Aircraft target detection in synthetic aperture radar (SAR) images is challenging due to discrete scattering points and complex environments. With neural network advancements, this task has become more feasible, although the existing networks often have too many parameters for performance-constrained devices like drones or sacrifice performance to reduce parameter count. To address this, we propose a lightweight multistage SAR aircraft target detection network, termed MSADNet, featuring a novel multistage asymmetric aggregation network (MAAN) module. The MAAN module enhances feature extraction through an improved asymmetrical bottleneck network and introduces a multiorder attention mechanism to focus on critical features. By carefully managing the shortest and longest gradient paths, the MAAN module achieves an organic integration of both, significantly improving network performance. The backbone network is constructed using the MAAN module, and the neck network employs a feature pyramid network (FPN) + PAN structure, integrating the MAAN module with a coordinate attention (CA) module. An anchor-free detection head is used for aircraft target detection. Testing on the SAR-AIRcraft-1.0 dataset demonstrates that MSADNet achieves a mean average precision (mAP) of 96.15% with only 4.88 M parameters. Compared to YOLOv8-s, MSADNet improves mAP by 0.61 percentage points while reducing the parameter count by 6.29 M, indicating high-performance detection with a low parameter count.

Tomography synthetic aperture radar (TomoSAR) is a cutting-edge radar observation technique that has the ability to produce three-dimensional images and can effectively extract forest vertical structure parameters, including forest height, a key forest parameter closely related to forest biomass and carbon storage. However, the phase errors in the TomoSAR data are unavoidable due to the elements such as orbit errors, which can seriously affect the quality of tomographic imaging and thus affect the accuracy of forest parameter extraction. To address this issue, various methods have been proposed. Nevertheless, they still exhibit restrictions when addressing phase errors with complex trends. To solve such problem, a novel method was developed and implemented in this paper, which includes two steps and remove parts of the phase errors with different trends sequentially. First, a wavelet decomposition and polynomial fitting-based approach was applied to each track to remove the slowly but significantly spatially-varying part of the phase errors. Secondly, the modified autofocusing algorithm is proposed to correct the remaining phase errors, which adopted the two-dimensional image entropy as the optimization indicator, providing stronger robustness compared to traditional indicator. Furthermore, in order to overcome the initial value dependency of the traditional search method, the proposed autofocusing algorithm used the particle swarm algorithm as search engine. After the phase error correction, the forest height was extracted by identifying the upper and lower boundary of the forest from the corrected TomosAR profiles. Two P-band datasets obtained in north China are adopted to examine the proposed phase error correction method. Experimental results show that compared with traditional autofocusing algorithm, the proposed method can achieve higher quality tomographic imaging results. On the basis of TomoSAR imaging, higher precision forest height extraction is obtained based on the new method.

The time-series InSAR technique is commonly used for surface subsidence monitoring, providing insights into the trends of environmental changes. In this study, we employed the time-series InSAR technique in the Hunshandake Sandy Land, which will help evaluate the effectiveness of desertification control measures. The results show that there is no significant widespread subsidence or uplift in the Hunshandake Sandy Land, indicating limited movement of sand particles and minimal impact from human activities. However, within certain small areas of the study region, notable surface subsidence and uplift phenomena were observed, which can be attributed to saline-alkali land presence. Additionally, InSAR monitoring revealed pronounced decorrelation in the crescent-shaped dune region, suggesting rapid surface changes occurring on these dunes.

Extracting buildings in Earth observation is a vital task for various applications. Medium-resolution remote sensing images can offer notable advantages like reduced storage needs and faster acquisition, facilitating more rapid revisits of a certain area. However, they often lack detailed building features, posing challenges for accurate segmentation. To address this, we present CDNet (Cross-resolution Distillation Network), a novel framework designed to boost building extraction accuracy on medium-resolution images, resulting in computational efficiency and low time cost. CDNet operates on a teacher-student network paradigm, employing cross-resolution knowledge distillation. The teacher network processes super-resolved images, endowing the student network with crucial priors to adeptly handle lower-resolution originals. Our method achieves a cutting-edge performance on the Multi-Temporal Urban Development SpaceNet (MUDS) Dataset, showcasing exceptional accuracy with a mean Intersection over Union (mIoU) of 62.97 and Boundary over Union (BIoU) of 29.35, respectively.

Coal mining activities will result in long-term multi-level surface deformation, causing significant damage to the infrastructure and environment. Currently, the three-dimensional (3-D) mining deformation could be resolved based on single-track or multi-track Interferometric Synthetic Aperture Radar (InSAR). Before starting to monitor a study area, it is necessary to determine what sort of methodology would best meet the accuracy requirement of 3-D deformation. To accomplish this, we present an error propagation model that can clarify the relationship of error propagation between the multi-track measurements in the radar line-of-sight (LOS) or azimuth directions and the 3-D deformation in the vertical, east, and north directions. Simulation results show that the accuracy of our proposed model could reach 1.2mm, 1.3mm, and 11.6m in the vertical, east, and north directions, respectively. Accordingly, we give the precision assessment of five cases for resolving 3-D mining deformation by using our proposed model.

The combination of the LOS and azimuth measurements at ascending and descending orbits could improve the three-dimensional (3-D) deformation precision, with the accuracy dependent on the magnitude of stochastic error within InSAR measurements. Accordingly, we present the simulations by using a new 3-D deformation inversion method called CovRM-InSAR. In the case study, we applied four InSAR measurements with varying standard deviation (SD) in the LOS and azimuth directions from two tracks to map 3-D deformation. The results show that it is beneficial for improving the east and north deformation whether considering the correlation between azimuth measurements in the covariance matrix. The precision of 3-D deformation resolved by CovRM-InSAR has close accuracy in the vertical and north directions and improved by up to 88% in the east direction when compared with the least square (LS).

Object Detection in Synthetic Aperture Radar (SAR) images holds significant potential for diverse remote sensing applications. Nevertheless, the substantial cost associated with object-level annotation poses a formidable challenge in SAR object detection. While cross-modal learning offers a promising solution to address this annotation hurdle by using annotated optical images, existing methods falter in achieving satisfactory results due to the significant modal gap between optical and SAR. In this study, we introduce the Decoupled Multi-Teacher (DMT) framework, specifically tailored for object detection in SAR images. By strategically decoupling cross-modal learning from detector training, DMT adeptly mitigates challenges associated with co-training disparate modalities. Our experiments showcase the substantial improvements achieved by the proposed method in SAR image object detection.

Synthetic aperture radar (SAR) has a wide range of applications in resource exploration, environmental monitoring, urban and rural planning, among others. However, SAR images often suffer from speckle noise, which requires the use of despeckling techniques. With the increasing availability of SAR time series, there is potential to develop more efficient despeckling methods. Nevertheless, in speckle reduction, the coherence between multitemporal SAR images creates new challenges. In this study, a patch-based low-rank approximation (PLRA) method is proposed for SAR time series despeckling using the RABASAR framework, which effectively eliminates temporal fluctuations and speckles. First, a similar patch search approach in time series is introduced to remove time-dependent changes by analyzing fluctuation models. Then, a low-rank approximation method based on patch stacks is proposed to obtain a low-rank image for noise filtering. Furthermore, the low-rank image is integrated into the RABASAR framework to improve the despeckling process. Experimental results demonstrate the superior performance of the proposed method in preserving image texture details, mitigating temporally correlated disturbances, and reducing speckle noise in comparison to other state-of-the-art methods.

In contemporary security detection systems, the utilization of millimeter-wave radar has assumed a central role owing to its non-contact and innocuous nature. This study addresses the challenging issues of detecting low-resolution and small targets in active millimeter wave (AMMW) images concealed detection. We identify a prevalent drawback in existing detectors, specifically the adoption of strided convolution or pooling layers, leading to a loss of crucial details that adversely impacts the detection rate. To overcome this limitation, we introduce a novel convolutional structure, termed Wavelet-Conv, which maintains information integrity while segregating features from high and low frequency bands, effectively replacing the unfavorable design. Furthermore, we harness the wavelet transform to enhance channel and spatial attention mechanisms, enabling more effective utilization of frequency band features and ensuring interpretability in the computational process. In this pursuit, we integrate the proposed Wavelet-Conv and Wavelet-Attention modules into the YOLOv8 framework, culminating in a unified model, termed Wavelet-YOLO. Through rigorous experimental validation on two AMMW datasets, our approach exhibits superior performance by significantly enhancing the recall and mean average precision (mAP) of small targets in AMMW images, while maintaining competitive inference speed. Extensive experiments demonstrate the outperformance of our proposed method over existing state-of-the-art approaches.

Three-dimensional (3-D) deformation could be resolved using multi-track Interferometric Synthetic Aperture Radar (InSAR), with the accuracy dependent on the magnitude of multi-source errors within InSAR measurements. To improve the precision of 3-D deformation, it is essential to understand the error propagation mechanism and then develop the methodology for reducing error impacts in 3-D decomposition processing. In this article, we present an error propagation model that incorporates both systematic and stochastic error propagation, which determines the error contribution of the multi-track InSAR measurements in the 3-D direction. The systematic error propagation includes generic systematic error and additional systematic errors (ASE) in the vertical and east directions caused by neglecting the north component. For stochastic error propagation, we construct the covariance matrix by considering variance and correlation from different InSAR measurements when using differential and multi-temporal InSAR techniques. Accordingly, we propose a new 3-D deformation inversion method, combining the covariance matrix and L2-norm regularization based on multi-track InSAR (CovRM-InSAR) to improve the precision of 3-D deformation with noise reduction. In the case study, we applied Sentinel-1A and ALOS-2 InSAR datasets from four tracks to map 3-D velocity in Wuhai and analyzed the time-series error propagation and 3-D uncertainty. The precision of 3-D deformation resolved by CovRM-InSAR has improved by up to 90%, 44%, and 98% in the vertical, east, and north directions, respectively. Additionally, the CovRM-InSAR has effectively reduced the stochastic errors by up to 38%, 15%, and 90% in the vertical, east, and north directions, respectively.

Long non-coding RNAs (lncRNAs) played essential roles in nearly every biological process and disease. Many algorithms were developed to distinguish lncRNAs from mRNAs in transcriptomic data and facilitated discoveries of more than 600 000 of lncRNAs. However, only a tiny fraction (<1%) of lncRNA transcripts (~4000) were further validated by low-throughput experiments (EVlncRNAs). Given the cost and labor-intensive nature of experimental validations, it is necessary to develop computational tools to prioritize those potentially functional lncRNAs because many lncRNAs from high-throughput sequencing (HTlncRNAs) could be resulted from transcriptional noises. Here, we employed deep learning algorithms to separate EVlncRNAs from HTlncRNAs and mRNAs. For overcoming the challenge of small datasets, we employed a three-layer deep-learning neural network (DNN) with a K-mer feature as the input and a small convolutional neural network (CNN) with one-hot encoding as the input. Three separate models were trained for human (h), mouse (m) and plant (p), respectively. The final concatenated models (EVlncRNA-Dpred (h), EVlncRNA-Dpred (m) and EVlncRNA-Dpred (p)) provided substantial improvement over a previous model based on support-vector-machines (EVlncRNA-pred). For example, EVlncRNA-Dpred (h) achieved 0.896 for the area under receiver-operating characteristic curve, compared with 0.582 given by sequence-based EVlncRNA-pred model. The models developed here should be useful for screening lncRNA transcripts for experimental validations. EVlncRNA-Dpred is available as a web server at https://www.sdklab-biophysics-dzu.net/EVlncRNA-Dpred/index.html, and the data and source code can be freely available along with the web server.

Object detection in remote sensing images (RSIs) is crucial for ground observation applications such as land surveying, urban planning, and precision agriculture. With the advent of convolutional neural networks, the performance of object detection in RSIs has been significantly improved. However, most studies have focused exclusively on the feature extraction, processing, and representation of geographical objects. The lack of integration of relevant common-sense knowledge has led to some obvious missed detections and illogical false alarms in the detection results. This letter proposes a novel and widely applicable approach for updating and fusing object co-occurrence knowledge into a two-stage multi-class object detection framework, called Auto Learner of Co-occurrence. The method captures the correlation between different object categories during the training process with the Co-occurrence Knowledge Updating module and utilizes the established co-occurrence weight matrix to enhance classification with the Knowledge Fusing module. The effectiveness and adaptability of the proposed method were validated through experiments on two multi-class object detection datasets using four popular two-stage detectors. The experiments also indicate that the speed penalty and the parameter increase resulting from the method are negligible.

Azimuth multichannel synthetic aperture radar (SAR) working in beam steering modes can effectively obtain images with wide unambiguous swath and high azimuth resolution. However, azimuth phase center fluctuation (APCF) phenomenon can be induced by the beam rotation in azimuth. Consequently, addition phases will be introduced in echoes of different channels. Different beam steering laws result in different degrees of APCF phenomenon, which lead to different azimuth channel phase errors. These phase errors lead to paired azimuth ambiguities or targets offset in imaging results. In this letter, the effects of different beam steering laws on the imaging results are analyzed, and the total additional wave paths required by different beam steering laws are compared, aiming at providing a reference for the design of the practical beam steering SAR system. The impacts of different beam steering laws on the imaging results are verified by simulation experiments.

Orthogonal frequency division multiplexing (OFDM) chirp waveform is considered a good choice in the waveform design for clutter suppression, which is due to its excellent characteristics such as spectral containment, phase diversity, great dynamic spectral allocation and high degree of freedom. Considering the purpose of clutter suppression, an OFDM chirp waveform design method based on multiple groups of subchirp durations optimization is proposed to improve the output signal-to-clutter-plus-noise ratio (SCNR) in this paper. The output SCNR is closely related to the waveform spectrum, so the waveforms’ energy spectral density functions are analyzed first to build the relation between the waveform parameters and spectra. Then, the multiple groups of subchirp durations optimization based on maximum SCNR is proposed and solved by an optimization method based on the sequential quadratic programming. Finally, the proposed method is verified and the optimized waveform is compared with the general waveform and the waveform with optimized single group of subchirp durations. The results show that the waveform optimized by the proposed method has higher output SCNR and the SCNR increment compared with the general waveform increases with the number of subcarriers. Besides, the high sidelobes of the general waveform are also greatly reduced.

Azimuth multichannel synthetic aperture radar (SAR) working in the sliding spotlight mode can effectively achieve ultrahigh resolution and wide swath imaging. However, multichannel sliding spotlight SAR echoes with large pulse bandwidth and azimuth steering angle are difficult to reconstruct and process due to the large range frequency-dependent Doppler centroid varying frequency support. To resolve this problem, two azimuth preprocessing approaches both based on the range frequency-dependent deramping strategy are proposed in this article. In both approaches, echoes are handled by a range frequency-dependent azimuth deramping function at first, which assign the signal measurements to unambiguous Doppler frequencies. Afterward, in the first preprocessing approach, azimuth multichannel raw data can be reconstructed and combined before azimuth upsampling in the conventional two-step preprocessing technique. The other way to handle the deramped multichannel raw data is the modified full aperture azimuth multichannel sliding spotlight reconstruction approach, which embeds azimuth multichannel reconstruction into azimuth convolution between the deramped raw data and the selected range frequency-dependent azimuth deramping function. Furthermore, the azimuth data in the second approach should be resampled to obtain a uniform azimuth sampling interval. Computational complexity and effects on the following imaging algorithm of both approaches are analyzed and compared. Simulation results validate the proposed two azimuth multichannel preprocessing approaches.

Staggered synthetic aperture radar (SAR) can effectively stagger range blind areas by periodically changing the pulse repetition interval (PRI), to achieve continuous imaging with an ultrawide swath. In conventional staggered SAR, azimuth samples are nonuniformly distributed due to the variable PRI, and complex resampling processing, such as best linear unbiased (BLU) interpolation and multichannel reconstruction processing, is required. In this article, a strict rule of PRI variation and phase center adjustment (PCA) is proposed for staggered SAR, to solve the problem of nonuniform azimuth sampling. In the proposed joint strategy of PRI variation and PCA, the relevant parameters for PCA are first determined. Then, the PRI sequence is designed according to PCA parameters to stagger blind ranges. Finally, the PCA rule is designed according to the designed PRI sequence and PCA parameters. During the data acquisition interval, the effective phase center is periodically adjusted pulse by pulse according to this rule, making the azimuth samples totally uniformly distributed. In addition, the discontinuously distributed missing samples can be estimated by signal estimation approaches. With the proposed joint rule of PRI variation and PCA, the emergence of false targets in imaging results can be avoided, and the complex resampling processing can be relieved to reduce the computational complexity. Simulation experiments on imaging results verify the advantages of this strategy.

Radio frequency interference (RFI) sources pose threats to wideband synthetic aperture radar (SAR) systems and accurate SAR image interpretation. Since most of RFI sources are narrowband, notch filtering is a simple but effective method for RFI suppression. In this letter, a modified two-step notch filtering approach combined with linear prediction is proposed to improve the SAR image quality. The notch filtering is used to mitigate narrowband RFI energy, while the linear prediction is introduced to recover the missing range spectral component of the SAR raw data from the desired scene, which is removed together with RFI sources by the notch filter. Because of the Gibbs phenomenon in Fourier series, the small residual RFI energy after notch filtering is enough to cause image visual disturbances and affect the accuracy of the following missing range spectrum extrapolation. The two-step notch filtering with a limited bandwidth is applied for better RFI sources mitigation. Simulation results on both simulated targets and real SAR raw data validate the proposed approach.

Array SAR can overcome the problems such as overlay and shadow caused by the terrain that the traditional SAR observes with large changes in slope. However, compared with traditional SAR, array SAR inevitably has multi-channel amplitude and phase errors due to its large number of channels. If these errors are not corrected, radar imaging will be degraded. This paper proposes a method based on time-domain processing to compensate for possible errors in the time domain. Finally, the effectiveness of this method is verified by CS algorithm experimental simulation.

In this paper, attention was paid to the impacts of RFI (Radio Frequency Interference) on SAR (Synthetic Aperture Radar) image ship detection with a proposed net structure based on deep learning. The RFI of different intensities can destroy the quality of SAR images, and pose difficulty for ship detection in marine environment. We acquired the dataset that contains numerous sample slices from two satellites, and took different ranges of SIR (Signal to Interference Ratio) to evaluate the robustness of the framework on the conditions. The experimental results show that the SAR images corrupted by RFI can affect the detection performance greatly. And it may provide suggest to researchers to conduct the interference properly when using the deep learning algorithm to monitor the ships in SAR images.

Compared with the conventional 3-D synthetic aperture radar (SAR) system, the arc 3-D SAR can achieve a wide azimuth observation extent. In this letter, a far-field pseudopolar format imaging algorithm for the ground-based arc 3-D SAR is presented. Different from the existing algorithms, the proposed algorithm exploits keystone formatting and fast Fourier transforms (FFTs) to obtain a pseudopolar grid in the elevation direction, then complex multiplications and FFTs are performed in the range-azimuth wavenumber domain to complete 3-D focusing. The advantages of this method are its low computational complexity and high efficiency. The sampling criteria and computational complexity are also discussed in this letter. Finally, the performance of the proposed algorithm is validated with numerical simulations.

Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question.Most approaches train attention models from a coarsegrained association between sentences and images, which tends to fail on small objects or uncommon concepts.To address this problem, this paper proposes a multi-grained attention method.It learns explicit wordobject correspondence by two types of wordlevel attention complementary to the sentenceimage association.Evaluated on the VQA benchmark, the multi-grained attention model achieves competitive performance with stateof-the-art models.And the visualized attention maps demonstrate that addition of objectlevel groundings leads to a better understanding of the images and locates the attended objects more precisely.

According to the characteristic of different air bubbles and sediments content in the Yellow River ice (YRI), the YRI is classified into three types: thermal ice (TI), frazil ice (FI) and consolidated ice (CI). In this paper, we are modeling YRI firstly, and the backscattering coefficient of the ice cover based on this model is deduced on the basis of the integral equation model (IEM) and the vector radiation transfer theory (VRT). Due to the characteristics of different types of YRI corresponding to different microwave electromagnetic scattering, a method of classification of YRI based on decision tree is proposed. The method uses horizontal polarization backscattering coefficient, correlation coefficient and cross polarization power to classify different types of YRI. Finally, the classification accuracy is evaluated by using the confusion matrix (CM). The results show that the classifier is effective for the YRI classification.

It is difficult to classify Hunshandake sandy land due to complex terrain, an improved classification method is proposed in this paper to promote the effect of classification. Firstly, scattering characteristics of ground objects are extracted. Then, the extracted features is combined to Jensen-Bregman logDet(JBLD) for classification. Finally, Hunshandake sandy land obtained by Radarsat-2 satellite is classified to verify the effectiveness of the method.

Ground-based SAR has become an important remote sensing technology means for deformation monitoring in recent years. However, in order to achieve higher efficiency data acquisition and wider observation, arc antenna array technology is applied to ground-based SAR. In this paper, a polar format imaging algorithm for arc antenna array ground-based SAR is proposed. Firstly, a brief background on ground-based SAR and arc antenna array are provided. Then, the signal model and imaging algorithm of arc antenna array ground based-SAR are introduced. Finally, simulation result is presented.

At present, various models are developed for soil moisture retrieval, but the application of polarimetric SAR data to retrieve soil moisture in sandy land is rare. Therefore, it is necessary to develop a method for retrieving soil moisture in sandy land. In this paper, we proposed a model to estimate the soil moisture in Hunshandake Sandy Land. The model does not need to take into account the surface roughness, only using the VV and HH polarization backscattering coefficients can be used to retrieve soil moisture. In addition, diversity of the vegetation over the sandy land is difficult to predict. We selected five samples to analyze the effect of vegetation on the inversion results of soil moisture in sandy land.

In this paper we propose a new extraction scheme for linear arranged targets in polarimetric SAR images based on a contrario theory. In this scheme, to reduce the influence of speckle, firstly a polarimetric whitening filter is applied to combine four images from different channels into a single channel image. Then a Cell-Averaging Constant False Alarm Rate detector with Weibull clutter background and a post-processing operator is used to detect point-like targets in the image. Finally, a searching approach based on contrario theory is applied to extract the linear arranged targets from other point-like targets. The experimental results varify the effectiveness in linear arranged targets extraction.

PolSAR features have great significance in application of vegetation classification, which can explain the scattering mechanism of the vegetation; the decision tree classifier not only can obtain good classification accuracy, but also can adjust the classification results, as well as make full use of PolSAR features to explain the scattering mechanism of the targets because of its simple and hierarchical classifier structure. Since all the classification methods are composed of two parts: feature selection and classifier selection, this paper established a classification method with PolSAR features as selected feature and decision tree as adopted classifier. As decision tree classifier is flexible in discriminant rules, the expected design of the experimental scheme introduces multiple data sources, multiple features and multiple classifiers into the framework of this classification method. In addition, discussion about how to improve the classification accuracy of the specific target has been made. The experiment of AIRSAR-Flevoland data illustrates the feasibility of this method.

Natural geological disasters such as landslide, mudslide and man-induced mine collapse, landslide and other mountain deformations seriously endanger the personal safety and property of people. GB-SAR (Ground-Based Synthetic Aperture Radar) imaging radar has been developing as an important technical for deformation monitoring. In this paper, a novel multi-aspect ground-based SAR deformation monitoring system is developed and introduced. Firstly, a brief background on ground-based microwave imaging radar for high accurately deformation monitoring is provided. Then, system configuration is given. Finally, some preliminary experiments are presented, and the obtained data is processed to further demonstrate the potential capability and precision of the system.

Desertification of semi-arid grasslands is a serious problem for economic development and ecological preservation. Using the Hunshandake Sandy Lands as an example, we present an overview of monitoring of land desertification using two different data sources including TM and MODIS data. The driving mechanisms of Hunshandake Sandy Land desertification are also discussed.

Airborne downward-looking linear-array three dimensional synthetic aperture radar (LA-3D-SAR) can achieve high-resolution three dimensional imagery with a uniform antenna array. However, the actual 3D imagery is unavoidably degraded by amplitude-phase errors due to non-ideal antenna characteristics and motion measurement deviations. This paper investigates the effects of these errors on the forms and the degrees of image quality degradation, and considers the use of corresponding calibration methods to eliminate the effects of errors. Two calibration methods are proposed, which are based on external parallel and point target calibrators, respectively. At last, real data experiments have shown the validity of the analyses as well as the effectiveness of the proposed calibration methods.

Simultaneous synthetic aperture radar (SAR) imaging and ground moving target indication (GMTI) is of great importance in remote sensing applications, but it is difficult to be implemented for existing methods due to the cross-interferences between stationary targets/clutter and moving targets. Generally, the imaged moving targets may be displaced in azimuth according to their radial velocities and superimposed upon clutter at a wrong location. In this paper, we proposes a simple simultaneous SAR and GMTI approach by exploiting the fractional Fourier transform (FrFT) algorithm, different from existing methods that perform first stationary clutter suppression and thereafter handle GMTI via Doppler parameters estimation. The feasibility is verified by simulation results.

The helicopter has wide application foreground in disaster relief owing to its good maneuverability. This paper firstly presents basic principle of MIMO-SAR based on arc antenna array and system configuration in details, establishes the corresponding imaging model and provides a particular antenna configuration of arc antenna array installed on helicopter. Then the responding system resolution theory, sampling criteria and performance parameter of the imaging mode are analyzed and a novel imaging algorithm is developed combined with arc aperture configuration. Finally, during platform takeoff and landing, high resolution image acquisition abilities are demonstrated by numerical simulations. On this basis, a ground based experimental system is constructed, also the feasibility and validity of the proposed mode is demonstrated by mean of experimental data acquisition and processing.

In this paper, in order to detect changes in high resolution remote sensing images, we propose an MRF-based change detection method combined with the semantic information. Two temporal high resolution remote sensing images are represented by features of superpixels. For given images, we transform the change detection problem into a binary classification problem by combining differences in both low-level features and semantic information in MRF smoothing framework. All pixels are divided into two categories: changed or unchanged, so we can extract change information from classification result. Experimental results of two Geo-Eye1 high-resolution remote sensing images at different time demonstrate the efficiency of this proposed method. Detection combined with semantic information can significantly improve the result than only with low-level features. Adding Markov smoothing can also improve the detection results slightly.

In this paper, we propose a saliency detection model for polarimetric SAR images based on inter-patch distances. The model is biology-based as it takes the human visual properties into account. Our model consists of local and global saliency detection, which obtained by multi-scale information extraction. What's more, inspired by the distance measures of different image patches, the model takes full use of the coherency matrix which includes the statistical information of pixels and precisely measures the similarity between patches. The experimental results demonstrate the effectiveness of our method.

In this paper, a spectral clustering based unsupervised classification scheme is proposed for processing large scale polarimetric synthetic aperture radar (PolSAR) data. Due to its high computational complexity, spectral clustering can hardly handle large PolSAR image. To overcome this bottleneck, a representative points based scheme is introduced. Instead of building pairwise affinity graph on the whole data set, we first build a bipartite graph between data points and a small set of selected representative points. Then an approximate large graph is constructed based on this bipartite graph. After that, spectral analysis on the approximate graph is solved efficiently by singular value decomposition (SVD). To integral context information, Markov random fields (MRF) model based smoothing is also performed to get the final clusters. We test the proposed approach on DLR ESAR data set. Experimental results demonstrate its effectiveness and efficiency.

The novel displaced phase center antenna (DPCA) azimuth multichannel technique is well suited to improve the coarser azimuth resolution of the terrain observation by progressive scans (TOPS) mode for a fixed total receive antenna length. However, both the azimuth multichannel nonuniform sampling and the aliased Doppler spectrum in TOPS raise the azimuth data processing difficulty. This letter proposes an innovative full-aperture processing approach, which adds an azimuth derotation step for each azimuth channel before azimuth multichannel data reconstruction and utilizes the prefiltering operation in the two-step focusing technique to resolve both azimuth ambiguity problems caused by the DPCA technique and azimuth beam progressive scanning. In addition to presenting the proposed azimuth preprocessing approach, the impact of the approach on the azimuth ambiguity-to-signal ratio is analyzed in detail. Simulation results validate the proposed multichannel processing approach for the TOPS mode.

This paper analyzes the signal properties of spaceborne squinted sliding-spotlight synthetic aperture radar (SAR). Both the squint angle and the azimuth beam steering during the whole acquisition interval will lead to the Doppler spectrum back-folding. According to the special signal properties of this mode, a new full-aperture focusing approach, which includes three major processing steps, is proposed. In this approach, the first processing step introduces an azimuth convolution and azimuth data mosaic to resolve the azimuth spectral folding phenomenon by generalizing the existing two-step focusing technique for conventional sliding-spotlight SAR data focusing. Afterward, the modified range migration algorithm is adopted to process the resulting raw data. As the azimuth time duration of the raw data is obviously reduced in the first processing step, the obtained SAR image may be back-folded in azimuth. The final azimuth postfiltering step is to resolve the possible aliased SAR image without any azimuth data extension. The proposed full-aperture focusing processor is efficient since only a limited azimuth data extension is required to resolve the back-folded Doppler spectrum and SAR image. Imaging results on simulated raw data validate the proposed imaging approach.

Terrain Observation by Progressive Scans (TOPS) synthetic aperture radar (SAR) and spotlight SAR are advanced SAR imaging modes for wide range swath and high resolution. In order to obtain a wider range coverage, azimuth multichannel is introduced in the literature. Since the azimuth bandwidth of beam steering SAR (BS-SAR; spotlight SAR, sliding spotlight SAR, or TOPS SAR) is much greater than that of a stripmap SAR, a signal reconstruction algorithm used for multichannel stripmap SAR may not be effective for multichannel BS-SAR. In this paper, a multichannel full-aperture azimuth processing algorithm is proposed for a BS-SAR. The key of this algorithm lies in the beam and the azimuth bandwidth compressions of multichannel signals in the Doppler-array and slow time-angle planes, respectively. Through compression processing, the beamwidth and the azimuth bandwidth are smaller than the available angle and equivalent pulse repeating frequency , respectively. Then, an improved post-Doppler STAP method is proposed to recover a 2-D spectrum. With the recovered signal, further processing can be utilized to focus the multichannel signal. Simulation and real data results show the effectiveness of the proposed algorithm.

The inherent limitation between azimuth resolution and range swath width in conventional spaceborne synthetic aperture radar (SAR) systems can be overcome by introducing the displaced phase center antenna technique. In these SAR systems, echoes from all subapertures should be reconstructed and combined together before single-channel SAR processors. However, in the multichannel sliding spotlight and Terrain Observation by Progressive Scans (TOPS) modes, azimuth beam progressive sweeping during the whole acquisition interval leads to that the total Doppler bandwidth spans over several N ·PRF intervals, where N is the number of azimuth channels and PRF is the pulse repetition frequency. As a result, conventional azimuth multichannel reconstruction algorithms for the stripmap mode are not directly suitable for sliding spotlight and TOPS modes. This paper proposes a novel imaging processor for both modes according to their azimuth echo properties. The key point of the proposed focusing processor is the first processing step of multichannel azimuth data reconstruction, which extends the two-step focusing technique to process raw data of the azimuth multichannel case. In addition to azimuth data preprocessing and accurate range cell migration correction steps, the final postprocessing step is added to correct the possible back-folded SAR images. Imaging results on simulated raw data validate the proposed imaging approach.

In order to reap the potential benefits that waveform diversity can provide for spaceborne synthetic aperture radar remote sensing, echoes from different subpulses constituting a complete transmit waveform should be effectively separated at first. This paper presents a new separation approach implemented by an advanced null-steering beamformer on satellite. Compared with common null-steering beamforming, our approach will take into account the characteristics of echo signal from the scene and accordingly embed the finite-impulse response (FIR) filtering process into the modified null-steering beamformer to deal with the issue of pulse extension. In this paper, echo signals generated by multidimensional encoded waveform will be analyzed in detail; based on this analysis, FIR filter and the new null-steering beamformer are derived. Simulation results show that much better separation performance can be obtained by our approach than by conventional null-steering beamforming.

The Terrain Observation by Progressive Scans (TOPS) mode is a novel spaceborne imaging mode which can be used to obtain wide-swath coverage and overcome major drawbacks in conventional ScanSAR. An efficient full-aperture imaging approach, which takes advantage of the two-step focusing technique and the azimuth baseband scaling operation, is presented for processing the TOPS-mode synthetic aperture radar (SAR) data. First, the proposed two-step focusing technique for spotlight and sliding spotlight SAR data focusing is adopted to resolve the aliased Doppler spectrum. Afterward, the following extended chirp scaling processing procedure with azimuth scaling factors is used to implement the residual TOPS raw-data focusing. Since the use of subapertures is avoided and only a limited azimuth-data extension is required, this algorithm is highly efficient. Simulation results validate the proposed imaging approach.