Hepatocellular carcinoma is a highly heterogeneous and complex malignant tumor, posing significant challenges for precise diagnosis and treatment. Existing methods face limitations in multimodal data integration and multi-phase feature modeling, making it difficult to fully exploit the complementary information from multi-phase CT images, structured clinical data, and medical texts. On one hand, most methods rely solely on single-modal data (e.g. CT images or clinical reports), failing to effectively utilize the complementary characteristics of multimodal data. On the other hand, traditional approaches typically concatenate multi-phase CT images as input to the model, ignoring the dynamic evolution features between different phases, leading to information loss and limited predictive performance. To address these issues, we propose a novel Tri-modal Phase-aware Contrastive Learning Framework (TPCL), which incorporates a Phase-aware Attention Fusion Network (PAAF-Net) and a Phase-Conditional Prompt Network (PCPN) to achieve deep alignment and integration of multimodal features. Additionally, we design a Multi-modal Contrastive Loss to further optimize the consistency of feature distributions across different modalities. Experimental results on multiple public and private datasets demonstrate that TPCL significantly outperforms existing methods, achieving up to an$18.54\%$improvement in ACC and a$10.09\%$improvement in AUC.

Multi-parametric magnetic resonance imaging (mpMRI) is widely used in the diagnosis of orbital lymphoproliferative disorders (OLPDs) due to its non-invasive nature. However, in clinical practice, contrast-enhanced T1-weighted (T1C) images are often unavailable due to contraindications to gadolinium-based contrast agents, meanwhile T2-weighted (T2w) images may also be omitted for time-sensitive diagnoses, making it a challenge to generate these images from T1-weighted (T1 w) image alone for multimodal differential diagnosis. Generative adversarial network (GAN)-based models partially address the issue of missing modalities in medical image analysis; however, they often suffer from unstable generation of missing images and lack integration with subsequent diagnostic tasks. To this end, we propose a One-to-Many Generative Adversarial Network (OMGAN) for diagnosing OLPDs in incomplete mpMRI, consisting of a cross-modal generator and a self-representation module, enabling multimodal diagnosis using pre-contrast images alone within a single model. Specifically, we first design an image-modality fusion module that incorporates trigonometric function coding and mixup augmentation to effectively guide the generation from T1 w to T2w and T1 C within one model. Then, we construct a cross-modal generator with a semantic disambiguation block to synthesize the missing images. Meanwhile, we use a self-representation module with a classification-guided branch to effectively extract task-relevant image features. Finally, multimodal features are fused to accomplish the differential diagnosis of OLPDs in the downstream task. Experiments on internal datasets demonstrated that OMGAN outperforms state-of-the-art GAN-based models, with the area-under-the-curve and accuracy improving by 8.18-14.04% and 12.53-16.39%, respectively. Codes are available at https://github.com/3Iasticheart/OMGAN.

Increasing application demands are driving the need for future spaceborne synthetic aperture radar (SAR) systems with high resolution and continuous ultrawide swath capabilities. Azimuth multichannel staggered SAR, which integrates variable pulse repetition interval (PRI) and multichannel techniques, presents a promising solution. However, the resulting nonuniform sampling invalidates conventional frequency-domain reconstruction algorithms and increases signal processing complexity. To address this challenge, this paper proposes a uniform reconstruction scheme based on phase center adjustment (PCA). By introducing a phase center variation, the scheme compensates for nonuniform components to achieve equivalent uniform sampling during data acquisition. The PRI design criterion is established to minimize the maximum PCA value and provide the allowable range of the initial PRI. Furthermore, activation strategies for both transmit and receive antenna elements are defined to jointly achieve the required PCA. Simulation results validate the effectiveness of the proposed scheme.

Interferometric Synthetic Aperture Radar (InSAR) shift measurement plays a key role in image coregistration and absolute phase measurement and has significant applications in the InSAR processing workflow. However, the current shift measurement algorithms are limited by the relative bandwidth of the SAR system, resulting in low resolution and accuracy. SSENet is a recent InSAR shift measurement approach that utilizes deep learning to address these issues to some extent. This paper proposes a calibration method for SSENet, which introduces a lightweight neural network designed to refine the marginally biased output shifts. Furthermore, we demonstrate the performance of the refined SSENet algorithm and its potential in assisting phase unwrapping using LSAR-01 bistatic Synthetic Aperture Radar (SAR) data.

Point cloud classification is a fundamental problem in 3-D point cloud analysis. However, most existing methods are supervised, which requires costly and laborious annotations of large-scale point cloud datasets. This severely limits the practical applicability of point clouds. Therefore, exploring point cloud clustering methods, which can group point clouds into semantically meaningful clusters in an unsupervised manner, is of great importance. However, this remains a formidable challenge for humans. Here, we present PointCluster, a novel framework for deep clustering of 3-D point clouds. To enable accurate and reliable self-supervision for the clustering process, the framework introduces two semantic pseudo-labeling algorithms: prototype pseudo-labeling and reliable pseudo-labeling. We devise a three-step training process for the clustering network. First, we adopt a cross-modal representation learning approach to optimize the feature model. Second, we freeze the network parameters of the feature model and apply the prototype pseudo-labeling algorithm to optimize the clustering heads separately. Third, we use the reliable pseudo-labeling algorithm to jointly train the feature model and the clustering head in a semi-supervised manner, which enhances the overall clustering performance. The experimental results demonstrate that PointCluster achieves the state-of-the-art clustering results on public datasets such as ShapeNet. Moreover, our method narrows the gap between unsupervised point cloud clustering and supervised point cloud classification, offering a new perspective for the point cloud classification task.

Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and hypergraph convolution network (CORONet) for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. Specifically, we first build a cross-sequence joint representation (COR) module to integrate image imputation and feature representation into a unified framework, encouraging effective feature extraction for subsequent classification. Then, we fuse multiple COR features and applied feature selection to reduce the redundant information between sequences. Finally, we deploy hypergraph structures to model high-order correlation among different subjects and extracted high-level semantic features by hypergraph convolutions for molecular subtyping. Extensive experiments on incomplete DCE-MRIs of 395 patients from the TCIA repository showed a significant improvement of our CORONet over state of the arts, with the area under the curve (AUC) of 0.891 and 0.903 for luminal and triple-negative (TN) subtype prediction, respectively. Similar advantages of CORONet were also confirmed in partial complete DCE-MRIs of 144 patients, achieving an AUC of 0.858 and 0.832 for predicting luminal and TN subtypes of breast cancer, respectively. Nevertheless, both of these values were lower compared to the scenario where DCE-MRIs from all 395 patients were utilized. Our study contributes to the precise molecular subtyping using incomplete multi-sequence DCE-MRI, thereby offering promising prospects for future risk stratification of breast cancer patients.

Coronary artery disease (CAD), a leading cause of mortality and morbidity, manifests as atherosclerotic plaques formed by the deposition of cholesterol and lipids within coronary walls. A plenty of machine learning methods have been developed to identify different plaques or the degree of stenosis. Few studies, however, focus on plaques vulnerability, which is crucial because vulnerable plaques are at a high risk of rupture or erosion even with less severe stenosis. To this end, we propose a weakly-supervised multi-contrastive learning network named WheelNet to differentiate vulnerable plaques from stable ones in coronary CT angiography (CCTA). Specifically, we first extract cross-sectional images along the coronary centerline and took consecutive cross-sections as one image sequence. Second, we construct a WheelNet with multiple branches to perform contrastive learning between different image sequences, dependent or independent of vulnerability labels of coronary plaques. Third, we perform patient-level feature aggregation via local-to-global feature encoding given the feature embeddings of image sequences. Finally, we differentiae patients with vulnerable coronary plaques from those with stable ones using an XGBoost classifier. Extensive experiments on the CCTA dataset of 108 patients show the superiority of our WheelNet over other state of the arts, with the diagnostic area-under-the-curve (AUC) of 0.74/0.75 with/without using vulnerability labels, respectively.

Orbital lymphoproliferative disorders (OLPDs) are complex orbital mass-like lesions ranging from benign to malignant. Precise preoperative diagnosis of OLPDs holds profound importance in facilitating timely and effective patient management. Recent studies have shown that exploiting multimodal images can boost the performance in identifying different orbital lesions. However, one or several imaging modalities are sometimes missing in practical applications, which has not yet been properly addressed in existing studies. To this end, we propose a novel OLPD diagnostic method with incomplete multimodal images based on self-/cross-representation and hypergraph ensemble. Specifically, in the first stage, we develop a self-representation network to extract unimodal features and a cross-representation network to impute missing features. In the second stage, by using unimodal features as input, we construct a hypergraph for each modality to make unimodal diagnosis; while for multimodal diagnosis we conduct a multi-view grouping fusion method to reduce the semantic gap between multimodal features and fuse multiple unimodal hypergraphs as multimodal hypergraph to perform multimodal diagnosis. In the third stage, we propose an ensemble strategy that incorporates unimodal diagnosis and multimodal diagnosis to accomplish the final decision. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art approaches.

Stereo-radargrammetric shift estimation is an important part of interferometric synthetic aperture radar (InSAR) data processing. However, the presence of residual topographical phase poses a challenge to achieving accurate coherent shift estimation in future high-resolution InSAR measurement tasks. In this work, we present a two-stage multi-baseline InSAR stereo-radargrammetric shift joint estimation approach. Our proposed method reduces the influence of the residual topographical phase, even in cases where no prior information is available or with low resolution prior digital elevation models (DEMs). In addition, a topography model based on Brownian motion is used to analyze the effect of the residual topographical phase on the accuracy of the shift estimation.

The interferometric synthetic aperture radar (InSAR) image shift measurement technique is of great significance in processing high-precision digital elevation model (DEM) generation and deformation measurements. It can be used in steps such as image fine coregistration, interferometric phase unwrapping and absolute phase calibration in the InSAR processing flow without an external DEM. However, the shifts estimated by current methods are of low resolution and have high measurement noise, which may have adverse impacts on subsequent applications. In this paper, a lightweight, high-resolution and low-noise interferometric stereo-radargrammetric shift estimation network (SSENet) is proposed to solve the aforementioned problems. It introduces deep learning technology to the InSAR shift estimation task for the first time. We propose forming multiscale 3D coherence coefficient cubes by projecting the shift values of the images onto the third dimension and then using a 3D convolutional network for multiscale fusion and encoding, followed by decoding with linear layers. In addition, a dataset generation and augmentation scheme based on real data is designed for model training and evaluation. Several sets of real SAR images from different regions of the world were used to evaluate SSENet. Compared with the typical coherent cross-correlation approach, SSENet reduces the mean absolute error of the estimated shifts by approximately 79% while improving the resolution by a factor of 4×4, making it possible to restore the absolute interferometric phase. Finally, we demonstrate a stitching strategy for processing large-scale SAR images and discuss the multiple potential uses of SSENet in the InSAR processing chain.

Curvilinear spotlight SAR (CSSAR) has a high degree of freedom and can be used for 3-D imaging and video SAR (ViSAR) persistent imaging. The direct and effective processing of CSSAR data is an important part of CSSAR applications. However, CSSAR has higher requirements for motion compensation, especially when the motion measurement is not accurate enough. In this paper, an extended polar format algorithm (EPFA) is proposed based on the non-uniform fast Fourier transform for CSSAR. First, a theoretical derivation and analysis of the 2-D space-varying phase error in CSSAR are carried out. Then, a 2-D autofocus algorithm is proposed, which takes into account the spatial variability of the phase error. The efficiency of EPFA embedded in 2-D autofocus processing is significantly higher than that of back projection. In addition, based on the principle of small-angle approximation and spatial frequency domain sub-aperture technology, a novel ViSAR image generation scheme for CSSAR is proposed, which can significantly reduce the number of redundant calculations. The proposed algorithm is verified experimentally using data with a bandwidth of 2.4 GHz.

Early identification of endometrial cancer or precancerous lesions from histopathological images is crucial for precise endometrial medical care, which however is increasing hampered by the relative scarcity of pathologists. Computer-aided diagnosis (CAD) provides an automated alternative for confirming endometrial diseases with either feature-engineered machine learning or end-to-end deep learning (DL). In particular, advanced self-supervised learning alleviates the dependence of supervised learning on large-scale human-annotated data and can be used to pre-train DL models for specific classification tasks. Thereby, we develop a novel self-supervised triplet contrastive learning (SSTCL) model for classifying endometrial histopathological images. Specifically, this model consists of one online branch and two target branches. The second target branch includes a simple yet powerful augmentation module named random mosaic masking (RMM), which functions as an effective regularization by mapping the features of masked images close to those of intact ones. Moreover, we add a bottleneck Transformer (BoT) model into each branch as a self-attention module to learn the global information by considering both content information and relative distances between features at different locations. On public endometrial dataset, our model achieved four-class classification accuracies of 77.31 ± 0.84, 80.87 ± 0.48 and 83.22 ± 0.87% using 20, 50 and 100% labeled images, respectively. When transferred to the in-house dataset, our model obtained a three-class diagnostic accuracy of 96.81% with 95% confidence interval of 95.61-98.02%. On both datasets, our model outperformed state-of-the-art supervised and self-supervised methods. Our model may help pathologists to automatically diagnose endometrial diseases with high accuracy and efficiency using limited human-annotated histopathological images.

Polarimetric synthetic aperture radar (SAR) has been extensively used in various remote sensing applications. In this article, a unified framework is designed to compare the classification performance of different polarimetric systems, which include quad-polarimetric (QP), compact-polarimetric (CP), and dual-polarimetric (DP). To avoid problems, such as the lack of uniform standards in feature extraction, the classification algorithm is directly based on the statistical characteristics of the coherency/covariance matrix and is implemented by extending the Wishart mixture model (WMM). The GF-3 data set in San Francisco and the AIRSAR agricultural data set in Flevoland are used in the experiment, and the following conclusions are generated. QP can achieve the highest classification accuracy in all classification tasks. When distinguishing three typical classes (water, urban, and vegetation) with very different scattering characteristics, the performance of different polarimetric systems is similar, and QP has only a slight advantage. For classification tasks of different classes with similar scattering characteristics, CP performs better in agricultural scenes, and the overall accuracy (OA) is only reduced by 3%–4% compared with QP. DP performs better in urban scenes, and OA is only reduced by 1%–3% compared with QP. These conclusions can provide guidance for future payloads’ design and the choice of polarimetric operation mode for existing multi-polarimetric SAR systems to achieve the purpose of giving full play to the advantages of different polarimetric systems.

Hybrid-polarimetric architecture may become a new option for spaceborne quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) mainly due to the range ambiguity improvement without loss of polarization information. To inherit the analytical methods developed for the conventional quad-pol SAR data, it is necessary to transform the hybrid quad-pol SAR data into a linear polarimetric basis. However, this operation will result in deterioration of azimuth ambiguity performance. The theoretical analysis on range and azimuth ambiguities in quad-pol SAR has been relatively complete, which is also summarized in this article. Yet, there is no real data to verify the equivalence of polarization information and to compare the ambiguity levels. Based on P-band airborne experiment, real hybrid and conventional quad-pol SAR data are used for comparative verification. In addition, considering that polarimetric target decomposition is often used in the practical applications, we suggest a new perspective from target decomposition for the ambiguities in quad-pol SAR. Identical polarimetric processing results for two sets of comparative data verify that hybrid quad-pol SAR data are indistinguishable from conventional quad-pol SAR data. The demonstrated intensity images and decomposed RGB images with azimuth ambiguities, acquired by the two quad-pol modes, completely conform to the theory.

In azimuth multichannel high-resolution and wide-swath (AMC-HRWS) synthetic aperture radar (SAR) system, undersampling of azimuth signal results in the failure of the single-channel echo imaging, so the echoes of all the channels should be reconstructed to obtain the unambiguous SAR image. Many methods have been proposed to reconstruct the stationary scene echoes. However, moving targets (MTs) will cause false targets in the reconstructed SAR image, which has a great influence on SAR image interpretation and moving target detection, especially in maritime scenes. Hence, we propose an unambiguous imaging method of the moving target for maritime scenes with AMC-HRWS SAR. First, azimuth deramp processing is introduced to obtain coarse-focused moving targets, and then their echoes can be extracted from the sea clutter. Based on the extracted data, a radial velocity estimation method is proposed, which is high efficiency and does not require redundant channels. In addition, based on the estimated radial velocity and the characteristics of the coarse-focused signal, a novel signal reconstruction method of moving target is presented. The reconstruction performance of this method is not affected by channel imbalance, and false targets can be removed even if there is radial velocity error. Besides, the effectiveness of this scheme is verified by the simulation data and GaoFen-3 SAR real data. The estimated radial velocities of ships are verified by automatic identification system (AIS) information. The imaging results show that false targets are effectively suppressed, and moving targets are located in correct positions.

Interferometric synthetic aperture radar (InSAR) is an efficient tool for global large-scale digital elevation model (DEM) generation. However, for steep terrain, the current approaches cannot stably reconstruct valid DEM products from a single-baseline InSAR image pair without an external reference DEM due to the influence of shadow/layover geometries, phase noise, and the Itoh condition limitation in the phase unwrapping (PU) process. In this article, a novel stereo-radargrammetry-assisted PU (SAPU) approach with no need for external auxiliary information is proposed to eliminate the constraint of the Itoh condition by exploiting the internal stereo-radargrammetric shifts. The method reduces the phase gradient in the interferogram and guides the PU process with automatically selected tie points. Notably, the current stereo-radargrammetry approaches will deteriorate to an incoherent state in steep terrain, hampering the reliability and accuracy of SAPU. Accordingly, we also propose an adaptive weighted subwindow-coherent stereo-radargrammetric shift estimation (AWS-CSE) method to improve the accuracy of subpixel shifts by introducing local topographic phase consistency in coherence estimation. We quantitatively validate the performance of the proposed methods based on the L-SAR 01 simulation data and three pairs of repeat-pass single-baseline Advanced Land Observing Satellite (ALOS) phased array type L-band synthetic aperture radar (PALSAR) images from different areas, comparing the results with those of various traditional and deep-learning-based PU methods. The findings suggest that the proposed methods can generate accurate DEMs from single-baseline measurements in steep terrain while avoiding additional data acquisitions.

This paper established a unified framework to compare the capabilities of different polarimetric SAR systems in target detection based on polarimetric covariance matrix. The framework contains two different strategies. The first is based on the original polarimetric matrix and the second is based on the main scattering matrix. An improved algorithm is proposed based on the second strategy. GF-3 quad-polarimetric data is used to compare the performance between different algorithms, the results show that the proposed algorithm can obtain the highest signal-to-clutter ratio (SCR). The performance of different polarimetric systems is further compared, and the results show that the quad-polarimetric (QP) performs best, compact-polarimetric (CP) performance is similar to QP, and dual-polarimetric (DP) performs worst.

Azimuth ambiguity degrades the quality of synthetic aperture radar (SAR) images and leads to the increase of false alarm rate in ground moving target indication (GMTI). Due to the existing azimuth ambiguity, suppression methods do not remove the first-order ambiguity completely and ignore the ambiguity above first order as well, moving target detection is affected by residual ambiguity. Hence, a novel method to suppress first-order and higher order azimuth ambiguities for the dual-channel SAR/GMTI is proposed in this letter. First, the displaced phase center antenna (DPCA) technique is applied to suppress clutter. Then, estimate the local azimuth ambiguity-to-signal ratio (LAASR) to find out the area affected by ambiguity. Finally, an inpainting algorithm is improved to patch the ambiguity area. The proposed method can remove the ambiguity almost completely. Moreover, the method is verified using the GaoFen-3 SAR dual-channel complex image data, and the result shows that the false alarm of moving target detection is degraded without reducing the detection rate.

Driven by Internet of Things (IoT) and 5G communication technologies, the paradigm of mobile computing has changed from centralized mobile cloud computing to distributed mobile edge computing (MEC). Narrowing the gap between high quality of service (QoS) requirements and limited computing resources, and improving the utilization of computing resources between IoT devices and edge servers have become key issues. In this paper, we formulate a stochastic optimization problem involving dynamic offloading and resource scheduling between the local devices, base station (BS) and the back-end cloud. The goal is to minimize the consumption of energy and computing resources in the MEC system with energy harvesting (EH) devices, while meeting the QoS requirements of IoT devices. In order to solve this stochastic optimization problem, we convert it into a deterministic optimization problem, and propose an online dynamic offloading and resource scheduling algorithm (DORS) based on Lyapunov optimization theory. It is proved that the DORS algorithm can effectively balance the relationship between scheduling cost and MEC system’s performance. The comparison experiments show the effectiveness of the DORS algorithm in reducing the energy consumption.

An airborne P-band SAR experiment was conducted to detect targets in a forest in October 2019 and different results of target detection in the forest under snow-covered and snow-free states were obtained. When there is no snow on the branches, the targets in the forest are not easy to be seen in the image, but when the branches are covered with snow, the targets in the forest are clearly visible in the image. Three quad-pol data sets were used for this paper to compare and analyze the differences of target detection in the forest that Yamaguchi Four Component Decomposition and H/α unsupervised classification methods were used to explain. The results reveal that snow affects the scattering characteristics of the forest, and indirectly affects the penetration depth of the P-band electromagnetic waves into the forest, resulting in a large difference between the two detection results.

Azimuth ambiguity greatly affects the image quality and application of synthetic aperture radar (SAR). Several azimuth ambiguity suppression methods have been proposed; however, these methods cannot remove the ambiguity completely and only the first-order ambiguity has been considered. Hence, in this letter, a novel method based on Wiener filtering and local azimuth ambiguity-to-signal ratio (AASR) estimation for N-order azimuth ambiguity suppression is proposed. The Wiener filtering is used to attenuate the ambiguity energy. Then the original image and the filtered image are combined to estimate local AASR, which is used to identify the ambiguity pixel. Finally, the ambiguity can be removed via interpolation. Through this method, the N-order ambiguity energy can also be suppressed to a lower level, and simultaneously, the consistency, resolution, and signal-to-noise ratio of an SAR image are maintained. Furthermore, in order to verify the practicability of the proposed method, it has been tested on the GaoFen-3 image and TerraSAR-X image.

Highly squint synthetic aperture radar (SAR) increases the flexibility and aspect-information of observation but poses several challenges to frequency-domain algorithms. The back-projection (BP) algorithm is recognized to produce the best results for highly squint mode but at high computational expense. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm has been proposed to improve the performance. However, CFBP is ineffective in the highly squint case because its range model (RM) is imprecise. In this letter, we propose a modified CFBP (MCFBP) algorithm for highly squint spotlight SAR imaging. We employ a transformed Cartesian coordinate system to treat the transceiver mode as approximately side-looking mode. According to the transformed coordinate system, we propose a modified RM (MRM) to reduce the range error. Based on the MRM, we derive modified image spectrum compression steps and the Nyquist sampling rate of the subaperture image. MCFBP inherits CFBP's accuracy and efficiency advantages. Results of simulations and experiments performed by the X-band airborne SAR system with a maximum bandwidth of 1.2 GHz validate the improved performance of the proposed algorithm relative to BP and fast factorized BP.

A backprojection (BP) algorithm is recognized as an ideal method for high-resolution synthetic aperture radar (SAR) imaging. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm is proposed recently to avoid massive interpolations and improve the performance. However, integrating autofocus techniques with the CFBP has not been discussed. In this letter, an autofocus CFBP algorithm is proposed to compatibly combine the autofocus processing within the CFBP. After modifying the spectrum compression step in the CFBP, the approximate Fourier transformation (FT) relationship between the modified compensated subaperture images and the corresponding range-compressed phase history data in the Cartesian coordinate is revealed. The phase error is obtained by the multiple aperture map drift method, and the singular value decomposition total least square method is combined to improve the estimate robustness. Employing the range blocking method, the range variance of the phase error is compensated. The proposed algorithm inherits the advantages of the CFBP. Experiments performed by the X-band airborne SAR system with a maximum bandwidth of 1.2 GHz validate the proposed approaches.

In this letter, an improved full-aperture imaging algorithm for scanning synthetic aperture radar (ScanSAR) mode is proposed, which fills the data gaps between bursts through a linear-prediction-model-based aperture interpolation technique in a subaperture manner before azimuth compression. It can significantly suppress the spikes induced by periodical data gaps and, at the same time, enhance the signal-to-noise ratio of the obtained ScanSAR imagery. This approach has a great potential in the interferometric context. The effectiveness of the proposed approach is demonstrated by both simulated and real ScanSAR data with different types of terrain. All the experimental data were acquired by the C-band SAR system with a bandwidth of 200 MHz, which was developed by the Department of Space Microwave Remote Sensing System, Institute of Electronics, Chinese Academy of Sciences.

In order to enable a testbed for spaceborne scanning synthetic aperture radar (ScanSAR) mode, in this letter, a ScanSAR autofocusing approach for airborne platforms has been developed. Autofocusing algorithms, such as the phase gradient autofocus (PGA) algorithm, prove to be a useful postprocessing technique to get refocused synthetic aperture radar images. However, conventional stripmap PGA does not work in ScanSAR mode when the full-aperture approach is used, due to the periodic data gaps in each subswath. To solve this problem, we extend the stripmap PGA to ScanSAR with some modifications, mainly in the subaperture segmentation and phase error estimation steps. The performance of extended stripmap PGA is evaluated by an airborne ScanSAR data set containing different types of terrain, with a high spatial resolution up to 3.5 m in azimuth.

As a novel mode of ground-based synthetic aperture radar (GB-SAR), Arc frequency-modulated continuous wave (FMCW) SAR is rarely discussed in the literature up to now. Compared with the conventional rail GB-SAR system, the synthetic aperture is formed by the rotation of antennas, which can scan a wide azimuth extent. Due to its convenience and potential in SAR applications, the Institute of Electronics, Chinese Academy Sciences, carried out a series of Arc FMCW SAR experiments in March 2013, and a lot of results were obtained. In this paper, we discuss the signal processing of Arc FMCW SAR and its first results in change detection and interferometric SAR. The components of the Arc FMCW SAR system are first described, and then, we focus on its signal processing, where the signal model and its properties are investigated; two focusing algorithms are developed for different purposes. The effectiveness of the algorithms is validated by both simulation and real data experiments. We also exhibit the applications of Arc FMCW SAR in landslide detection and digital-elevation-model extraction for the first time. Results show its huge potential in ground-based applications.

Nonnegative eigenvalue decomposition (NNED), which insists and guarantees that each decomposed scattering component corresponds to a physically realizable scatterer, is powerful for polarimetric synthetic aperture radar (SAR) images analysis. Previous NNED is mainly illustrated under the reflection symmetric condition. In this paper, the coherency matrix approach is derived to implement the NNED for the nonreflection symmetry scattering case. We explicitly show the diversifications of the decomposition results between NNED with and without reflection symmetry assumptions, and quantitatively analyze the differences between them using the E-SAR polarimetric data acquired over the Oberpfaffenhofen area in Germany.

The purpose of this letter is to show that the azimuth scanning mode of a synthetic aperture radar can be applied to deriving ocean wave spectra and determining the wave propagation direction for the first time, which reveals its enormous potential and great future in ocean observation. The improved Doppler beam sharpening imaging algorithm is used to produce a sequence of individual subimages of ocean waves in the same scan region from different aspect angles with a high revisit rate. These subimages have an inherent property that they are formed at different discretely delayed times. Therefore, wave propagation direction can be determined from a pair of wave images in different scans. Several different methods are applied to the real airborne radar wave data, including the methods of scan sum (taking the standard Fourier spectrum of the scan-summed image), spectral sum, spectral phase shift, and cross-correlation function of subimages. The processing results demonstrate the effectiveness of the algorithms.