in situ data, the accuracy of the products generated by MB InSAR can still be assessed theoretically. In this article, by obtaining the closed form optimality condition of the Chinese remainder theorem (CRT) optimization model, the new independent quantitative index for MB PU accuracy evaluation was mathematically established. Interestingly, we found that: 1) the optimality condition is a sufficient and necessary condition for the accuracy assessment of the MB PU and 2) it is affected by the normal baseline lengths of the MB InSAR system and the interferogram noise intensity. To practically apply this mathematical condition, a deep convolutional neural network (DCNN) was developed to refine MB InSAR product accuracy. The validity and effectiveness of the proposed approach have been systematically verified using simulated and acquired interferometric datasets.

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) possesses unique advantages in forest parameter retrieval due to its all-weather, all-day observation capability and effective acquisition of vertical structure information of ground targets. Based on the Random Volume over Ground (RVoG) model, the existing three-stage method fits the coherent line to estimate the ground phase. However, the accuracy of tree height inversion is restricted by noise during the estimation process. A new ground phase estimation method was proposed by fitting coherent lines using multiple pixels, improving the SNR. Results demonstrate that, compared to the existing three-stage algorithm, our method performs better in the tree height inversion of managed and natural forests.

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) is a combination of polarimetric SAR and interferometric SAR, possessing both the sensitivity of interferometric SAR to the vertical information of objects on the ground and the sensitivity of polarimetric SAR to the geometric morphology and dielectric constant of objects. Therefore, it is a crucial technology for inverting forest structural information.Currently, the mainstream approach for forest tree height inversion continues to explore the role of polarimetric information in the inversion process. However, the important role of interferometric SAR in this process is often overlooked. This paper innovatively proposes the estimation of the tree height gradient maximum likelihood function. It introduces a Maximum Likelihood approach for Tree-height Estimation (ML-TE) that directly extracts tree height information from interferograms. Experimental results demonstrate the effectiveness of this method in tree height inversion.

Global navigation satellite system-based bistatic synthetic aperture radar interferometry (GNSS-based InBSAR) suffers from low image resolution, low signal-to-noise ratio (SNR), and accumulated satellite baselines because navigation satellites are used as transmitters. Therefore, traditional texture-based and leader image fixed image registration and persistent scatterer (PS) point selection algorithms cannot be adopted. In this article, a sequential image registration algorithm is proposed based on the preselected PS (prePS) point association for GNSS-based InBSAR systems. First, the prePS points are selected based on the coherence coefficient between theoretical and actual resolution cells. Then, image registration is achieved through prePS point association and subresolution offset estimation, and sequential registration is adopted to replace traditional leader image fixed registration. Finally, PS point selection and interferometric phase extraction are implemented based on the analysis of the resolution cells. Raw data from BeiDou navigation satellites are used to indicate the effectiveness of the proposed algorithm in GNSS-based InBSAR.

In this paper, the surface deformation of Jiuzhaigou County is analyzed. The ascending ALOS-2 data of 15 scenes from December, 2017 to September, 2019 is used, and the results of surface deformation in time series are extracted by the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. The monitoring results show that the overall deformation rate of Jiuzhaigou is between −10mm/a∼ 10mm/a, which is in a stable state; 261 potential geological hazard points are extracted with an area of about 21.2km2. Field investigation verifies the reliability of SBAS-InSAR technique for surface deformation monitoring. In addition, the factors affecting the development of geological disasters are also discussed.

To reconstruct the spectrum of a wide band signal by stepped-frequency chirp signals, the mismatch between sub-band signals should be compensated. In this paper, a novel method is proposed to estimate the phase error between adjacent sub-band signals based on the overlapped spectra. It is very computationally efficient and effective without increasing the hardware load. Finally, the simulated and real data confirm the validity of the method.

An novel imaging algorithm for multiple elevation beam (MEB) synthetic aperture radar (SAR) systems with channel phase errors is presented. Considering their effect on the imaging result, the estimation of channel phase errors is converted into the estimation of the direction of arrive (DOA) angles of the received signal, and the sparse-based algorithm is adopted. After obtaining the DOA angles, the overlapped subpulses can be separated. Then the traditional imaging method can be utilized to achieve the high resolution SAR image. Finally, the simulation result confirms the proposed method.

Synthetic aperture radar (SAR) target configuration recognition is a challenging task, and the key point is to realize effective feature extraction. An algorithm combing the advantages of sparse graph and locality preserving projections (LPP) is proposed to achieve SAR target configuration recognition. Taking the merits of sparse representation (SR) into consideration, an affinity matrix is established to realize effective structure preserving of the dataset. Besides, the problem of matrix singularity in LPP is effectively resolved by diagonal loading. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database validate the effectiveness and superiority of the proposed algorithm.

Focusing on the problem of the real-time implementation in sparse representation (SR) based recognition algorithm, a fast sparse representation (FSR) algorithm is presented in this paper to improve the efficiency of synthetic aperture radar (SAR) target configuration recognition. Taking the inertia variance characteristic of SAR target images over a small range of azimuth angles into consideration, training samples of each configuration are averaged. Instead of using all the training samples to establish the dictionary in SR, the average samples are utilized to construct the dictionary in FSR. A small dictionary accelerates the speed of the proposed algorithm.

It is very difficult for the millimeter wave (MMW) seeker to detect and capture the target. Tracking the target unstably, even losing the target happens frequently. Focusing on the problem, a simple but effective target recapture method is presented for narrow-beam MMW seeker in this paper. The parameters outputted by the inertial navigation system (INS) and the seeker are utilized to deduce the coordinates of the target. And then, target searching is implemented again on the basis of the deduced coordinates. The target recapture time can be dramatically reduced by using the proposed method, thus guaranteeing enough terminal guidance time. The effectiveness of the proposed method is verified by the mooring test-fly experiments.

Focusing on the problem of the millimeter wave (MMW) seeker performance evaluation, which plays an important role for the terminal control algorithm design, an evaluation method is proposed based on the real-time kinematic (RTK) for moving targets. Firstly, time synchronization is realized for different global position system (GPS) carrier platforms taking a controller as the reference. And then, the key parameters associated with the guidance control are calculated on the basis of the GPS measurements. Finally, parameter comparisons are implemented by using the calculated values and the seeker's outputs. The effectiveness of the proposed MMW seeker evaluation method is verified by the mooring test-fly experiments.

Polarimetric NASA/JPL UAVSAR imagery for the curved and graded Coronado Bridge over San Diego Bay, California, USA was analyzed. The bridge was shown with three types of features. Feature 1 consisted of nearly even distributed dots, feature 2 dots and a curved segment, and feature 3 a continuous curved segment. On the basis of the SAR image geometry and polarimetric decomposition method, dots of feature 1 were produced by the double-bounced interactions of the bridge surface and light poles on the far side of the bridge toward the SAR. Dots of feature 2 were from the double-bounced interactions of the ocean surface and light poles on the near side toward the SAR. The curved segment of feature 2 came from the double-bounced interactions of the ocean surface and side of the bridge facing the SAR. Curved segment of Feature 3 was the multiple interactions of the ocean surface and bottom of the bridge.

The influence of the azimuth angle and water surface roughness determined by wind speeds on the SAR imaging of a metallic bridge over water was simulated after the identification of the double- and triple-bounced radar returns. Different bounced returns were caused by interactions of the bridge surface and water surface. In the simulation, the radar wavelength was L-band. The azimuth angle was between 0 and 45°. The wind speed was from 0 to 40m/s. The Pierson-Moskowitz spectra were used to model the spatial spectral energy distribution of the sea surface. The Kirchhoff approximation algorithm was used to compute the backscatter. With the noise level of a SAR system at -30dB, three regions were delineated. In region I, both double- and triple-bounced returns existed. There were only double-bounced returns in region II. None of the double- and triple-bounced returns was in region III.

Locality preserving projections (LPP) can preserve the local structure of the datasets effectively. However, it is not capable of separating the samples that are close to each other in the high-dimensional space but belong to different classes. Focusing on the problem, a class-dependent locality preserving projections (CDLPP) algorithm is proposed in this paper. The class information is embedded into the LPP model, and the similarity matrix and the difference matrix are constructed according to the class information. The similarity matrix is utilized to preserve the local structure of the samples belong to the same class, whereas the difference matrix is utilized to separate the samples that are close to each other in the high-dimensional space but belong to different classes. Experiments are conducted using the moving and stationary target acquisition and recognition (MSTAR) database, the results verify the effectiveness of the proposed algorithm.

The performance of the seeker highly influences the design of the control algorithms and the attack precision of the missile. Before the missile with seeker mounted on is launched, the performance of the seeker needs to be accurately evaluated, especially for the expensive ones. Focusing on the problem, a millimeter wave seeker evaluation method is proposed based on the differential global positioning system (DGPS) principle. Firstly, the parameters of the line-of-light (LOS) rates and the missile to target distance are calculated with the data obtained by the DGPS. Then, the results are compared to the ones that are outputted by the seeker itself. The effectiveness of the proposed algorithm is verified on the real seeker data, comparisons with the inertial navigation system (INS) further demonstrate the advantage of the proposed method.

High resolution synthetic aperture imaging using rotating fan-beam scatterometers is studied. First, the working mode is presented. Then, the relationships among pulse repetition frequency, the number of coherent pulses and the unambiguous swath are discussed. Considering the sparsity of the imaging region and the limited number of pulses, we adopt the sparse recovery method to obtain the target images. By utilizing multiple frequency systems, the unambiguous imaging swath is enlarged. Finally, the simulated results confirm the proposed method.

Hyperspectral image (HSI) is usually composed of hundreds of bands which contain very rich spatial and spectral information. However, the high-dimensional data may lead to the curse of dimensionality phenomenon when it is used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we developed a deep learning classification framework based on the spectral and spatial information of hyperspectral image. Firstly, the deep learning features in different layers could be extracted automatically. Secondly, based on the learned deep learning features, we could obtain the classification of hyperspectral image with logistic regression (LR) classifier. Finally, we compared our approach with other methods including quadratic discriminant analysis with the multilevel logistic spatial prior (QDAMLL), logistic discriminant analysis with the multilevel logistic spatial prior (logDAMLL), linear discriminant analysis with the multilevel logistic spatial prior (LDAMLL), subspace multiclass logistic regression with the multilevel logistic spatial prior (MLRsub MLL), support vector machine on extended morphological profiles (SVM/EMP), support vector machine on expectation maximization and post-regularization (SVM-EM-PR). The experimental results showed that our method obtained the optimum accuracy, which was better than the other six approaches. And the OA was up to 99.39%. Therefore, the deep convolutional neural networks (DCNNs) is a robust method for land use classification with hyperspectral image.

A Moon-based synthetic aperture radar (SAR) system can provide large-scale, long-term, and constant earth observation (EO). Nevertheless, several problems should be solved before implementation. The problems include the ultra-small range of antenna viewing angles, the largest cell size of SAR allowed without the consideration of range cell migration and the related antenna size, and the decorrelation caused by long integration time. Although the moon-based EO system is a concept at present, with a baby but concrete and persistent step, the giant leap for human beings will be achieved.

A novel algorithm to estimate moving target velocity for a spaceborne high resolution and wide swath (HRWS) synthetic aperture radar (SAR)/ground moving target indication (GMTI) system is presented. To retain the power of the moving target, one needs to know the velocity of the moving target before clutter suppression and spectrum reconstruction. According to the relationship between the velocity and cone angles, the estimation of velocity is transformed into the estimation of direction-of-arrival (DOA) of the received signal using the sparse DOA technique. If the clutter is ignorable, the algorithm is directly applied to the received signal. Otherwise a preprocessing based block matrix is performed. Simulated results confirm the effectiveness of the proposed algorithm.

When combined with digital beam-forming (DBF) techniques, multichannel synthetic aperture radar (SAR) systems can achieve high-resolution and wide-swath SAR imaging. However, inevitable channel biases will degrade the performance of DBF in practice. To address this problem, a novel channel phase bias estimation algorithm is proposed in this letter. Theoretical analysis reveals that the signal of the first channel, which is considered as the reference channel, can be reconstructed from the signals of other channels, regardless of noise and signals outside the Doppler bandwidth. In the presence of phase biases, there is a reconstruction error after the cancellation by subtracting this reconstructed signal from the original signal. However, by minimizing the reconstruction error, the channel phase biases can be precisely estimated. The effectiveness of the proposed algorithm is validated by the experimental results.

The aim was to study seasonal snow and permanent snow variation in alpine regions using coherence component data derived from a multi-temporal of PALSAR InSAR data. With coherence decomposition technique, we obtained the multi-temporal data of temporal-coherence component near Mt. Dagu, China, where vegetated surface, seasonal snow cover or grazing area, and permanent snow cover exist. The variation of temporal-coherence component through time indicated changes of snow cover and status within the grazing zone and permanent snow area or areas above tree line. After the analyses of the temporal-coherence components from January to February, February to April, April to May, and January to May of 2008, we were able to identify snow status and change of snow cover above local tree line. The overall accuracy level greater than 71% was achieved in the identification when compared to those derived from multi-temporal TM images of Landsat 5. The results were promising.

Multichannel synthetic aperture radar (SAR) systems in azimuth can achieve high-resolution and wide-swath imaging. However, the quality of final SAR image can be degraded by the channel mismatch in phase which increases the energy outside the processed Doppler bandwidth (PDB). To address this problem, a calibration algorithm is proposed in this letter by minimizing the energy outside the PDB. Theoretical analysis shows that the presented method can be interpreted as an adaptively weighted least square estimation problem, where the weights are related to the signal-to-noise ratio (SNR) of the echoes from different directions. Simulation results reveal that our method outperforms the conventional methods in the case of quasi-uniform sampling, particularly at the low-SNR region.

A nonlinear chirp scaling algorithm (NCSA) is proposed for bistatic SAR data processing with high squint angles in tandem configuration. Besides the dependence of the azimuth frequency, the dependence of range is considered for the Doppler chirp rate of tandem bistatic SAR. The proposed algorithm can compensate the range dependence of both the range cell migration (RCM) and the second range compression (SRC) terms well by appropriately setting the coefficients of the phase term, which is obtained after the nonlinear chirp scaling process in the two-dimensional wavenumber domain. Only fast Fourier transforms (FFTs) and phase multiplies are required, and fast imaging is implemented in the frequency domain. Satisfying focusing qualities verify the effectiveness of the proposed algorithm by simulations.

In this paper, a comparison between four channel error estimation methods for high resolution and wide swath (HRWS) synthetic aperture radar (SAR) systems is present. Three of the methods are based on subspace theory and implemented in Doppler frequency domain, while the fourth is based on the correlation between adjacent samples and implemented in time domain. After a brief overview of the approaches, the performance of each is analyzed with respect to its computational complexity and precondition. Quantitative results are shown using the ground-based real-data.

With the combination of digital beamforming (DBF) processing, multichannel synthetic aperture radar (SAR) systems are promising in high-resolution wide-swath imaging. However, the mismatch among channels will degrade the performance of DBF. In this letter, two novel methods are proposed to estimate channel errors for multichannel SAR systems in azimuth. The first method is based on the fact that the space spanned by the signal eigenvectors is equal to that spanned by the practical steering vectors. In the second method, the channel errors are directly estimated by the antenna patterns without matrix decomposition and inversion processing. Both the theoretical analysis and experiments demonstrate the effectiveness and efficiency of these two methods.

Incorporated with digital beam-forming processing, multichannel spaceborne synthetic aperture radar (SAR) systems are able to overcome the minimum antenna area constraint and yield high resolution and wide swath (HRWS) images. This letter mainly investigates the performance of the space-time adaptive processing (STAP) approach applied to HRWS SAR imaging. The analytic expressions for the signal-to-noise ratio (SNR) scaling factor and azimuth ambiguity to signal ratio (AASR) are derived and confirmed by the simulated results. Then, the influence of channel errors on HRWS imaging is analyzed in detail.