In Synthetic Aperture Radar (SAR) systems, accurate jamming recognition is essential for effective anti-jamming methods. With the rapid development and rich diversity of jamming techniques, compound jamming types has become particularly prominent. However, most existing deep learning-based recognition methods still rely on multi-classification for individual jamming types and sequentially process the signal in a single pulse repetition time (PRT). To overcome these limitations, this letter proposes a novel ML-SwinT-LSTM model for SAR compound jamming sequence recognition, which integrates Swin Transformer (SwinT), Long Short Term Memory (LSTM), and a multi-label classification head (ML head). Specifically, time-frequency (TF) spectrogram sequences, obtained from jamming signals, are fed into the proposed model to extract image features, capture sequence correlation, and independently identify the presence of each jamming component. The sequence length 20 is selected to ensure a trade-off between temporal information and computational cost. The effectiveness of the proposed model is validated using a simulated compound jamming sequence dataset for training and real jamming data for testing. Ablation and comparative experiments demonstrate that the proposed model achieves higher accuracy and lower computational complexity.

Deep learning has demonstrated significant potential in remote sensing scene classification; nevertheless, its efficacy is frequently hindered by the requirement for extensive labeled datasets and its restricted generalisation in practical applications. We developed a Self Supervised multimodal representation Learning (MMRL) framework for Local Climate Zone Classification (LCZC) to tackle these problems. Our approach utilises novel encoder architecture that derives representations exclusively from synthetic aperture radar (SAR) and multispectral (MS) data. To enhance the learning process, we implemented multimode loss and consistency loss, enhancing the model ability to prioritize the most meaningful features. The encoder, trained on the unlabelled So2Sat-LCZ42 dataset, acquires robust and transferable representations, further refined for a few-shot LCZ classification task utilising a restricted number of labelled samples. This approach allows the model to efficiently utilize extensive unlabeled data, leading to enhanced performance in subsequent classification tasks. Experimental findings on the So2Sat-LCZ42 benchmark validate that our self supervised learning (SSL) based approach achieved better accuracy than the current state-of-the-art(SOTA) SSL models, illustrating its efficacy for label-efficient LCZC.

Effective noise suppression is crucial for the subsequent interpretation tasks of SAR imagery. Traditional SAR image processing techniques often overlook the coherent nature of noise, leading to a loss of vital detail during filtering. With advancements in deep-learning, significant strides have been made in image processing. However, existing deep-learning methods do not fully leverage the imaging mechanisms of SAR, resulting in a lack of specificity and interpretability in the filtering process. To balance noise reduction with detail preservation and to address the “black box” issue in filtering, we propose an interpretable filtering method that employs a correlation-based upward search for density peaks. Initially, we develop an MeanShift-Markov Random Fields filter (MS-MRF) that integrates MeanShift with Markov Random Fields (MRF) in the joint spatial-spectral domain, ensuring both correlation and detail preservation; the derivation of the MS-MRF filter is rigorously grounded in mathematical theory. Subsequently, we integrate MS-MRF with convolutional operations in deep-learning to create a novel convolutional filter, Interpretable MS-MRF Convolution (IMMC), which enhances the model’s interpretability, noise reduction capabilities, and detail retention. Extensive experiments demonstrate that our method outperforms State of the art(SOTA) SAR denoising techniques, achieving an average SSIM of over 85.00% and an average PSNR exceeding 35.00dB across synthetic datasets with varying noise levels, showing significant improvements in noise suppression, detail preservation, and interpretability.

Synthetic Aperture Radar (SAR) system operating in squint working mode has high flexibility and can be widely used in many fields. This paper mainly focuses on decoupling and imaging processing of squint stripmap SAR data. A modified Chirp Scaling Algorithm (CSA) based on the Linear Range Walk Correction (LRWC) method is proposed for this squint stripmap imaging mode. By introducing the Azimuth Non-Linear Chirp Scaling (ANLCS) filtering, the azimuth variant Doppler Frequency Modulation Rate (DFMR) can then be equalized. Simulation is carried out to validate the effectiveness and efficiency of the proposed algorithm.

The advent of high-temporal technology enables spaceborne synthetic aperture radar (SAR) to capture sequential images at a high-frame-rate. By leveraging the extensive temporal information of these images, it becomes feasible to detect high-speed targets. This paper introduces a novel method for extracting the trajectories of high-speed weak targets using the kernel functions and the Hough transform. The proposed method capitalizes on the regular perturbations observed in high-speed targets within sequential images to extract trajectories effectively. Simulation experiments conducted to assess the performance of this method demonstrate its ability to accurately trace high-speed target movements, even under low signal-to-noise ratio (SNR) conditions.

Deep learning-based SAR image interpretation have gained much attention in recent years, in this paper supervised learning for SAR images is discussed first and then novel self supervised contrastive learning method is presented to reduce dependency on large amount of labeled data. SAR images provide valuable information about earth surface. Unlike optical images, SAR images are formed by backscattered signals influenced by surface roughness and other physical properties. Deep learning models can extract details from SAR data that may be missed by traditional image processing. Furthermore, deep learning models can learn patterens and features from SAR images to enable automated interpretation and analysis. It is difficult and time consuming to get labeled data. Supervised CNN is used to extract meaningful data from the SAR images for classification but it uses labeled data extensively, using self-supervised contrastive learning, positive pairs are constructed for each image; positive pairs are generated using augmentation of the SAR image. The contrastive loss function is designed such that it encourages the network to learn similar representation for positive pairs. A novel features extractor is introduced to get robust features. Contrastive loss and backpropagation are used to train the network without labels, after training, fine tuning is done on small number of labels called few shot. Downstream tasks such as classification is performed after fine tuning. Results are evaluated on the widely used SAR bench mark dataset.

The Global Navigation Satellite System (GNSS), as an illumination source with broad coverage capabilities, is considered for passive radar target detection. Due to the low signal power, long-time integration is required for improving the energy of moving targets. However, the receivers often use independent local oscillators, and the bistatic configuration brings challenges to signal synchronization. This paper proposes a time-frequency synchronization method based on dual-channel processing for GNSS-based PR. Firstly, the direct channel data is used to construct the range delay and Doppler phase compensation factors. Then the time-frequency synchronization error in the reflect echo is eliminated using the compensation factors. Finally, direct wave suppression is performed for the subsequent energy integration and target detection. A ship detection experiment has been conducted using GPS L1 signal. The results show that the synchronization errors caused by satellite motion and receiver local oscillator drift are eliminated and direct wave is effectively suppressed. The container ship is successfully detected in the range Doppler (RD) domain.

Spaceborne bistatic synthetic aperture radar (SAR) system employed different locations for transmitter and receiver could provide additional interference information compared to monostatic SAR. The spaceborne bistatic multichannel SAR system can lift the restrictions between high-resolution and wide-swath. Multichannel SAR system utilize pulse-repetition frequencies less than Doppler bandwidth at the cost of ambiguous Doppler spectrum. This represents that the radar echoes received by each channel are under-sampling. Unambiguously reconstruct the Doppler spectrum is necessary for SAR image focusing. However, Conventional azimuth reconstruction method in the Doppler spectrum for bistatic multichannel SAR result in failure because of the variety of expression for round-trip slant range of the bistatic SAR echoes. To address this problem, an improved approach to azimuth spectrum reconstruction for bistatic multichannel SAR is proposed in this paper. Application of matrix filter method can realize the recovery for signals of all channels. The effectiveness of the proposed approach in this paper is demonstrated with simulation experimental results.

Synthetic aperture radar (SAR) working at the terrain observation with progressive scan (TOPS) mode can obtain wide-coverage images. The improvement of image coverage makes further demands for higher radiation accuracy. However, traditional imaging algorithms do not take into account the effects of errors introduced by beam rotation, resulting in poor radiometric deterioration and distortion of image quality. This letter focuses on the specific manifestations of this phenomenon and the reasons for its formation. To improve the TOPS SAR image quality, a method based on the generalized cross correlation (GCC) of subsignals is presented to estimate the parameters of beam rotation in this letter. Experimental results with real spaceborne TOPS SAR data are provided to validate the analysis of this phenomenon and show that the proposed method improves the radiation accuracy for more than 0.2 dB.

Squint spaceborne synthetic aperture radar (SAR) can significantly improve the ground observation performance of platform sensor through flexible beam pointing; however, it is still challenging for squint spaceborne sliding spotlight SAR data focusing since the significant range cell migration (RCM) and range azimuth coupling (RAC) caused by the squint angles. Whereas the linear range walk correction (LRWC) can deal with these problems, the traditional chirp scaling (CS) algorithm cannot adapt to the signal model after the LRWC operation, especially in the case of sliding spotlight mode. In this letter, a hybrid CS algorithm (HCSA), integrating the modified CS (MCS) in range time domain with the frequency CS (FCS) and frequency nonlinear CS (FNLCS) in azimuth frequency domain, is proposed for squint sliding spotlight SAR system. After LRWC and deramp operations, the hybrid CS (HCS) based on the updated signal model is employed to fulfill whole scene focusing, which can equalize the Doppler frequency modulation rate (DFMR) and handle the back-folding issue in azimuth time domain simultaneously. The geometric correction is then be applied to obtain the focused SAR image without LRWC geometry distortion. Simulation demonstrates that for the spaceborne SAR with 1.0 m resolution in both range and azimuth directions, and a squint angle of 30°, the proposed HCSA can realize effective focusing within the full scene.

The global navigation satellite systems (GNSS), as illuminators of opportunity, form passive bistatic radar (PBR) systems that offer advantages, such as wide coverage, low power consumption, and low cost, making them suitable for moving target detection. However, the low power budget and small Doppler tolerance of GNSS signals pose challenges for energy accumulation and detection for moving targets. Additionally, range migration and Doppler shift caused by target motion during long-time integration must be addressed. This letter proposes a coarse-to-fine Doppler parameter search method for long-time coherent integration of maneuvering targets. In the coarse parameter search stage, the first-order Doppler phase modulation is removed within and between pulses. Pulse compression and range migration correction are accomplished using a unified range compensation filter. In the fine parameter search stage, quadratic Doppler phase modulation is compensated, and the fast Fourier transform (FFT) is used to refine the Doppler search step size efficiently. An experiment using GPS L1 signal as the illuminator is conducted, successfully detecting a ship target. The comparison results indicate that the proposed method significantly improves implementation efficiency without the loss of coherent integration gain.

Traditionally, synthetic aperture radar (SAR)-based ship target detection is performed in the image domain, where SAR imaging processing has to be applied first. However, SAR imaging processing is complex and time-consuming, especially in the wide-swath working mode. Actually, for open sea scenes, most echoes are sea surface signals with no ship targets, and there is no need for imaging processing in those areas. Therefore, non-image domain ship target detection is studied in this letter, and a novel Incept-text convolutional neural network (TextCNN) model is proposed for ship target detection in the SAR range-compressed domain (RCD). In the proposed method, the SAR echo data are converted into a 1-D range profile signal first by range compression and mean pooling, and then, the Incept-TextCNN model is proposed and applied, and information about existence of ship targets in relevant range cells will be its output. Finally, the effectiveness and efficiency of the proposed method is testified by simulation and real spaceborne SAR data, and the results demonstrate that the proposed model can filter out the invalid range-compressed data of the sea surface area, which can significantly reduce the amount of data for subsequent SAR imaging and ship classification.

In this study, we introduce a novel self-supervised learning adapter based on permutated image restoration (PIR) for effectively transferring pretrained weights from natural images to remote sensing object detection tasks. The adapter’s unique methodology encompasses a three-phase process: segmenting and permuting image blocks, estimating permutation matrices for sequence reconstruction, and applying specialized loss functions for accurate block positioning. The use of our approach results in the maintenance of fidelity in both absolute and relative block positions as demonstrated by the evaluation of block similarities. The empirical results indicate significant performance enhancements for diverse datasets spanning optical and SAR data types, including HRSC2016, SODA-A, and RSDD, while effectively avoiding overfitting.

High-resolution and high-squint spaceborne synthetic aperture radar (SAR) system has excellent earth observation performance. However, high-squint spaceborne SAR data is more challenging to process than the general broadside counterpart because of the severe range-azimuth coupling (RAC). Whereas the classic linear range walk correction (LRWC) method can handle this problem, it is performed in azimuth time-domain and seriously constrains azimuth swath width. In this article, a decoupled hybrid correlation Algorithm (DHCA), combining the range-azimuth decoupling (RAD) in 2-D frequency domain with the modified hybrid correlation (MHC), is proposed to handle sliding spotlight spaceborne SAR data of high-squint angle case. The decoupled hybrid correlation (DHC) is the main body of the proposed algorithm, and it starts with RAD filtering in 2-D frequency domain, which is designed to eliminate the majority of the range cell migration (RCM) and RAC caused by high-squint angles. A nonlinear chirp scaling (NLCS) in range direction is subsequently performed to equalize the variant range chirp rate caused by residual RCM and RAC. Coarse range compression can then be realized uniformly by the range-matched filtering. The NLCS operation and range coarse compression ensure that the range-Doppler (RD) signals of all targets within the whole scene occupy very narrow range cell scopes. By making full use of the principle of stationary point (POSP) to signals, the range compression position and range frequency mapping relationship after NLCS can be derived. The MHC can therefore be fulfilled by extracting the RD signals along the residual RCM and constructing the reference correlation function after the range NLCS. Thus, both the efficiency and the accuracy of focusing processing are guaranteed. Simulations are carried out to validate the proposed algorithm.

Rotating ships usually suffer from complicated defocusing in high-resolution spaceborne synthetic aperture radar (SAR) imagery due to the non-uniform motions, which severely restricts the classification and interpretation of ships in SAR applications. In this paper, a novel refocusing method is proposed to improve the imaging quality of rotating ships by postprocessing SAR imagery. We first attempt to utilize the nonlinear chirp scaling (NLCS) principle to correct space-variant phase errors induced by target rotations. Specifically, an azimuth inverse compression approach without zero-padding operation is designed to recover the azimuth frequency modulation (FM) of the target image data. Then, a modified NLCS operation is derived for space-variant phase error correction by introducing a high-order perturbation function. An objective function based on image quality is also constructed to estimate the optimal perturbation parameters. Finally, simulated data and real spaceborne SAR data are processed to demonstrate the good performance of the proposed method in comparison with other state-of-the-art techniques.

Processing high-squinted spaceborne SAR data in the sliding-spotlight mode is a challenging task due to azimuth spectral aliasing and range-azimuth coupling for frequency-domain imaging algorithms, and most critically, the variation of Doppler parameters causes significant reduction in the depth-of- azimuth-focus (DOAF). In this paper, a novel imaging algorithm is proposed for focusing high-squinted spaceborne SAR data in the sliding-spotlight mode. First, linear range walk correction (LRWC) and range frequency-dependent de-rotation are applied to remove the coupling of range frequency with the Doppler parameters. Then, a modified range migration algorithm (RMA) is derived for accurate focusing. The de-ramp operation combined with improved nonlinear chirp scaling (INCS) is employed for solving the aliasing problem of azimuth time and extending the depth-of-azimuth-focus in the third step. Finally, geometry distortion caused by LRWC is corrected. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

The ScanSAR mode can change the antenna angle during operation and obtain a wide swath by scanning multiple strips at one time. However, due to discontinuous working in azimuth, the scalloping effect in ScanSAR will degrade the image quality. In this paper, a novel adaptive scalloping suppression method is proposed by analyzing the complex scene as well as the scalloping distribution. First, the images of the various types of scenes are pre-processed so that the distribution of sub-images satisfies the Kalman filter conditions. Then, the problem of space-variant property is solved by performing adaptive blocking in the range direction. Finally, the Kalman filtering algorithm is introduced to process the scalloping in each sub-block separately, and the processed sub-blocks are fused to obtain the final result. The proposed method is verified by the real ScanSAR images of GF-3. Experimental results show that the proposed method is more efficient for scalloping suppression than the existing ones for both general and complex scenes, and has clear improvement for large-scale images with strong scalloping, which fully verifies the robustness and adaptability of the proposed method.

The capability to work on the high-squint mode brings greatly advantages to the Synthetic Aperture Radar (SAR) system. However, the efficient imaging of the high-squint SAR data has not been well settled. Due to the severe Range-Azimuth Coupling (RAC), the conventional imaging algorithms fail to work properly in the high-squint cases. In this paper, a Decoupled Chirp Scaling Algorithm (DCSA) is proposed for the high-squint SAR data imaging, whose key step is a Range-Azimuth Decoupling (RAD) preprocessing in the 2-D frequency domain. After RAD, the majority of the RAC is eliminated, where the high-squint SAR data is simplified into the quasi-broadside mode. The data can be then processed via the chirp-scaling-typed scheme, which is summarized as follows: The data after RAD is firstly transformed into the Range Doppler (RD) domain, in which a Non-Linear Chirp Scaling (NLCS) is performed in the range direction to eliminate the chirp rate variations caused by the residual RAC. The chirp scaling processing is simultaneously performed to equalize the Range Cell Migrations (RCM) for the full scene data. The range compression, bulk RCM correction, as well as a compensation for the cubic and quartic phases introduced by the NLCS are then accomplished in the 2-D frequency domain. Finally, the azimuth compensation is accomplished in the RD domain to obtain the final focused SAR image. Different from the Linear Range Walk Correction (LRWC) accomplished in the azimuth time domain, the RAD method proposed here does not destroy the azimuth-invariant property of the SAR echoes. Therefore, the DCSA can adapt to the wide swath SAR data imaging. Simulation shows that for the airborne SAR with 1m resolution in both range and azimuth directions, and a squint angle of 45°, the DCSA can achieve the bulk focusing of the full-scene data with a range swath of 10km, whereas the azimuth swath is not limited. Compared with the classical CSA, the DCSA requires only one more range FFT/IFFT and one more complex matrix multiplication, which is therefore timely very efficient.

High-resolution or Medium Earth Orbit (MEO) spaceborne synthetic aperture radar (SAR) system face the problem of long synthetic aperture time. In this case, ship targets with complex angular oscillation motions introduced by sea waves are blurred severely in SAR images. An effective way to refocus targets with rotation movements is inverse synthetic aperture radar (ISAR) technique. However, residual space-variant motion errors after ISAR processing still cause defocusing, especially in heavy sea states. In this paper, a coarse-to-fine refocusing method for moving ship targets is proposed. Firstly, a coarse refocused image is obtained via ISAR technique. Then, a novel space-variant and high-order motion compensation approach is devised to realize fine refocusing. Experiments on real spaceborne SAR data verify the effectiveness of the proposed method.

The Continuously Varying Pulse Repetition Interval (CVPRI) technique is an effective method to acquire Synthetic Aperture Radar (SAR) echo data with significant Range Cell Migration (RCM). The non-uniformity of echo data along the azimuth direction is the primary problem faced in the imaging processing caused by the CVPRI. This paper is focused on the reconstruction of Non-Uniformly Sampled (NUS) signal based on the CVPRI. Then, a modified imaging algorithm combined with the azimuth Non-Uniform Sinc Interpolation (NSI) method is proposed to achieve the NUS echo data focusing. Simulations are conducted to validate the effectiveness of the novel reconstruction method as well as the modified imaging algorithm.

Ultra-high spatial resolution is an important development direction of spaceborne synthetic aperture radar (SAR). The finer the resolution, the more details can be obtained from the image. The range resolution mainly depends on the transmit signal bandwidth. However, due to the limitation of the hardware system, the traditional technologies have difficulties in realizing the wide working bandwidth of the radar. Synthetic bandwidth technology is an effective approach to achieving high range resolution. In actual systems, due to differences in hardware and atmospheric transmission characteristics, there are errors in amplitude, phase, and time delay between the received subband signals. Based on the spaceborne SAR multi-subband system model, this paper analyzes the influence of various errors, and proposes a subband splicing method based on phase gradient autofocus (PGA) and time-domain correlation. The simulation results verify the effectiveness of the algorithm.

Global Navigation Satellite System (GNSS)-based Passive Radar (GPR) is gaining increasing attentions recently due to its potential on moving target detection (MTD). The main problem of the GPR is the low power density of GNSS signals. With the upgrades of GNSS, the total transmit power for the new launched satellites has been greatly improved. However, the total transmit power is allocated to several independent signals, which cannot be directly integrated. This paper proposes a multi-source image fusion method for the GPR which aims at coherently integrating the power allocated to different signals for the same satellite, and improving the detection performance. Validation experiments using GPS signals as illumination source and an airplane as target are conducted. The target is successfully detected by exploiting GPS L1 and L5 signals for the same satellite as illumination sources. Two-image fusion is performed utilizing the proposed method. Significant SNR improvement is achieved, which validates the feasibility of the proposed method.

Operating multi-channel synthetic aperture radar (SAR) in ScanSAR mode enables an ultrawide-swath and high-resolution imagery. A critical step in multi-channel SAR data processing is to accurately estimate and compensate phase inconsistency between channels. However, the Doppler spectrum distribution of ScanSAR relies heavily on the characteristic of illuminated regions, which may make the traditional time-domain phase estimation method failure under nonuniform scenes. In order to ensure that the performance of phase estimation is robust with respect to the variation of scenes, an innovative scene adaptive phase inconsistency estimation method for the ScanSAR system is presented in this letter, which can estimate channel phase error more accurately by extracting scene-center frequency from received data. The performance of the presented approach is evaluated using real airborne multi-channel SAR data. Experimental results show that the modified phase inconsistency estimation method is more effective and adaptable to different scenes compared with the conventional method.

Polarimetric synthetic aperture radar (PolSAR) image semantic segmentation is currently of great importance for synthetic aperture radar (SAR) image interpretation, especially in agricultural applications. Several convolutional neural networks (CNNs) have been implemented for SAR image semantic segmentation in urban or land cover applications. However, existing CNNs often break one semantic area into several pieces or confuse the adjacent semantic area in a PolSAR image. To address these issues uniformly, a refined pyramid scene parsing network (PSPNet) is proposed for PolSAR image semantic segmentation in an agricultural areas. Compared to conventional PSPNet architecture, the refined PSPNet adopts a multilevel feature fusion design in its decoder to effectively exploit the features learned from its different encoder branches. Besides, a polarimetric channel attention module is incorporated into the network to capture rich polarimetric features in a PolSAR image. Furthermore, an edge-aware loss function is devised to guide the network to refine pixel-level edge information directly from semantic segmentation prediction, separating easily confused agriculture area with sharp contours. Experimental results on one airborne millimeter-wave PolSAR dataset verify that the proposed network achieves promising semantic segmentation accuracy and preferable spatial consistency.

The Global Navigation Satellite System (GNSS)-based passive bistatic radar (PBR) which uses the GNSS signal as the illuminators of opportunity is studied for moving target detection (MTD). GNSS-based PBR has many advantages due to the removal of the transmitting device; however, its fundamental limitation is the low power density of the GNSS signal. Therefore, the integration time should be sufficiently long to obtain a promising maximum detectable range. On the other hand, the integration time is limited by the range migration and Doppler migration of the echo caused by target motion. In this letter, a novel MTD algorithm is proposed for the GNSS-based PBR, by employing a modified radon Fourier transform (MRFT) to achieve the required long-time integration for moving targets. The MRFT integrates the echo energy via joint searching of range, Doppler, and Doppler rate of the target, which can handle not only the range migration but also the Doppler migration problems, and significantly improves the signal-to-noise ratio (SNR) of the echo signal. An experiment using the GPS L5 signal as the illumination source is conducted and a moving car is successfully detected by the proposed algorithm, although significant range migration and Doppler migration are present due to variation of its speed.

For azimuth multi-channel synthetic aperture radar (SAR), unavoidable inconsistency errors between channels can degrade SAR image quality severely, leading to possible ghost targets and image defocusing, etc. To address this issue, a novel channel inconsistency estimation method is proposed based on maximum normalized image sharpness. First, channel amplitude and time delay errors are corrected in the coarse compensation step. Then images of each channel are attained by azimuth spectrum recovery and imaging processing. Next, range-variant channel phase errors are estimated via optimizing normalized image sharpness, which reaches the maximum value when the image is focused well or ghost targets are suppressed completely. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is employed to get the optimal solution based on the derived gradient of objective function. Finally, the ultimate image is formed through adding up phase compensated images of each channel. By optimizing the focused image quality, the proposed algorithm achieves high estimation accuracy. Simulated data and real multi-channel SAR data are processed to demonstrate the effectiveness of the proposed method.

The recent local climate zones (LCZ) classification scheme provides spatially fine granular descriptions of inner urban morphology. It is universally applicable to cities worldwide and capable of supporting various urban studies. Although optical and dual-pol synthetic aperture radar (SAR) data continue to push the frontiers of this task, the potential of quad-pol SAR data for LCZ classification is not yet explored. In this article, we propose a novel complex-valued convolutional neural network (CNN),SAR4LCZ-Net, to tackle this challenge. SAR4LCZ-Net improves the state-of-the-art by exploiting two facts of this specific task: the semantic hierarchical structure of the LCZ classification scheme and the complex-valued nature of quad-pol SAR data. To validate the performance of our algorithm, we generate a Chinese Gaofen-3 quad-pol SAR dataset for LCZ which covers 31 cities around the world. Results show that the proposed SAR4LCZ-Net improves 2.4% on overall accuracy (OA) and 4.5% on average accuracy (AA) compared with the real-valued CNN with the same structure. Gaofen-3 quad-pol SAR data also showed its advantage over the dual-pol Sentinel-1 data. It enhanced 5.0% on OA and 7.2% on AA in LCZ classification, under a fair comparison with a model trained by Sentinel-1 of the same area.

Global navigation satellite system (GNSS)-based passive bistatic radar (PBR) has many advantages benefiting from the bistatic configuration and the excellent properties of the GNSS signals. In this article, the possibility of moving target indication (MTI) using a GNSS-based PBR is analyzed and illustrated by experiments. The power budget is first evaluated to investigate the capabilities and limitations of the GNSS-based PBR. Due to the low power density of GNSS signals, long-time integration is required to achieve long-range surveillance. In our previous work, a Radon Fourier transform (RFT)-based long-time integration algorithm has been proposed for GNSS-based PBR. However, the RFT-based methods are computationally inefficient. In this article, a novel MTI algorithm based on high frame rate image sequence (HFRIS) is introduced to the GNSS-based PBR. Benefiting from the avoiding of the iterative multidimensional parameter searching, it has a higher computational efficiency than the RFT-based methods. At the same time, the possibility of multistatic positioning for moving targets using the GNSS-based PBR is analyzed. It is shown that the absolute position of the target in three dimensions could be determined if at least three satellites are utilized to measure the range delay of the target return. To confirm our analysis, experiments are conducted which detects the airplanes using the GPS signals as illuminators of opportunity. The collected data are processed by both the RFT-based method and the HFRIS-based method. Both methods have successfully detected the airplane target at a distance of about 5 km and the HFRIS-based method shows a far better performance in terms of processing efficiency. The multistatic positioning experiment is also conducted and the estimated position of the target is well consistent with the values acquired by the flight record, which shows the potential of the GNSS-based PBR on multistatic operations.

The azimuth multichannel technique has been widely used in synthetic aperture radar (SAR) systems for improving the resolution and expanding the illumination area. However, due to phase inconsistency (PI) of different channels, the image quality deteriorates significantly, including resolution loss and appearance of ghost targets. In this letter, by exploiting the rotation-invariant property of the steering vector of the multichannel SAR signal, a PI error compensation method is proposed based on the estimation of signal parameters by rotation invariance technique (ESPRIT). Experimental results are presented using both simulated and real data to demonstrate the performance of the proposed method.

Scanning synthetic aperture radar (ScanSAR) mode is widely used in Earth observation because of its capability of acquiring wide-swath images with moderate resolution. However, due to the operation mechanism of ScanSAR mode, the acquired images often suffer from the scalloping problem, resulting in significant deterioration of image quality. In this article, a novel scalloping suppression method is proposed for ScanSAR images based on the modified Kalman filter with preprocessing. First, an image model is built to analyze the effect caused by scalloping. Then, a modified Kalman filter is proposed to estimate the intensity of scalloping. However, if the scene is complicated or the scalloping effect is strong, the Kalman filter works with poor performance. Therefore, an innovative preprocessing operation is introduced, involving image segmentation and pixel value filling. Finally, the proposed method is verified by the GaoFen-3 (GF-3) and TerraSAR-X satellite images with different scenes. The results demonstrate that the proposed method can accommodate the complex scene well and achieve effective scalloping suppression.

With the development and extensive utility of synthetic aperture radar(SAR), SAR systems are required not only to achieve the large-scale imaging but also to continuously stare at focus areas. Accordingly, SAR based on highly elliptical orbit provides a new way for solving these observation problems. This paper presents a method for spaceborne SAR echo simulation in a highly elliptical orbit based on the antenna beam steering control. Due to the effects of the high eccentricity and the apogee of the highly elliptical orbit, squint angle, looking-down angles of antenna beam and other factors are considered to design the beam control of the antenna, so that the satellite can keep illuminating on the earth surface to acquire echo for the whole cycle. In this paper, the proposed method of antenna beam steering implements the SAR echo simulation in highly elliptical orbit. The feasibility of the proposed method was analyzed by the steering control angles' variation characteristics and verified by simulating the lattice targets echo signal and imaging.

High-resolution spaceborne P-band SAR has broad prospects in the future due to its penetrating capabilities. However, conventional imaging methods are no longer suitable for high-resolution P-band SAR because of ionospheric effects including dispersion and scintillation. In this paper, an imaging compensation scheme with modified Chirp Scaling (CS) has been proposed, correcting ionospheric effects by a new spectrum segmentation method and Phase Gradient Autofocus (PGA). The effectiveness of the proposed compensation scheme for high-resolution P-band SAR has been verified by point target and scene simulation with ALOS-PALSAR data.

Ship detection is one of the important ocean applications of radar images. However, researches for ship detection in low-resolution images are relatively scarce. To improve the ship detection performance in low-resolution conditions, the paper adopts the range-Doppler (RD) images and takes advantage of the multi-frame information with the long short-term memory (LSTM) network. In this paper, the interpolation method and the LSTM method are proposed, which have the advantages of speed and precision respectively and show strong anti-interference ability.

With the video synthetic aperture radar (ViSAR) system proposed, it becomes possible to generate synthetic aperture radar (SAR) images with high frame rate. Benefiting from high frame rate, pixel intensity in a SAR image sequence changes approximately continuously, providing a new way to detect targets in time domain. This paper presents a weak moving point target detection method based on high frame rate SAR image sequences and machine learning. In our method, statistical features are extracted from time domain for distinguishing between background and target. At the same time, the detection problem is transformed into a binary classification problem of determining whether the target exists based on machine learning and these statistical features. The experiments are evaluated using simulated SAR data and the results show that the proposed method can be used to detect moving point targets at a low signal to noise ratio (SNR).

Global Navigation Satellite System Reflectometry (GNSS-R) is becoming a research hotspot in the remote sensing areas recently. This paper is focused on a new application of GNSS-R: Moving Target Indication (MTI). A modified GNSS-R based MTI algorithm is proposed. By the adding of a Doppler Frequency Modulation Rate (DFMR) estimation step, the modified algorithm could significantly improve the Signal to Noise Radio (SNR) of the target. An experiment is conducted for validation and the proposed algorithm performs well.

This article investigates passive synthetic aperture radar (SAR) image formation using Terrestrial Digital Video Broadcast (DVB-T) transmitters of opportunity and an airborne receiver. The proposed airborne system does not use separate channels for direct and reflected signal reception. The proposed image formation algorithm suppresses artifacts due to the presence of the direct signal in radar echo data using modified CLEAN method and accounts for unknown irregularities in airborne platform motion using a map-drift autofocus (MDA) technique. The algorithms are theoretically derived and experimentally confirmed via an appropriate airborne campaign, and their experimental performance is measured.

Azimuth multi-channel technology has been widely used in high-resolution wide-swath synthetic aperture radar (SAR). However, for airborne multi-channel SAR, motion error decreases image quality seriously, including resolution loss and false target appearance. In this paper, a multi-channel SAR imaging method is proposed combined with improved two-step motion compensation. First, multi-channel SAR motion error geometric model is established and the range-invariant channel-dependent phase error is derived. After that the range-dependent phase error for different channels is analyzed in detail. Then, the multi-channel imaging method with motion compensation is addressed. Finally, airborne SAR data is used to validate the proposed method. Processing results show that the proposed method can correct motion error and improve image quality effectively.

ScanSAR images are widely used in both military and civil fields with the capability of wide swath. However, the scalloping effect seriously affects the quality of scanSAR images, especially in the complex scenes, e.g. the sea-land junction scene. This paper presents a modified Kalman-filter method for scalloping suppression. First, the scanSAR image model is built, considering the scalloping effect and noise. Then, Kalman filter is adopted for suppressing the scalloping effect. Moreover, pre-processing method, on the basis of image statistical characteristics, is implemented to accommodate complex scene. Specifically, the pre-processing, involving image segmentation and brightness filling, divides the image into sub-images with different brightness and provides linear-Gaussian condition for Kalman filter through brightness filling. Finally, the method is verified by GaoFen-3(GF-3) SAR images, with the discussion and conclusion.

High-resolution low-frequency spaceborne synthetic aperture radar is a significant development of SAR technology. Integration time of synthetic aperture and the space varying of echo signal become the key factors that seriously affect the quality of SAR imaging. Traditional signal models and imaging algorithms cannot meet the accurate requirements of low-frequency SAR exceptional imaging. Based on the modified equivalent squint range model and hybrid digital correlation algorithm, a new two-step low-frequency spaceborne SAR imaging algorithm is proposed. The relativity of the two-dimensional (azimuth and range) point target reference spectrum is removed by the improved algorithm, and the precise imaging result is achieved through the first and the residual range cell migration correction steps. A simulation experiment has been conducted to verify the effectiveness of the algorithm.

Based on the multiple azimuth squint angles mode, a novel moving target velocity estimation method is proposed, including both along range and along azimuth directions. The acquisition geometry of multi-azimuth angle mode is given first with variation of antenna azimuth squint angles. Then, range curvature is corrected in range-frequency domain after range compression and the residual range curvature can be neglected which is smaller than a range bin. Furthermore, azimuth velocity is estimated by the slope variation of the range walk trajectory from two certain observations and then is the range velocity estimation based on the slope from one observation. Velocity estimation accuracy is also analyzed, considering the errors caused by azimuth squint angle and trajectory slope. The effectiveness of the proposed strategy is demonstrated by experimental results.

This paper presents an efficient approach to obtain land masking using synthetic aperture radar (SAR) images, which is based on level set method and fully convolutional network (FCN) classification. First, the level set method is applied to the cropped SAR input image for initial contours. Second, FCN model has been trained to classify the input image by two labels of water and land which will find the possible region existing real coastlines, named region of interest (RIO). Third, to select the desired contour from extracted boundaries in the first step, according to the proportions to be covered in RIO of step two. Then, final land masking can be obtained after morphological processing and color filling. The method proposed in this paper is fast and accurate enough for ship detection in high-resolution SAR images. It is also robust to speckle noise and geographical changes. Experimental results on GF-3 SAR images show good performance of this method.

Real-time imaging products using Spaceborne Trinodal Pendulum synthetic aperture radar formation can provide valuable information in certain applications. The onboard orbit determination data of the spaceborne SAR platform is essential for the SAR imaging procedure. For real-time SAR imaging, the onboard orbit determination data is relatively low in accuracy compared with the orbit data obtained by off-line processing for the focusing of SAR data. The influence of errors in onboard real-time orbit determination data on SAR image quality should be considered. This paper proposes a Monte Carlo simulation model for inspecting the influence of onboard orbit determination data on imaging quality. This simulation model and its result may be helpful for the development of SAR real-time imaging focusing on providing terrain change information in a short time.

The spatial localization of interference source is a critical procedure in Radio frequency interference (RFI) suppression. It is not suitable to use traditional multi-station interference localization methods in most synthetic aperture radar (SAR) systems. This paper proposes a novel method of single RFI localization based on conjugate cross-correlation of dual-channel SAR signals. First, interference detection technology is applied to check whether echo signal is interfered. Then, conjugate cross-correlation is performed on SAR signals, and the distance difference between interference source and receiving channels is calculated. Finally, spatial localization equations of interference source are established, and optimization algorithm is applied to solve the coordinates of interference source. The results of simulation experiments illustrate the effectiveness of the proposed method.

Global navigation satellite system (GNSS)-based bistatic radar has become an attractive technology in remote sensing applications, and coherent integration is a vital operation for improving its detection ability due to the very weak signal. For small to medium UAV detection, one problem is the coherent integration loss due to issues such as the Doppler-intolerant characteristic of GNSS signal and the large range cell migration over long coherent integration time. In this paper, a modified UAV detection algorithm is proposed. The quadratic phase term caused by transmitter motion is first compensated, then the UAV target echo signal is focused in the Range-Doppler domain by an improved Radon Fourier Transform (RFT) with range-walk removal and Chirp-Z transform. Finally, a refined range matched filter with a shifting Doppler is applied for range compression, and the UAV target is accurately focused in the range-Doppler plane. Numerical simulations demonstrate that the range and Doppler parameters of UAVs can be effectively obtained.

Synthetic Aperture Radar (SAR) is widely used in remote sensing field, especially for the acquisition of quantitative information about the earth’s environment. Calibration processing is critical for SAR image quantitative applications. This paper proposes a spaceborne SAR calibration simulator to describe the calibration process. First, Amazon rainforest area SAR image from Gaofen-3 was chosen to generate backscatter coefficient by adding a reference reflector for the purpose of quantitative analysis. Then the backscatter coefficient was entered into the spaceborne SAR calibration simulator. The simulator will perform echo simulation based on the system parameters of Gaofen-3. Based on the simulation echo, image formation processing is implemented for generating the focusing image. Furthermore, peak method and integral method were utilized to calibrate the SAR image. Simulation results show that integral method is better for calibration accuracy.

Frequency Modulated Continuous Wave (FMCW) is an alternative to pulsed mode operation of Synthetic Aperture Radar (SAR). It has advantages of less power requirements, low mass, low cost, and therefore simpler system; but the disadvantage is requirement of separate antennas for transmission and reception. Interrupted FMCW can use a single antenna by interleaving transmission and reception, but the disadvantage is discontinuity of data in azimuth which, after processing leads to the appearance of unwanted spikes paired with the compressed pulse. This paper presents a solution for the one-dimensional azimuth processing of interrupted data. An all-pole model is fitted on each individual segment of deramped azimuth data, and spectrum is estimated which reveals the target locations corresponding to that segment. Finally, the spectrum of all the segments is added to obtain a complete target profile in azimuth. Three different methods have been used for calculating model coefficients, and the results have been compared. It is shown that covariance method and Burg's method give sharp peaks for target locations.

In recent years, electromagnetic vortex waves carrying orbital angular momentum (OAM) arouse extensive attention in remote sensing imaging fields. However, most of the researches concentrate on the staring imaging mode, where the radar is motionless relative to the targets. We establish a novel system operation mode based on the principle of synthetic aperture radar (SAR). The echo model upon this novel operation mode is established and the new imaging method modified from the back-projection (BP) algorithm is deduced. Simulation and image evaluation results demonstrate the validation of the proposed method and OAM-based SAR system can be utilized in the SAR data acquisition and signal processing fields.

With increasing development of Synthetic Aperture Radar (SAR) jamming technology, the jamming effects analysis for some innovative SAR, such as Multi-channel and Multi Input and Multi Output (MIMO) SAR, is significant. The geometry model of Multi-Channel and MIMO SAR are introduced. Meanwhile, the jamming blanket factor is researched and the barrage jamming is simulated. Simulations demonstrated jamming effects for both Multi-Channel and MIMO SAR.

Orbital stations can be an alternative platform choice for SAR payloads besides professional remote sensing satellite platform. However, such platform cannot provide as precision motion parameters as remote sensing satellite platform for SAR imaging due to its unique structure. The influence of motion parameter errors on SAR azimuth resolution degradation should be taken into consideration. Selecting the second order phase error caused by Doppler frequency rate with error as intermediate variable between motion parameter errors and SAR azimuth resolution, this paper proposed a Monte Carlo simulation model has been set up to obtain the specific probability distribution of SAR azimuth resolution degradation. A typical Monte Carlo simulation result is given to show the effectiveness of the method and may help actual SAR payload system design.

Quantization step size is a very important system parameter in TOPS and sliding spotlight acquisition modes, as the azimuth antenna beam is electronically steering during the illumination. By replacing continuous steering, staircase-like steering in azimuth antenna beam is often used in orbited SAR, causing a quantized error on azimuth antenna pattern (AAP) and an amplitude modulation on illuminated target echo. Consequently, infinitely many spurious targets are introduced in azimuth direction. In this paper, investigations of quantized AAP on TOPS and sliding spotlight SAR image are carried out, by unified modelling on quantized AAP and spurious targets signal. Then, the position of maximum peak of spurious targets and its peak level are derived, which is only decided by azimuth antenna length, signal wavelength and quantization step size, and have nothing to do with the acquisition mode. Finally, simulation results verify the correctness of the proposed mathematical model of spurious target.

Global Navigation Satellite System (GNSS)-based bistatic Synthetic Aperture Radar (SAR) recently plays a more and more significant role in remote sensing applications for its low-cost, flexibility and real-time global coverage capability. In this paper, a novel high-order imaging algorithm is presented for GNSS-based bistatic SAR. Firstly, the modified equivalent squint range model (MESRM) is introduced to describe range history of the GNSS-based bistatic SAR, and the accuracy of the MESRM is analysed. Secondly, a high-order image formation algorithm based on the MESRM is derived, where the range cell migration (RCM) of the echo signal is corrected by two-step range cell migration correction (TSRCMC), and the azimuth-variance of the echo signal is solved by azimuth block hybrid correlation. Finally, simulation and experiment are performed to validate the presented algorithm.

An autofocus operation is an indispensable procedure to obtain well-focused images for synthetic-aperture radar (SAR) systems without precise navigation devices. Three challenges have been faced in the very high-resolution (VHR) airborne SAR autofocus due to the long cumulative time: the varying along-track velocity, the residual range cell migration (RCM), and the range-dependent phase errors with higher order components. When it comes to the stripmap mode, the autofocus becomes more complicated, since the scenario with a few strong scatterers is more likely to be encountered with a moving beam. Combining the merits of parametric and nonparametric autofocus algorithms, a robust motion error estimation method is proposed in this paper. First, we perform a stripmap multiaperture mapdrift autofocus operation to extract the along-track velocity and the most range-invariant errors, removing the residual RCM at a subaperture scale. Second, one referential center block is selected to retrieve the residual range-invariant error, which can eliminate the residual RCM globally in the range dimension. With a global high-quality input, the residual range-variant phase errors can be retrieved precisely utilizing a center-to-edge local maximum-likelihood weighted phase gradient autofocus kernel at last. Experiments on real VHR airborne stripmap SAR data are performed to demonstrate the robustness of the proposed method.

Recently, electromagnetic (EM) vortex with orbital angular momentum (OAM) attracts more attention in radar imaging fields, which needs a variety of OAM modes. However, most of them are based on motion relative static. In this paper, we propose a novel SAR imaging method based on electromagnetic vortex with OAM. The geometry model of synthetic aperture radar (SAR) is utilized for EM vortex with OAM and the echo signal model is deduced correspondingly, including the additional phase items produced by EM vortex waves. Then the imaging algorithm aiming at OAM based on Chirp-Scaling algorithm is proposed. Simulation results validate the effectiveness of the proposed method and that OAM beams can be applied in SAR signal processing.

The vertical electron density distribution can have a significant impact on ionospheric measurements of Total Electron Content (TEC) derived from estimates of Faraday rotation (FR) with spaceborne low-frequency SAR data. Using vertical electron density data and the IGRF-12 magnetic field model, simulations of FR under different vertical electron density distributions for an L-band SAR have been performed. The usual approximation that the geometric and magnetic field parameters can be estimated at 400 km when converting FR to TEC is shown to cause significant errors, especially for regions at low latitude and near the zero FR line.

The multi-azimuth-angle (MAA) UAV SAR is introduced in this paper. Compared with the traditional TomoSAR that rebuilds the 3-D scene only from the boresight aspect, the MAA UAV SAR can rebuild the 3-D scene from different multi azimuth angles and then provide multiple or even full aspects 3-D image by the novel 3-D image formation algorithm presented in this paper. Firstly, the MAA UAV SAR is introduced, followed by the 2-D imaging algorithm to focus the raw data. Then, a novel squinted tomography method is provided to obtain squinted 3-D image using multiple 2-D images with the same azimuth angle. Finally, the squinted 3-D images are geolocated and fused to construct a full 3-D image. Imaging results are provided to demonstrate the better performance of the MAA SAR.

Time-sequential SAR image is product of multi-angle observation mode, which is widely used in moving targets detection and multi-directional information extraction. Utilizing the long integration time in spotlight/sliding spotlight SAR, it can generate a series of images of one scene during different periods of time. Focusing on the problem of high data redundancy and low efficiency in the process, this paper proposes an efficient image formation algorithm based on subaperture combination. The whole imaging process is segmented into three steps, which are subaperture partition, imaging and combination. Modified implementation model and corresponding imaging algorithm are presented. Theoretical analysis of calculated amount and simulations of point target show the effectiveness of the method proposed in this paper.

SAR image segmentation is the pre-process for SAR image application. This paper presents a new SAR image segmentation algorithm combining both the the bias field and Markov random field(MRF) characteristic with fuzzy clustering model(FCM) called BMFCM. The MRF characteristic of an image includes the spatial information of the image and the bias filed estimation is introduced to deal with the grey intensity inhomogeneity in SAR image. Considering the specific features of SAR images, the experiment results on real SAR images demonstrate the validation of the proposed algorithm.

Comparing with the traditional global navigation satellite system (GNSS) based Bistatic SAR (GNSS-based BiSAR), the BiSAR based on GPS-L5 signal plays a superior role in remote sensing applications, as for its feature of stronger power and larger bandwidth. In applying this method, the consequent problems, yet, such as long dwell time and large range cell migration (RCM) are inevitably leading the traditional image processing algorithm non-applicable. Therefore, a refined imaging formation algorithm for this BiSAR data imagery was proposed. Firstly, a modified bulk RCM correction (RCMC) was presented based on a fifth-order two-dimensional point target spectrum. After bulk RCMC processing, the phase resulted from RCM and range-azimuth coupling at the reference range was removed. Then, as the bulk RCMC dramatically changed the echo's range history, a refined hybrid correlation operation emerged to compensate the residual phase errors. Finally, the simulation and imaging results measures the validity and accuracy of the proposed method.

In this letter, an accurate mathematical model for azimuth ambiguity in stripmap synthetic aperture radar (SAR) images is first constructed, with an azimuth ambiguity factor (AAF) defined as the residual amplitude and phase terms of ambiguities. Next, a novel framework for reconstructing and suppressing azimuth ambiguity is proposed based on the analysis of the AAF. In this framework, azimuth ambiguities are accurately reconstructed by applying reconstruction filters in the range Doppler and 2-D frequency domain, and then, the reconstructed signal is used for suppressing azimuth ambiguities. Moreover, the proposed framework does not depend on the statistical characteristics of a SAR image and is capable of reducing the space-variant ambiguities. As verified by both simulated data and real TerraSAR-X data, the proposed method is capable of suppressing azimuth ambiguities in SAR images.

Multisource remote sensing data have been widely used to improve land-cover classifications. The combination of synthetic aperture radar (SAR) and optical imagery can detect different land-cover types, and the use of genetic algorithms (GAs) and support vector machines (SVMs) can lead to improved classifications. Moreover, SVM kernel parameters and feature selection affect the classification accuracy. Thus, a GA was implemented for feature selection and parameter optimization. In this letter, a GA-SVM algorithm was proposed as a method of classifying multifrequency RADARSAT-2 (RS2) SAR images and Thaichote (THEOS) multispectral images. The results of the GA-SVM algorithm were compared with those of the grid search algorithm, a traditional method of parameter searching. The results showed that the GA-SVM algorithm outperformed the grid search approach and provided higher classification accuracy using fewer input features. The images obtained by fusing RS2 data and THEOS data provided high classification accuracy at over 95%. The results showed improved classification accuracy and demonstrated the advantages of using the GA-SVM algorithm, which provided the best accuracy using fewer features.

There are two challenges for efficient processing of both the sliding spotlight and terrain observation by progressive scans (TOPS) data using full-aperture algorithms. First, to overcome the Doppler spectrum aliasing, zero-padding is required for azimuth up sampling, increasing the computation burden; second, the azimuth deramp operation for avoiding synthetic aperture radar (SAR) image folding leads to azimuth time shift along the range dimension, and in turn the appearance of ghost targets and azimuth resolution reduction at the scene edge, especially in the wide-swath case. In this paper, a novel three-step algorithm is proposed for processing the sliding spotlight and TOPS data. In the first step, a modified derotation is derived in detail based on the chirp z-transform (CZT), avoiding zero-padding; then, the chirp scaling algorithm kernel is adopted for precise focusing in the second step; and in the third step, instead of the traditional range-independent deramp, a range-dependent deramp is applied to compensate for the time shift. Moreover, the SAR image geometry distortion caused by range-dependent deramp is corrected by employing a range-dependent CZT. Experimental results based on both simulated data and real data are provided to validate the proposed algorithm.

Aimed at the problems that SAR (Synthetic Aperture Radar) environment parameter inversion and data processing are lack of both overall design and synergy, this paper proposed a novel unification scheme for environmental remote sensing application. Compared to the reality that application follows data processing in the frontend, the performance of application is highlighted as the ultimate objective in the proposed scheme, where the frontend procedures should serve the backend application to get a better result. Frontend processing is divided into three parts: system design, imaging processing and post-processing procedure, while backend processing is categorized as image processing and environment parameter inversion. The concept of unification scheme focuses on the feedbacks between frontend and backend, and some demonstrations are also given in the following sections.

Ionospheric effect is considered to be one of the factors limiting the accuracy of deformation measurement acquired from interferometric SAR data. The correction of this error source was studied recently, and some new methods was developed and improved. One of these methods is the range Split-Spectrum. In this work, we introduce a new algorithm for implementing the method. An improvement is made, when one corregistration step is performed, and no resampling of the slave images is needed. The performance of the method is studied on a couple of ALOS data, the variation of the differential STEC on the interferogram, was determined as less than 0.5 TECU.

This study proposed a novel method to analyze slick-free and oil covered sea surface backscattering in the special case of Deepwater Horizon (DWH) oil spill accident based on combination of tilted Bragg scattering and volume scattering components. The DWH accident represents a particular and challenging case due to the very large amount of leaked oil that came from the bottom of the ocean. The proposed scattering model consists of first estimate the large scale tilting angle of sea surface Bragg scattering mechanism and then of the retrieval, through its linear relationship with the relative dielectric constant, of oil-insea volume concentration. Finally, Bragg and volume scattering components can be estimated which provide useful information for a better understanding of i) the sea surface status and ii) the weak-damping properties the leaked oil due to purification/emulsification phenomena. The model is tested considering actual UAVSAR L-band fully-polarimetric SAR data.

Although Synthetic Aperture Radar (SAR) can capture rich land cover information as a most important advanced technique in the field of international earth observation, the application effects still limited significantly. One of the reasons is that the study of SAR imaging processing, SAR image processing and SAR applications are usually conducted respectively, and the study on integrating the three processes is lacked. Focusing on the science problem, taking the typical natural distribution targets (surface deformation, sea ice classification) and man-made targets (building complex and collapsed buildings) as examples, some application studies oriented to SAR environmental parameters inversion are conducted, and the information integrated frames and methods are proposed.

State of the art UAV technology shows its advantage in both civil and military applications, and it is required to meet the needs for payloads special designed for UAVs. However, electric powered multi-rotor UAV provides limited power and more sensitive to the payloads mass. Linear frequency modulated quasi continuous wave SAR leads to small and efficiency radar system which meets multi-rotor UAV's requirements. This paper provides a C-Band UAV SAR combining with linear modulated quasi continuous wave technique as well as integrated radar and communication design. Experiments will be taken to verify the usage of UAV SAR in typical SAR working mode and GMTI ability.

To process squinted sliding-spotlight synthetic aperture radar data, the azimuth preprocessing step based on the linear range walk correction (LRWC) and derotation operations is implemented to eliminate the effect of 2-D spectrum skew and azimuth spectral aliasing. However, two key issues arise from the azimuth preprocessing. First, the traditional chirp scaling (CS) kernel is not suitable for data focusing because the property of the 2-D spectrum is changed significantly; second, the spatial variation of the targets' Doppler rates along the azimuth direction due to the LRWC operation limits the depth-of-azimuth-focus (DOAF) seriously. In this letter, a modified accurate CS kernel is derived to realize range compensation. Then, an azimuth spatial variation removing method based on the principle of nonlinear CS is proposed to equalize the Doppler rates of the targets located at the same range cell, which can extend the DOAF and improve processing efficiency. Finally, a novel imaging algorithm is proposed, with its effectiveness demonstrated by simulation results.

The spaceborne P-band synthetic aperture radar (SAR) is affected by ionospheric scintillation, which seriously impacts the imaging quality and measurements of biomass. Considering polarization modes and topographic features, a hybrid scheme for compensating ionospheric scintillation effect is proposed based on Phase Gradient Autofocus (PGA) and Faraday rotation (FR) estimation based method. Simulating with ALOS-PALSAR data, we quantitatively analyze the effectiveness of compensation and verify the scheme is capable of compensating scintillation effect considering different polarization modes and topographic features.

Spaceborne squinted sliding spotlight synthetic aperture radar (SAR) can achieve high-resolution image with well flexibility. However, traditional imaging algorithm cannot focus its data, as the serious coupling between the range and the azimuth and the azimuth spectral aliasing problem. To process the data, a refined two-step algorithm is proposed in this paper. Firstly, the linear range cell migration correction (LRCMC) and De-rotation are adopted to solve the azimuth spectral aliasing problem. Then the data is focused with a modified wavenumber domain algorithm (WDA). Also, the azimuth nonlinear chirp scaling (NCS) method is used to solve the limited depth-of-azimuth-focus (DOAF) problem. At last, the geometric correction is adopted to correct the geometrical distortion. The computer simulation results verify the validity of the proposed imaging algorithm.

Motion compensation (MOCO) is the key step in airborne Synthetic Aperture Radar (SAR) imaging processing. Motion errors can be obtained from the navigation data which is acquired from the inertial navigation system (INS) mounted on the plane. However, the accuracy of the navigation data is limited which is far from application requirement for high resolution system. Accordingly, raw-data based MOCO approach is necessary in airborne SAR azimuth focusing which is defined as autofocus method. The autofocus method includes no-parametric techniques such as phase gradient autofocus (PGA) method and parametric techniques including contrast optimization algorithm (COA), map drift algorithm (MDA), etc. Based on the conventional two-step MOCO method, this paper proposes an improved two-step MOCO technique combining the parametric and the no-parametric techniques to alleviate the dependence on the navigation measurement. Firstly, an improved sub-aperture COA is implemented to acquire the Doppler rate accurately which can be utilized in the estimation of motion errors. In the next step, PGA is applied to eliminate the residual phase errors. Imaging results on real strip-mode airborne SAR data validate the proposed MOCO approach.

Remote sensing data have been commonly used for agricultural crop monitoring. This paper was assessed the quality of using SAR and optical data fusion for maize classification. Two different SAR data sets from different sensors including dual polarization (HH and VV) X-band COSMO-SkyMed (CSK) and quad polarization (HH, HV, VH and VV) C-band RADARSAT-2 images were fused with THAICHOTE (namely, THEOS, an Earth observation mission of Thailand) optical data. This paper describes a comparative study of multi-sensor image fusion techniques in order to maintain spectral quality of the fused images. Principal Component Analysis (PCA), Intensity-Hue-Saturation (IHS), Brovey Transform (BT) and High-pass filter (HPF) techniques are implemented for image fusion. For the supervised classification, maximum likelihood was applied to the fused images to identify maize crop. Finally, the accuracy assessment was done by comparing maize maps generated from fused images and THAICHOTE classification. The PCA fused RADARSAT-2 with THAICHOTE images consistently provide excellent classification accuracies, well over 85%. The results obtained not only improving of the classification accuracy, but also can be identified the growing cycle of maize crop.

Spaceborne Synthetic Aperture Radar (SAR) has attracted special interests in many applications, but suffers from image quality degradation caused by azimuth ambiguities. To suppress this phenomenon, different from conventional suppression method, this paper concentrates on the analysis of precise mathematic expression of azimuth ambiguities. Exploiting the specific characteristic of ambiguities, a restoration algorithm to suppress azimuth ambiguities of stripmap SAR is proposed. This method is not sensitive to SNR, and also suitable for distributed targets. Azimuth ambiguities are accurately restored and then cancelled in SAR image. Proposed method has been shown effective by experiments, demonstrating capabilities of removing artifacts and revealing covered targets in stripmap SAR image.

Block adaptive quantization (BAQ) is widely utilized for reducing downlink data rate of spaceborne synthetic aperture radar (SAR). The mutual information upper bond of BAQ compressed data is proposed by means of establishing a mathematical mapping relationship between output signal entropy and input signal saturation degree, using information theoretical analysis method. Threshold saturation degree of input signal for BAQ to be effective are also obtained. Simulation SAR raw data are used to verify the correctness of the mapping relationship.

GR-strip (Geography-Referenced stripmap) imaging mode is a new imaging mode of SAR (Synthetic Aperture Radar), it is very different from the normal stripmap imaging mode. Operating in this mode, trajectory of aircraft can be un-parallel to swath, thus a higher flexibility is obtained due to the reduces of limitation for radar trajectory. Meanwhile, the characteristic of unlimited swath in azimuth orientation is also reserved in this mode. However, the shortest slant ranges are variant in GR-strip SAR for different points along azimuth direction on the strip and the resolution in azimuth is space-variance correspondingly. This cannot be solved by conventional SAR imaging algorithms. This article focused on two parts, the first part is the introduction of GR-strip SAR, the second part is a modified back-projection algorithm which is applicative to GR-strip SAR.

The P-band synthetic aperture radar of the European Space Agency BIOMASS mission will be affected by ionospheric phase scintillation at high latitudes, which introduces a random high-order azimuth phase error. The dependence of the performance of the phase gradient autofocus (PGA) algorithm for scintillation compensation on the strength of ionospheric turbulence and the signal-to-clutter ratio (SCR) is analyzed. In order to keep resolution degradation below 2%, the SCR must exceed 16 and 20 dB for turbulence strengths CkL = 1033and 1034, respectively. For large values of CkL, phase scintillation adds significantly to post-PGA degradation in the integrated and peak sidelobe ratios. Simulations based on scenes derived from PALSAR data demonstrate the effectiveness of PGA.

Two challenges have been faced in signal processing of ultrahigh-resolution spaceborne synthetic aperture radar (SAR). The first challenge is constructing a precise range model, and the second one is to develop an efficient imaging algorithm since traditional algorithms fail to process ultrahigh-resolution spaceborne SAR data effectively. In this paper, a novel high-order imaging algorithm for high-resolution spaceborne SAR is presented. First, a modified equivalent squint range model (MESRM) is developed by introducing equivalent radar acceleration into the equivalent squint range model, and it is more suitable for high-resolution spaceborne SAR. The signal model based on the MESRM is also presented. Second, a novel high-order imaging algorithm is derived. The insufficient pulse-repetition frequency problem is solved by an improved subaperture method, and accurate focusing is achieved through an extended hybrid correlation algorithm. Simulations are performed to validate the presented algorithm.

The performance of spaceborne synthetic aperture radar (SAR) at lower frequencies, such as VHF/UHF bands, is affected by ionosphere effects, especially Faraday rotation (FR). A quantitative analysis of Faraday rotation impacts on spaceborne VHF/UHF-SAR image formation is presented in this paper. The error models of FR resulting from calm Total Electron Content (TEC) and fluctuant TEC, for Linearly Polarized (LP) and Dual Circularly Polarized (DCP) mode, are established. The simulation results indicate that for LP mode Faraday rotation effect on azimuth signal can be neglected, while the range signal is seriously affected, bur for DCP mode, the error in both range and azimuth signals is negligible. Therefore, DCP mode is a better choice to decrease the effect on image focusing caused by FR effect.

For high resolution spaceborne SAR system, the errors caused by straight trajectory assumption and stop-go approximation cannot be ignored. In this paper, an algorithm is proposed to compensate the above errors. First, a novel modified hyperbolic range equation (MHRE) is introduced to describe the satellite's orbit which is more precise than the traditional hyperbolic range equation (THRE). Then, the accurate signal model without stop-go approximation is deduced based on the MHRE. What's more, the residual phase errors in two-dimensional frequency domain caused by the two error sources are obtained. At last, the accuracy and validity of the proposed compensation algorithm are verified by the computer simulation results.

Polarimetric features retrieved from CTLR (circularly transmit and linearly receive) Synthetic Aperture Radar (SAR) data was analysed in details. A new parameter, namely, Damping Status Sensitivity Index (DSSI) was proposed for quantitatively evaluating the PolSAR characteristics' capability of discriminating different damping patterns of oil slicks and clean seawater. The L-band Uninhabited Aerial Vehicle SAR (UAVSAR) data was utilized in the experiments. It was shown that polarimetric characteristics retrieved from CTLR compact polarimetric SAR data were nearly as same as those derived from fully polarimetric SAR data and can be applied for discriminating different damping status of oil spill and look-likes.

High resolution imaging is an important trend for SAR. The wide bandwidth signal is a must for high resolution. The error of traditional model based on the narrow bandwidth system may be inevitable for high resolution imaging. In this paper, a general SAR echo model is derived for the wide bandwidth system, and we also get the error factor between the traditional and general model. By simulating the two models, the conclusion is derived that if the transmitted signal is LFM waveform, the target is single and the receiving antenna is unit weight, the error of the two models can be neglected. The general echo model in more complicated situations is in the ongoing research. This study will make for the further research of high resolution SAR imaging algorithm.

Airborne synthetic aperture radar (SAR) systems are very sensible to deviations of the aircraft to the reference flight path. The trajectory errors can be divided into along-azimuth errors and the line-of-sight (LOS) displacement, while the latter one is the main source of motion errors. The LOS displacement can be obtained from the navigation data in which an ideal flight path should be assumed first. However, the accuracy of the assumed nominal trajectory is limited especially in high resolution SAR systems. To improve the precision of the estimation of LOS displacement, we present a novel method to acquire a precise flight path in the imaging intervals. This method can estimate trajectory deviation precisely by making full use of the navigation data.

Notch filter is one of the most popular techniques for Radio Frequency Interference (RFI) suppression in the wideband Synthetic Aperture Radar (SAR). In this work, we have wideband `TerraSAR-X' data affected by a near zero RFI (radio frequency interference), this RFI is modulated with a pulse wave, which creates side lobes in the frequency domain, and creates difficulty for filtering this kind of RFI. In this kind of data, the notch filter will eliminate the frequency, but create a transient response after the discontinuity which is a non zeros signal; in this paper, notch filter technique has been used in forward and backward range direction, so the transient effect will be created in different position of the images. Then we combine these two images using the best spectrum of the azimuth direction.

In order to generate high accuracy data for space-borne Synthetic Aperture Radar (SAR) image interpretation, a novel parallel SAR echo generation approach is proposed in this paper. Target's backscattering coefficient is variable at the condition of wide bandwidth and large aspect angle. Considering targets' space-variant and time-variant backscattering, electromagnetic scattering at arbitrary aspect angle and frequency point is calculated accurately by Finite Difference Time Domain (FDTD). However, FDTD is a time-domain method causing large computation load, thus a parallel processing scheme is designed to reduce simulation time. It was observed that proposed approach provided higher image quality compared to classical method, and parallel scheme was also verified effective in the experiment.

This paper analyzes an innovative geographically referenced (GR) stripmap mode for the airborne synthetic aperture radar (SAR). In the GR stripmap SAR, the antenna beam illuminates orthogonally with the ground strip, which is not parallel to the SAR trajectory. Benefiting from the GR stripmap mode, the effective observation swath can be enlarged comparing to what can be realized by the traditional stripmap SAR. A modified nonlinear chirp scaling (MNLCS) method is suggested for the GR stripmap SAR imaging. It can solve the problem caused by scatterers' non-uniform Doppler history that disables most traditional imaging algorithms for the GR stripmap SAR. The MNLCS algorithm consists of three main steps. First, a bulk range migration compensation procedure eliminates the linear range migration (range walk). Second, an azimuth perturbation filter unifies the Doppler frequency modulation rate for each range cell. Lastly, a modified chirp scaling processing finishes the data focusing. Performance of the MNLCS algorithm was analyzed, followed by a series of computer simulation results, which validated the MNLCS for the GR stripmap SAR imaging.

To high resolution spaceborne SAR, one problem is inevitable that as to targets with elevation downs in one scenario, their imaging quality will decline after processing with imaging algorithm. SAR images are confined to two-dimensional domain and it's hard to reflect targets' height property. Instead, target A with some height is projected to target B with no height while A and B are in the different range door but with the same slant range from the satellite. Doppler parameters of B are used to image A due to the two-dimensional limitation and then azimuth defocusing and range location shift will occur. Target height can be regarded as one error source. In this paper, imaging quality of targets with height error is improved through self-focusing method among three-step focusing algorithm as to high resolution sliding spotlight SAR. Experiment results show that it's effective.

In highly-squint strip-map Synthetic Aperture Radar (SAR) onboard high-speed platform, the large range-walk is eliminated by continuously varying its pulse repetition interval (PRF), which overcomes the limitation of echo data collection. However, the accompanying problems of azimuth non-uniform sampling (ANS) and changing of Doppler history are inevitable. Therefore, an imaging formation algorithm for this SAR data imagery is proposed firstly. In the proposed algorithm, the range cell migration correction is performed to eliminate the changing of Doppler history. And the baseband Lagrange interpolation is implemented to reconstruct the ANS data. Then, the bulk compression function and stolt interpolation relationship are deduced in the focusing processing. Finally, the imaging results justify the validity and accuracy of the proposed algorithm.

Squinted multi-channel SAR is a available mode to achieve the high-resolution and wide-swath, because it is more flexible than broadside SAR. However, the traditional processing algorithm of broadside multi-channel mode is not suitable for squinted multi-channel mode, because the high squint angle will lead the reconstruction to fail. In this paper, a modified reconstruction method adapted to squinted multichannel SAR signal is proposed. The large central Doppler frequency of the squinted Doppler spectrum is taking into account, and the reconstruction method is modified accordingly. Meanwhile, the range walk correction is performed before the reconstruction to remove the mismatch between the reconstruction filters and the squinted signal with large range cell migration. Furthermore, the processing algorithm based on the wave-number algorithm framework with range walk correction is proposed. Finally, computer simulation results are presented to demonstrate the validity of the proposed algorithm.

In space-borne azimuth multi-channel SAR, the antenna phase center spacing should be measured precisely to obtain the reconstruction filters. In this paper, a data-based onboard estimation method of antenna phase center spacing in spaceborne multi-channel SAR system is proposed. Firstly, the principle of data-based onboard estimation is presented, then the estimation method in details is described step-by-step. Finally, simulations are carried out, with two influence factors, SCR and focusing accuracy, to demonstrate the validity of the proposed estimation method.

An improved reconstruction algorithm is proposed for compact polarimetric (CP) synthetic aperture radar (SAR) on oil spill detection. Based on the differences in statistical behavior between open and oil-covered sea surfaces, the proposed algorithm can iteratively reconstruct quad-pol SAR images from CP SAR data. During the experiment, it outperformed two existing compact SAR reconstruction algorithms in terms of both statistical and information theoretical analysis.

A novel framework is proposed for mitigating azimuth ambiguities in spaceborne stripmap synthetic aperture radar (SAR) images. The azimuth ambiguities in SAR images are localized by using a local mean SAR image, SAR system parameters, and a defined metric derived from azimuth antenna pattern. The defined metric helps isolate targets lying at locations of ambiguities. The mechanism for restoration of ambiguity regions is selected on the basis of size of ambiguity regions. A compressive imaging technique is employed to restore isolated ambiguity regions (smaller regions of interconnected pixels), whereas clustered regions (relatively bigger regions of interconnected pixels) are filled by using exemplar-based inpainting. The simulation results on a real TerraSAR-X data set demonstrated that the proposed scheme can effectively remove azimuth ambiguities and enhance SAR image quality.

The polar format algorithm (PFA) is a wavenumber domain imaging method for spotlight synthetic aperture radar (SAR). The classic fixed-parameter PFA employs interpolation technique for data correction. However, such an operation will induce heavy computational load and cause degradation in computation precision. To optimize image formation processing performance, this study presents a novel parameter-adjusting PFA, which can implement SAR image formation at an extremely highly squint angle with obviously improved computation efficiency and imaging precision. In the parameter-adjusting PFA, radar parameters, such as center frequency, chirp rate, pulse duration, sampling rate, and pulse repeat frequency (PRF), vary for each azimuth sampling position. Due to the parameter adjusting strategy, the echoed signal can be acquired directly in keystone format with uniformly distributed azimuth intervals. In this case, range interpolation, which is necessary in the fixed-parameter PFA to convert data from polar format to keystone format, can be eliminated. Chirp z-transform (CZT) can be employed to focus SAR data along the azimuth direction. Compared with truncated sinc-interpolation, CZT was found to perform better in inducing less phase and amplitude errors in data processing. When residual video phase (RVP) compensation was accomplished for dechirped signal, the processing steps of the parameter-adjusting PFA were simplified as azimuth CZTs and range inverse fast Fourier transforms (IFFT). Lastly, computer simulation of multiple point targets validated the presented approach.

Ionosphere sounding is of significant importance for compensating ionospheric effects on satellite communication, navigation and remote sensing systems at lower frequency, i.e. L-band, P-band etc.. A novel spaceborne HF/VHF-radar scheme is proposed for ionosphere sounding. Based on Frequency Division (FD) technique, the mode for large-scale ionospheric structure sounding can generate a two-dimensional (2D) topside electron density image. Based on synthetic aperture technique, the mode for small-scale ionospheric irregularities imaging can generate a 3D irregularities image. The effectiveness of the proposed SAR system was verified by means of computer simulations. The simulation results show that the resolution of large-scale sounding mode is as high as 0.49km, and the resolution of small-scale sounding mode is as high as range 0.9km, azimuth 0.27km and across-track 0.25°.

With the resolution of SAR developing continually, it is found that in the process of image processing, using equivalent squint range model would bring large phase error. So using traditional CS algorithm for processing will encounter difficulties. In order to solve this problem because of unsuitable slant range model, this paper introduces the fourth-order slant range model to replace the original model, and conducts a study on CS algorithm based on the fourth-order slant range model. This paper introduces the theoretical formulation and simulation results of the new algorithm. This algorithm can perform well for both low-resolution space-borne SAR and high-resolution space-borne SAR.

In this paper, a novel sub-band fusion technique for SAR echo using compressed sensing was proposed. As we all known, high resolution and wide swath are the trend of SAR systems, while the range resolution of SAR is limited by the bandwidth of transmitted signal. Our proposed algorithm aimed to take the compressed sensing and sub-band fusion into the SAR systems, realize high resolution and reduce the amounts of SAR conveyed data. Internal calibration was used to extract the phase error from each channel. And Orthogonal Matching Pursuit algorithm was used for range signal recovery. After coherent processing, we can acquire ultra-wide bandwidth echo. And finally, refined range Doppler algorithm was employed for range cell migration correction and azimuth compression. We proved the proposed algorithm used the fusion experiment at the end of the paper.

This paper deals with the problem of azimuth imaging worsening when three-step focusing algorithm fulfills imaging requirements of sliding spotlight SAR with wide azimuth swath. This is caused by spaceborne sliding spotlight SAR spatial variation characteristic: single point target's relatively large range cell migration and azimuth point targets' different Doppler experiences at different azimuth locations. To solve this problem phase error introduced by the equivalent strabismus model is compensated and the reference Doppler rate in the first step of TSFA is modified. A comparison of the imaging results to one azimuth 15km swath scene between refined TSFA and original TSFA is exhibited through simulations. The results prove that refined TSFA is able to get consistent good azimuth imaging quality of wide swath scene.

Urban area man-made target detection based on SAR images has been a challenging field for years due to the complicated scattering mechanisms of dense buildings and the poor visual quality of SAR images caused by speckle noises. To overcome the effect of speckle noise, a substantial portion of SAR image processing methods are based on statistical characteristics. In this paper, the importance of using non-zero mean models is discussed in the experiments, and two statistical models are proposed for polarization SAR image processing. Moreover, a non-zero mean index is invented to test the scattering determinacy level. This work will be very helpful for the improvement of urban area information extraction based on fully polarized SAR images.

The squint synthetic aperture radar (SAR) is capable of improving the coverage performance of SAR system while suffering from large range cell migration (RCM) and heavy computation load. To alleviate RCM, this paper proposes an innovative sliding receive-window (SRW) technique, in which the receive-window starting time varies pulse by pulse as a function of range-walk. Moreover, a refined Omega-K algorithm is deduced for focusing highly squint airborne stripmap SAR using SRW technique. In refined Omega-K, a new stolt interpolation relationship is found for the RCM is modified by the SRW technique. Finally, the imaging results justify the validity and accuracy of the refined Omega-K algorithm.

The side-lobes of strong target in conventional SAR image always impact the image qualities, sometimes submerge the weak targets. A novel SAR scheme with ultra-low range PSLR performance was proposed, by employing CC-S as SAR transmitted waveform. The CC-S coding waveform was utilized to acquired ultra-low range PSLR performance, by mutually canceled the side-lobes. As the CC-S is composed of groups of self-complementary sequences, each sequences should be transmitted with proper scheme, and the receiving time difference of each sequences should be compensated. Furthermore, the image formation algorithm for accurately focusing raw data of the SAR system was also proposed. Computer simulation results were presented, which demonstrated the validity of the proposed SAR scheme and image formation algorithm.

Agile small SAR has broad applications, due to its high mobility and wide coverage. In this paper, we put forward an attitude steering strategy for agile small SAR satellite with sliding spotlight mode. This attitude steering strategy contains three steps. And according to the results of simulation, this attitude steering strategy has a very good performance.

Glacier, due to the sensitivity to the climate change, has become an important indicator for global change research, especially the glaciers in the Arctic, Antarctic, and the third pole (Qinghai-Tibet plateau). Remote sensing, which has a capability to acquire the regional information of glaciers quickly, has become an effective tool to monitor and study the change of glaciers. In this paper, combining the NECP/NCAR reanalysis climatic data, the long time-series and continental area change of the glaciers and their melted lakes in West Kunlun with different watersheds were comparative analyzed by using LandSat TM/ETM+images from 1991 to 2009, which is located in the northern margin of the Qinghai-Tibet plateau. The results show: 1) The total area of the glaciers in eastern West Kunlun reduced firstly then increased. Meanwhile the total area of the glaciers in western West Kunlun sustained decreased in recent 20 years. 2) There is a significant negative correlation between the area change of glaciers and the corresponding welted lakes in West Kunlun. 3) The area change difference of the glaciers in the eastern and western regions in West Kunlun is mainly induced by temperature.

A novel technique for removal of scalloping in ScanSAR images is proposed. Scalloping artifact is modeled by gain and offset parameters exhibiting periodic variations as function of azimuth time. In proposed technique, Kalman filter is used to find minimum mean square error estimates of gain and offset parameters are found out for each azimuth position in a sub-swath. The range samples at a certain azimuth position are considered as observations for the parameters estimation. The estimated gain and offset are used to remove the scalloping artifacts from ScanSAR image. The proposed technique was applied on test images corrupted with scalloping artifacts and simulation results exhibited the potential of proposed technique to be used as postprocessing step in ScanSAR imaging.

A novel technique for despeckling of synthetic aperture radar (SAR) is proposed. A predefined number of overlapping subsets of pixels are selected from SAR image. Each subset is comprised of pixels selected from uniformly distributed locations. The subsets of pixels are elected in such a way that at least 20% of pixels in any subset should be different from pixels in any other subset. By considering each subset as compressive samples, a complete SAR image is reconstructed using convex optimization algorithm. These compressive reconstructed images are used to obtain despeckled SAR image. The proposed technique is tested on patches from stripmap TerraSAR-x data set. The proposed despeckling outperforms other benchmark despeckling methods in terms of visual quality as well as despeckling capability measuring metrics.

A new technique based on best bases sparse representation is proposed for fusion of remote sensing images. In order to carry out multi-resolution image fusion, low-resolution image is upscaled to match resolution of high-resolution images. Corresponding patches from remote sensing images are represented by finding out best bases from over-complete dictionaries comprising of elements derived from basis function of DCT, Wavelets, ridgelets, and curvelets. The corresponding bases of image patches are combined based on local information parameter (LIP) derived from respective patches. The use of LIP helps ensure transfer of details in high-resolution image into fused image.

In order to effectively reduce the storage volume of complex image data in high-resolution synthetic aperture radar (SAR), blocked spectrum compressive framework [1][2] based on Root-MUSIC algorithm is proposed. In this paper block-wise processing [3] of SAR image method is introduced, which can effectively reduce the storage space of measurement matrix. Gaussian random matrix is employed as observation matrix [4][5] to obtain observed value of each sub-block. Model parameters can be calculated by means of Root-MUSIC algorithm. Spectrum signal is reconstructed from small amount of measurements. Simulation results with real spatial-sparse SAR image demonstrate that data storage capacity can be reduced to as low as 1.17%, which validate the effectiveness of the method.

Biomass, as an indicator of vegetation productivity, can evaluate the contribution of wetland vegetation to carbon sink and carbon source. Long time and quantitative biomass study can help to acknowledge and understand the global carbon balance and carbon cycle. RADAR, which can work all day/weather and can penetrate vegetation in some extent, can be used to retrieve vegetation structure information, even the biomass. Here, the RADARSAT-2 data was used to retrieve vegetation biomass in Poyang Lake wetland. Based on the canopy scattering model, which is based on radioactive transfer model, the vegetation backscatter characteristics at C band were studied and good relationship between simulation results and backscatter in RADATSAT-2 image were achieved. Using the backscatter model, pairs of training data (backscatter coefficients in HH, VV, HV polarization mode and polarization decomposed components) were built and were used to train the Back Propagation (BP) artificial neural network (ANN). The biomass was inversed using this ANN, and compared to the field survey. It shows that the combination of the canopy scatter model and polarimetric decomposition components can improve the inversion precision efficiently.

The interpolation-based Polar Format Algorithm (PFA) can be used in bistatic Synthetic Aperture Radar (SAR) image processing while suffering from heavy interpolation computation load and complicated space-dependent resolution. To decrease computation load, this paper presents a nonlinear trajectory-based PFA, in which sensors are designed to fly on conical surface to avoid range interpolation. Due to this conical bistatic geometry, two advantages can be achieved. First, part of computation load can be converted to navigation system and processing speed can be improved. Second, a space-independent range resolution can be approached. A following multi-scatter simulation validates the presented approach.

MIMO radar can be considered as a particular communication system, which enjoys similar profits to the MIMO communication theory. Based on the information theory, the concept of channel capacity was introduced into the MIMO radar system. In this paper, the capacity expressions for MIMO SAR system were derived. As is shown in the simulation results, the channel capacity of the MIMO SAR depends on the number of the antennas, channel character and radar system parameters. The capacity of MIMO SAR increases with pulse repetition frequency and decreases with range and velocity. Furthermore, the larger the antenna number is, the more information can be obtained from the echoes. Meanwhile, the MIMO SAR capacity is larger than phase array SAR in high SNR.

The compact polarimetric mode of synthetic aperture radar (SAR) provides an approach of polarimetric observation by a reduced PRF and doubled swath width, which has already proven its advantage compared with the traditional full-polarimetry (FP) mode in several occasions. In this paper, a quantitative evaluation method for the compact polarimetric SAR image reconstruction is proposed. The quad-pol data are reconstructed by the iteration algorithm proposed by Souyris et al.. Analysis is carried out on the performance of the algorithm based on information theoretic analysis. The performance of the algorithm is evaluated quantitatively by implementing information entropy, mutual information, Kullback-Leibler distance and difference of entropy between the reconstructed and original FP data. Based on this analysis, several key parameters of this algorithm is verified and optimized.

A novel compressive sensing (CS) based non-uniformity correction (NUC) technique for focal plane array (FPA) imaging system is proposed. Instead of estimating NUC parameters from video sequence at 100% sensors in FPA, only 40% sensors are selected via CS measurement matrix for real-time NUC estimation and CS reconstruction algorithm is exploited to obtain NUC compensation table for 100% sensors. In this study, temporal highpass filtering technique is used to estimate NUC parameters at selected pixels from sequence of video frames. The simulation results show that proposed CS based NUC framework can effectively remove FPA non-uniformities.

Geosynchronous orbit synthetic aperture radar (GEO-SAR) has a good application prospect for its shorter repeat period and a wider swath compared to low Earth orbit SAR. Doppler characteristic of the echo is analyzed. The Doppler history varies frequently in space and approximates a high order signal. A back projection algorithm for high resolution GEO-SAR based on satellite-ground geometry model is preliminarily proposed. Steps of the algorithm are given. The simulation results indicate the algorithm can achieve high resolution image formation in large scene. The azimuth resolution is less than 2m.

This paper conducted thorough research to the theory of space-borne Sliding spotlight and TOPS data processing. First, the unified definition of hybrid factor of both TOPS and sliding spotlight mode was given. Then, the hybrid factor was analyzed and amended along the range direction combined the geometry model. Combined with amended hybrid factor, azimuthal time-frequency characteristic of was analyzed and discussed in detail by mathematic derivation. Based on the analysis, an efficient and precise extended three-step image formation algorithm was presented for sliding spotlight and TOPS image formation. Finally, the imaging results justify the effectiveness of the extended three-step algorithm.

Spaceborne synthetic aperture radar (SAR) systems operating at lower frequencies, such as P-band, are significantly affected by Faraday rotation (FR) effects. A novel algorithm for calibrating the circular-transmit-and-linear-receive (CTLR) mode spaceborne compact polarimetric SAR using mixed calibrators is proposed, which is able to correct precisely both FR and radar system errors (i.e., channel imbalance and crosstalk). Six sets of mixed calibrators, consisting of both passive calibrators and polarimetric active radar calibrators (PARCs), are investigated. Theoretical analysis and simulations demonstrate that the optimal calibration scheme combines four polarimetric selective mixed calibrators, including two gridded trihedrals and two PARCs, together with total-electron-content measurements by the Global Navigation Satellite System system.

This paper conducted thorough research to the theory of space-borne TOPSAR data processing. The time-frequency characteristic of TOPSAR mode signal was analyzed and discussed in detail by mathematic derivation combined with the TOPSAR factor. Based on the analysis, an efficient and precise three-step image formation algorithm was presented for TOPSAR image formation without data division, and the operation in every step was theoretically proved effective. The de-rotation operation in the first step and deramp operation in the third step are adopted to finish the stretch in both frequency and time domains respectively, by which the azimuth folding effects in the time and frequency domains are overcome.

Spaceborne polarimetric synthetic aperture radar systems operating at lower frequencies, such as P-band, are significantly affected by Faraday rotation (FR). A new set of FR estimators is derived from the off-diagonal terms in the measured covariance matrix of a distributed target. These estimators have a phase ambiguity of period , instead of /2 as for the published estimators, and this ambiguity can be completely resolved for arbitrarily large values of FR using total electron content maps derived from Global Navigation Satellite System measurements. Simulations show that one of the new estimators has particularly high resistance to system noise and channel amplitude imbalance but greater sensitivity to channel phase imbalance than the published estimators. Hence, the expected values of residual system distortion after calibration may affect the choice of estimator.

Attitude stability is a very important design parameter to synthetic aperture radar (SAR) satellite platform. The mathematical expression of distributed satellite SAR system impulse response function with attitude jitter is studied using paired echo theory. The effect of attitude stability on interferometric performance is confirmed according to the change of peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR). The constraint relationship is given between three-axis attitude jitter and interferometric performance. Through computer simulation, the relationship curve is obtained to verify the conclusion. The study in this paper provides an important theoretical basis for the integrated design of distributed satellite SAR system.

The exploration of ionosphere is significant for satellite communication and navigation etc. Spaceborne HF-SAR is utilized for the observation of topside ionosphere, in order to acquire higher spatial resolution, global scale ionospheric electron density map and irregularities distribution. The operation mode and system parameters are introduced. The echo signal of spaceborne HF-SAR has long synthetic aperture time, large range migration, and small depth of focus due to the low carrier frequency. Considering these characteristics of spaceborne HF-SAR, a two dimension time-frequency domain correlation image formation algorithm is presented. The effectiveness of the algorithm is validated by computer simulation results.

The GMTI (Ground Moving Target Indication) performance for circular scanning SAR (Synthetic Aperture Radar) equipped on slow platform was analyzed in this paper. The characteristics of SAR equipped on slow platform and the GMTI application were descript in the beginning, and the circular scanning mode was advanced to resolve the low azimuth imaging efficiency of the slow platform borne SAR. Then the range models of the stationary and moving targets based on circular scanning mode were built, and the Doppler frequency expressions were derived from the range models. The detectable velocities distribution areas were derived by separating the Doppler frequency of two kinds of targets. The quantitative computer simulations were given to demonstrate the detectable velocities area along with azimuth angle and pulse repetition frequency in the end.

Multi-sensor image fusion has attracted much attention in the remote sensing area. It urgently needs a universal and effective objective evaluation approach to measure the effect of image fusion. A new approach based on the singular value decomposition (SVD) is proposed for the remote sensing image fusion assessment. This method measures the divergence of the singular value features between the source images and fused image, and calculates energy distortion of the fused image from input images. By that means, the effect of the fusion algorithm is measured. Experiments are conducted from three aspects to confirm the idea. Firstly, when the source images include a SAR image, this method is more effective than Piella's evaluation methods and Xydeas's evaluation methods. Secondly, the experiments of different kinds of sensors and pixel-level fusion algorithms show that this objective evaluation appears highly consistent with the subjective evaluation. Lastly, the simple feature-level fusion images are measured by this objective evaluation method, and the results show coherence to the subjective factors. These experiments demonstrate its general effectiveness.