Deep learning (DL) based synthetic aperture radar (SAR) imagery ship detection is challenged by multiscale ships on the identical SAR image, which inevitably leads to insufficient and low-quality positive samples during training and ultimately degrades detection performance. To address this issue, we propose a Scale-Sensitive Adaptive Sample Allocation Strategy (SSA-SAS) for SAR ship detection. SSA-SAS ranks candidate boxes using a unified score that integrates a scale-sensitive Wasserstein distance (SSWD), a shape cost, and classification confidence. SSWD serves as the core regression metric, enabling adaptive tolerance to positional offsets based on object scale. Meanwhile, the shape cost introduces morphological priors to guide early-stage optimization. These components jointly enhance the quantity and quality of selected positive samples throughout training. Experimental results show that SSA-SAS improves average precision (AP) by up to 2.6% on the high-resolution SAR images dataset for ship detection and instance segmentation (HRSID) dataset and 1.4% on the SAR ship detection dataset (SSDD), while accelerating network convergence by approximately 5.0%.

The complementary waveform-based moving target detection faces challenges like limited Doppler resolution and poor range sidelobe suppression. This letter proposes a Complementary-Moving Target Detection (C-MTD) method to address the coherent integration issue of Waveform-Domain Complementary Signal Sets (WDCSS), a subset of generalized complementary waveforms. C-MTD enhances Doppler resolution and extends complementary properties to the range- Doppler map by applying coherent slow-time processing to consecutive WDCSSs. Simulations demonstrate that C-MTD outperforms conventional methods in Doppler resolution, range sidelobe suppression, and detection under low input signal-to-noise ratios (SNRs) conditions.

For satellite brightness temperature images, researchers are constantly pursuing higher resolutions to obtain more detailed meteorological information. In this paper, a novel deep-learning-based modelling approach, named Zero-Sample Gradient Guidance Spatial Resolution Enhancement (ZSGRE), is developed explicitly for microwave radiometers. The detailed model, including mathematical derivation and key parameters, is presented. Subsequently, the proposed approach is applied in four scenarios: synthetic scene, simulated geographical brightness temperature, practical measurement of microwave radiometer in Fengyun-3D (FY-3D), and a cyclone analysis on the Atlantic. Compared with other methods, the proposed ZSGRE method improves 2.51% of SSIM (structural similarity), enhances 2.3 dB of PSNR (Peak Signal-to-Noise Ratio), and decreases 15.8% of IFOV (Instantaneous Field of View). Such applications demonstrate ZSGRE’s significant performance: zero-sample preparation and spatial resolution enhancement.

Near-field communication using large-scale antenna arrays is one of the hot research topics in the sixth-generation (6G) wireless communication. Reconfigurable intelligent surface (RIS) is a cost-effective method for manipulating electromagnetic waves in the near field. We propose a 2-bit dual-polarized RIS that has the merits of low cost, low power consumption, high phase accuracy, and polarization diversity. Each element consists of an aperture-coupled microstrip patch, two single-pole-four-throw (SP4T) switches, and two groups of microstrip delay lines. Two-bit phase shift is achieved by using only one SP4T switch that controls the connection of four parallel delay branches. Dual polarization is generated by placing two orthogonal slots with two 2-bit phase shifters. A 15×15 RIS prototype operating in the 3.6 GHz band is fabricated and measured. The beam can be scanned in the ±60° range, with a peak aperture efficiency of 40.1% for horizontal polarization and 38.3% for vertical polarization. What is more, the total power consumption of the RIS is merely about 100 mW, which is very attractive for massive deployment in 6G near-field communication.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

The auto-correlation is not sufficient for the complete statistical characterization of a complex sequence, especially in applications involving nonlinear systems. Therefore, this letter addresses the sequence design problem considering the local shaping of complete second-order correlation, which controls the sidelobe level for both auto-correlation and conjugate correlation over specific lags. An efficient algorithm based on the Majorization-Minimization technique is proposed, which only consists of close-form iterations with full fast Fourier transform (FFT) operations. Numerical simulations indicate that the proposed algorithm significantly outperforms the existing gradient method in terms of the achieved objective and runtime. The application in nonlinear radar is also presented.

In recent years, the utilization of In-Band Full-Duplex (IBFD) technology has gained considerable attention as an effective solution to address limited spectrum resources. However, achieving an IBFD system necessitates the utilization of both passive suppression and active cancellation techniques to completely eliminate strong self-interference (SI) signals in the local receiver. This research paper aims to tackle this challenge by introducing the Multi-Domain Intelligent Cancellation (MDIC) method, which integrates analog-domain and digital-domain cancellation techniques through comprehensive nonlinear system modeling. Moreover, this study presents an extensive analysis of the underlying principles of MDIC, while evaluating the limits of SI suppression performance under the assumption of perfect channel estimation and no phase noise. The channel parameter estimation is conducted using the time-domain least squares method, without considering the effects of Gaussian white noise and phase noise. To enhance the performance of SI cancellation (SIC), a deep learning network (DLN) is introduced in the digital domain. Simulation results demonstrate that the proposed MDIC method achieves a substantial SIC performance of approximately 60 dB, which closely approaches the theoretical limit. Furthermore, experimental data indicates that the proposed deep learning approach exhibits superior SIC performance and broader adaptability to broadband signals compared to traditional methods.

The false targets generated by interrupted sampling repeater jamming (ISRJ) bring serious threats to radar target detection and imaging. Transmitted waveform design with joint processing has become a promising way to deal with them. In this letter, the ambiguity function (AF) of integral power frequency modulated (PFM) waveform is derived. Meanwhile, the time-frequency characteristic of ISRJ signal received by the radar is analyzed on the range-Doppler plane. In consideration of the range-Doppler coupling characteristic, an improved Doppler filtering processing method is proposed to suppress the jamming false targets. Simulation results demonstrate that the selected PFM waveform with proposed joint processing method can effectively suppress the false targets resulting from ISRJ.

Passive multi-frequency microwave sensors frequently struggle with difficulties of non-uniform spatial resolution among multiple channels. The raw measurements in the land-sea transition zone are seriously contaminated. Conventional analytical deconvolution techniques suffer from the trade-off between spatial resolution enhancement and noise amplification, leading to low data integrity in the practical spatial resolution matching application. In order to provide multi-channel microwave radiometer data with matching levels of spatial resolution, a method based on iterative deconvolution with close loop priors(ICLP) is proposed. Specifically, a destriping module is first utilized as pre-processing step to maintain high data integrity. Then, the close loop mechanism using sparse adaptive priors is proposed to balance the spatial resolution and data integrity enhancement. Also, progressively iterative deconvolution is introduced to realize controllable levels of spatial resolution enhancement(spatial resolution matching) for multi-channel data to reach a consistent level. Experiments performed using both simulated and actual Microwave Radiation Imager(MWRI) data demonstrate the validity and effectiveness of the method.

Deep learning methods have played a more important role in hyperspectral image classification. However, general deep learning methods mainly take advantage of the samplewise information to formulate the training loss while ignoring the intrinsic data structure of each class. Due to the high spectral dimension and great redundancy between different spectral channels in the hyperspectral image, these former training losses usually cannot work so well for the deep representation of the image. To tackle this problem, this work develops a novel deep manifold embedding method (DMEM) for deep learning in hyperspectral image classification. First, each class in the image is modeled as a specific nonlinear manifold, and the geodesic distance is used to measure the correlation between the samples. Then, based on the hierarchical clustering, the manifold structure of the data can be captured and each nonlinear data manifold can be divided into several subclasses. Finally, considering the distribution of each subclass and the correlation between different subclasses under data manifold, DMEM is constructed as the novel training loss to incorporate the special classwise information in the training process and obtain discriminative representation for the hyperspectral image. Experiments over four real-world hyperspectral image datasets have demonstrated the effectiveness of the proposed method when compared with general sample-based losses and showed superiority when compared with state-of-the-art methods.

Nowadays, deep learning methods, especially the convolutional neural networks (CNNs), have shown impressive performance on extracting abstract and high-level features from the hyperspectral image. However, the general training process of CNNs mainly considers the pixelwise information or the samples' correlation to formulate the penalization while ignores the statistical properties especially the spectral variability of each class in the hyperspectral image. These sample-based penalizations would lead to the uncertainty of the training process due to the imbalanced and limited number of training samples. To overcome this problem, this article characterizes each class from the hyperspectral image as a statistical distribution and further develops a novel statistical loss with the distributions, not directly with samples for deep learning. Based on the Fisher discrimination criterion, the loss penalizes the sample variance of each class distribution to decrease the intraclass variance of the training samples. Moreover, an additional diversity-promoting condition is added to enlarge the interclass variance between different class distributions, and this could better discriminate samples from different classes in the hyperspectral image. Finally, the statistical estimation form of the statistical loss is developed with the training samples through multivariant statistical analysis. Experiments over the real-world hyperspectral images show the effectiveness of the developed statistical loss for deep learning.

Future communications will provide higher transmission rates and higher operating frequencies. In addition, agile beam tracking will be an inevitable trend in technology development. The terahertz retrodirective antenna array proposed and discussed in this paper can be a better solution for agile beam tracking. The array receives a 40-GHz navigation signal and accurately retransmits a 120-GHz beam in the direction of the arrival wave. Simulation results indicate that the proposed array with a stacked sandwich structure has realized the tracking of the received wave. The scanning radiation pattern shows that the array gain is 23.87 dB at 19.9° when the incident angle is 20° with a relative error of only 0.5%, meaning that there is a lateral error of only 8.7 m at a transmission distance of 5 km.

Though Faster R-CNN based two-stage detectors have witnessed significant boost in pedestrian detection accuracy, they are still slow for practical applications. One solution is to simplify this working flow as a single-stage detector. However, current single-stage detectors (e.g. SSD) have not presented competitive accuracy on common pedestrian detection benchmarks. Accordingly, a structurally simple but effective module called Asymptotic Localization Fitting (ALF) is proposed, which stacks a series of predictors to directly evolve the default anchor boxes of SSD step by step to improve detection results. Additionally, combining the advantages from residual learning and multi-scale context encoding, a bottleneck block is proposed to enhance the predictors' discriminative power. On top of the above designs, an efficient single-stage detection architecture is designed, resulting in an attractive pedestrian detector in both accuracy and speed. A comprehensive set of experiments on two of the largest pedestrian detection datasets (i.e. CityPersons and Caltech) demonstrate the superiority of the proposed method, comparing to the state of the arts on both the benchmarks.

Object detection generally requires sliding-window classifiers in tradition or anchor-based predictions in modern deep learning approaches. However, either of these approaches requires tedious configurations in windows or anchors. In this paper, taking pedestrian detection as an example, we provide a new perspective where detecting objects is motivated as a high-level semantic feature detection task. Like edges, corners, blobs and other feature detectors, the proposed detector scans for feature points all over the image, for which the convolution is naturally suited. However, unlike these traditional low-level features, the proposed detector goes for a higher-level abstraction, that is, we are looking for central points where there are pedestrians, and modern deep models are already capable of such a high-level semantic abstraction. Besides, like blob detection, we also predict the scales of the pedestrian points, which is also a straightforward convolution. Therefore, in this paper, pedestrian detection is simplified as a straightforward center and scale prediction task through convolutions. This way, the proposed method enjoys an anchor-free setting. Though structurally simple, it presents competitive accuracy and good speed on challenging pedestrian detection benchmarks, and hence leading to a new attractive pedestrian detector. Code and models will be available at https://github.com/liuwei16/CSP.

This work develops a novel end-to-end deep unsupervised learning method based on convolutional neural network (CNN) with pseudo-classes for remote sensing scene representation. First, we introduce center points as the centers of the pseudo classes and the training samples can be allocated with pseudo labels based on the center points. Therefore, the CNN model, which is used to extract features from the scenes, can be trained supervised with the pseudo labels. Moreover, a pseudo-center loss is developed to decrease the variance between the samples and the corresponding pseudo center point. The pseudo-center loss is important since it can update both the center points with the training samples and the CNN model with the center points in the training process simultaneously. Finally, joint learning of the pseudo-center loss and the pseudo softmax loss which is formulated with the samples and the pseudo labels is developed for unsupervised remote sensing scene representation to obtain discriminative representations from the scenes. Experiments are conducted over two commonly used remote sensing scene datasets to validate the effectiveness of the proposed method and the experimental results show the superiority of the proposed method when compared with other state-of-the-art methods.

Most existing video-based object detection methods utilize successful image-based object detector as a base network, and additionally exploit temporal information with either bounding-box post-processing or feature enhancement from multiple frames. However, little work has been done on directly modeling temporal motion in an efficient way for detection in surveillance videos. In this paper, a simple but effective module, denoted as motion-from-memory (MFM), is proposed to encode temporal context for improved detection in surveillance videos. With appearance features extracted from a base CNN, the MFM module maintains a dynamic memory for each input sequence and output motion features on each frame. This module costs minor additional model parameters and computations, but is very helpful for moving object detection, especially in surveillance videos. Thanks to the additional MFM module, the performance of a light-weight MobileNet-based Faster RCNN detector is boosted by 13.93% in mAP, achieving comparable performance to that of strong ResNet-50-based. When MFM is integrated into an even weaker but faster single-stage detector, it ranks the second best one among all published works when submitted to the DEETRAC vehicle detection benchmark, with 69.10% mAP, compared to 69.87% of the best one. However, when running speed is considered, the proposed method is the fastest one, running at 33 FPS with 540×960 surveillance videos on a moderate commercial GPU (NVIDIA GTX 1080Ti), which is about 3 times faster than the second fastest one.

Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model's redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral-spatial features.

Microwave radiometers are the key sensors to globally monitor environmental parameters; however, it suffers from its low and nonuniform spatial resolution. In this paper, a superresolution (SR) technique based on image degradation and residual network is proposed to enhance the spatial resolution of microwave radiometer data. Specifically, an improved degradation model is proposed to construct pairs of high-resolution (HR) and low-resolution (LR) data for training and testing. In addition, a new residual network connected by the SR main and gradient auxiliary branches in parallel is designed to achieve SR reconstructions, where eight-channel gradient maps extracted from LR data are input into the auxiliary branch to help to reconstruct. SR results are eventually generated by the trained SR network. Experiments executed on both simulated and actual data demonstrate the soundness and the superiority of the proposed SR technique.

The cardinalized probability hypothesis density (CPHD) filter was proposed as a practical approximation to the multi-target Bayes filter with tractable computational complexity. However, the CPHD filter has limitations in dealing with missed detections, extracting target state in its particle implementations, and in maintaining track continuity. In this paper, a new improved CPHD filter is proposed as a solution to address these limitations, with efficient track continuity and extraction. This filter inherits tractable computational complexity and addresses the drawbacks of the standard CPHD filter. The proposed filter is implemented using Gaussian mixtures, and simulation results demonstrate the effectiveness of the proposed filter compared to the conventional multi-taraet filter in challenging scenarios.

Vehicle detection is still a challenge in complex traffic scenes, especially for vehicles of tiny scales. Though RCNN based two-stage detectors have demonstrated considerably good performance, less attention has been paid to the quality of the first stage, where, however, tiny vehicles are very likely to be missed. In this paper, we propose a deep network for accurate vehicle detection, with the main idea of using a relatively large feature map for proposal generation, and keeping ROI feature's spatial layout to represent and detect tiny vehicles. However, large feature maps in lower levels of a deep network generally contain limited discriminant information. To address this, we introduce a backward feature enhancement operation, which absorbs higher level information step by step to enhance the base feature map. By doing so, even with only 100 proposals, the resulting proposal network achieves an encouraging recall over 99%. Furthermore, unlike a common practice which flatten features after ROI pooling, we argue that for a better detection of tiny vehicles, the spatial layout of the ROI features should be preserved and fully integrated. Accordingly, we use a multi-path light-weight processing chain to effectively integrate ROI features, while preserving the spatial layouts. Experiments done on the challenging DETRAC vehicle detection benchmark show that the proposed method largely improves a competitive baseline (ResNet50 based Faster RCNN) by 16.5% mAP, and it outperforms all previously published and unpublished results.

Recently, deep models have shown powerful ability for remote sensing scene representation. However, the training process of these deep methods requires large amount of labelled samples while usual remote sensing image datasets cannot provide enough training samples. Therefore, the learned model is usually suboptimal. To solve the problem, this work focuses on obtaining multiple choices by training multiple models simultaneously, and then the human oracle can choose a proper one from these choices. However, training several models separately usually makes the obtained results similar. This paper tries to diversify the obtained choices by encouraging the obtained choices to repulse from each other. Experiments are conducted on Ucmerced Land Use dataset to validate the effectiveness of the proposed method to provide multiple diversified choices.

Deep models with multiple layers have demonstrated their potential in learning abstract and invariant features for better representation and classification of remote sensing images. Moreover, metric learning (ML) is usually introduced into the deep models to further increase the discrimination of deep representations. However, the usual deep ML methods treat the training samples in each training batch in the stochastic gradient descent-based learning procedure independently, and thus, they neglect the important contextual (structural) information in the training samples. In this paper, we first introduce deep structural ML (DSML) into the literature of remote sensing scene classification and specifically capture and use the structural information during the training on the remote sensing images. Further analysis demonstrates that DSML usually makes many learned metric parameters similar. This similarity leads to obvious model redundancy and thus decreases the representational ability of the model. To address this problem, this paper proposes a new diversity-promoting DSML (D-DSML) method by regularizing the learning procedure by a diversity-promoting prior over the parameter factors. The proposed D-DSML encourages the parameter factors to be uncorrelated, such that each factor can model unique information, and thus, the model's description ability and classification performance would be significantly improved. Experiments over six real-world remote sensing scene data sets demonstrate that the proposed method obtains much better results than those obtained by the original deep models and has comparable or even better performances when compared with state-of-the-art methods.

In this work, a diversified deep structural metric learning is proposed for remote sensing image classification. Firstly, a deep structural metric learning is introduced to take full advantage of structural information of training batches. Secondly, we impose a diversity regularization over the factors of deep structural metric learning to encourage them to be uncorrelated, such that each factor tends to model unique information during the training phase and all factors sums up to capture a larger proportion of information. The diversified model could benefit the classification of remote sensing images. Experiments are conducted on two real-world remote sensing image datasets to evaluate the effectiveness and wide applicability of the proposed approach. The results show that our proposed method can obtain comparable or even better results on remote sensing image classification when compared with the recent results.

The earth observing mission scheduling problem is an important real-world problem that impacts the opportunity of dealing with emergencies and the collection of hazard monitoring research data. The period of traditional data acquisition cycle for earth science research is often too long to receive some important observation data in time. Besides, with rapid developing of sensor web techniques, the envisioned future earth scientists and emergency workers would like to investigate the natural phenomenon by using large numbers of sensors based on different platforms, such as satellite, balloon, aircraft and ground-based. How to schedule these sensors that are frequency agile and capable of multi-scene observations for completing a hazard monitoring research mission is a difficult task. In this paper, we focus on solving two problems above. The active observation model based on hazard monitoring domain knowledge is proposed for reducing responsive time of abnormal phenomenon. And then information gain model is introduced for evaluating observation schedule. On this basis, multi-platform sensor optimization scheduling model is constructed. Simulation and analysis show that the proposed model can solve the problem effectively. Moreover, the normal requests are also taken into account during these events for maximizing the value of various sensors.

This paper proposes a semi-supervised nonnegative matrix factorization algorithm for face and gait recognition. The proposed algorithm imposes hard constraints on the labelled data points, such that the data points that belong to the same class are projected to the same lower dimensional point. In addition, it introduces a graph Laplacian regularization term that preserves the local geometry structure of the data by penalising large distances between the projections of points that are close in the original space. This results in a constrained optimization problem, that is solved using block coordinate descent with multiplicative update rules. Experimental results on several publicly available datasets demonstrate that proposed method performs in par or considerably better than state of the art methods.

From the perspective of sparse signal representation, an autofocusing method in inverse synthetic aperture radar imaging is proposed. Different from the idea of taking the entropy or contrast as the optimization objective in the presently existing algorithms, this method exploits the intrinsic sparsity distribution of scattering centers to compensate the indeterminacy of the measurement system, and a universal regularization model is constructed to simultaneously balance the measurement errors and the sparsity constraint. Accordingly, an effective iterative algorithm on the basis of solving a matrix equation and a trigonometric equation is proposed to estimate the phase errors, which makes the conventional minimum entropy method (MEM) a special case of the proposed method. Specifically, with the sparsity measure being selected as the logarithm function, an analytic representation is derived for the solution of the matrix equation, and the convergence and computational complexity of the proposed method is also discussed. Experimental results show that the proposed method outperforms the present data-driven algorithms in terms of efficiency and robustness, such as MEM, phase gradient autofocusing algorithm, and maximum contrast method.

We first establish the dynamic Radar Cross Section (RCS) signal model for a conical ballistic missile warhead with precession motion. Scintillation is modeled as a log-normal multiplicative noise. The distribution of the obtained RCS signal is nonGaussian and cannot be obtained in closed-form. Hence, the exact Maximum Likelihood Estimation (MLE) of the pertinent parameter, the missile precession frequency, is untractable. We propose three pseudo MLE approaches. The first approach, called GML, enforces a Gaussian distribution on both the additive and multiplicative noise components. The second approach, called ML8, ignores the additive noise in the measured RCS. The third approach, called AOML, ignores the multiplicative noise. Simulations show that accounting for the multiplicative noise in the estimation significantly improves estimation performance.

Geometrical features are widely used in the SAR ship recognition. Two-dimensional (2D) features are sometimes ambiguous and the three-dimensional (3D) ones are hence preferred. Most of the existing researches are based on the 3D coordinate estimation of the scattering centers. These techniques may lose efficiency when working with ocean ships, due to their complicated motion on the waves and the scattering center scintillation among different views. In this work, the ship is simplified as an ellipsoid and the feature extraction problem are transformed into the size and aspect estimation of the ellipsoid. Considering that the projection of the ellipsoid on a SAR imaging plane is an ellipse, whose parameters can be estimated from the ship image, a regularized Least Square (LS) problem is constructed, with SAR images under known elevations and unknown azimuths. The solution of the problem gives the radar azimuth estimation. The 3D geometrical features, such as the size in each dimension, the Length-to-Width Ratio (LWR) and Length-to-Height Ratio (LHR), are evaluated in the end. Experiment with the electromagnetic (EM) simulated images shows the feasibility and efficiency of the ellipsoid approximation and the performances of the estimation are examined via Monte Carlo techniques.

Synthetic aperture radar (SAR) chip segmentation is a crucial step in SAR automatic target recognition. When interested objects are placed on the ground, shadow is observed, and then the goal of SAR chip segmentation is to delineate target and shadow regions from background clutter. Due to fluctuations in SAR images, purely intensity-based methods lost many meaningful target and shadow regions. This drawback maybe conquered by introducing extra contextual information. The spatial relation between target and shadow regions is a kind of important contextual information, but rarely exploited in previous methods. In this paper, according to SAR imaging geometry, we conclude that target and shadow regions should be connected along the range direction. Based on this inter-connectivity between target and shadow regions, a new SAR chip segmentation method called SRPF-MRF is proposed. The new method introduces a Spatial Relational Potential Function (SRPF) term as constraint on the inter-connectivity between target and shadow regions, into Markov Random Field (MRF) based segmentation method. By SRPF-MRF segmentation, the segmented target and shadow is more complete, and therefore brings benefits to the subsequent feature extraction and object recognition. Finally experimental results on MSTAR dataset are given to show the superiority of SRPF-MRF method.

This paper presents an efficient algorithm combining the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method with an improved discrete complex image method (DCIM) for electromagnetic scattering from electrically large objects in both lossless and lossy multilayered media. The required spatial Green's functions obtained by the improved DCIM are accurate both in the near- and far-field regions without any quasi-static and surface-wave extraction. Then, the scattering by buried objects is considered using the BCGS-FFT method combined with the improved DCIM. Numerical results show the improved DCIM can save tremendous CPU time in scattering involving buried objects.

In this letter, the local degree-of-freedom (LDOF) theorem for reduced-dimension space-time adaptive processing (STAP) methods is presented, and a rigorous proof is provided. LDOF is more valuable for practical STAP methods than conventional full degrees of freedom. The effectiveness of the LDOF theorem is verified, and the influences of some operations in practice on the LDOF are analyzed by simulations.

Geophysical model function is the basis for the wind vector retrieval with scatterometer and a number of models have been developed to operationally retrieve the ocean surface wind in the past three decades. However, none of the operational models ever took the water surface temperature into account in its modeling, which is considered to have some effect on the ocean backscattering, and in turn on the model accuracy. Taking Sea Winds as an example, this paper attempts to develop new geophysical model functions with surface temperature to be taken into account by using its level 2A data and corresponding buoy data. For contrast, two independent models are established for the ocean water and fresh water respectively. The modeling results and analysis indicate that some effect of the surface temperature on backscatter were found for both types of water, but with a larger extent of the temperature effect for fresh water.

Geophysical model function(GMF) is the basis and prerequisite for the ocean surface wind vector retrieval with scatterometers. Among many operational models, the Qscat-1 model was specifically developed for SeaWinds scatterometer and is being applied to its operational wind retrieval. This paper is to validate the accuracy of the Qscat-1 model by using some SeaWinds Level 2 data and corresponding buoy data. First, a comparison between L2B and co-located buoy wind speed was made to analyze the systematic bias between them, and then a new geophysical model function was established using the match-ups of the L2A and Buoy data to further valuate the accuracy of the Qscat-1 model. The analytical and modeling results indicate that there may be some systematic error in the Qscat-1 model.