Synthetic aperture radar (SAR) is vulnerable to interference, including intentional and unintentional-ones. Noise frequency modulated (FM) interference is a kind of intentional interference, which has the characteristics of broadband and randomness, which makes the noise FM signal become a kind of most commonly used interference signal. Noise FM interference will have a serious impact on the SAR image, but the current algorithms for interference suppression are not sufficiently studied. This paper extends a time-domain cancellation algorithm for suppressing the noise FM interference of SAR. This algorithm can reconstruct the noise FM interference signal from the contaminated SAR echo, and then suppress the interference component in the echo by time-domain cancellation. Finally, this paper validates the superior performance of the algorithm by point target simulation and Radarsat-1 data. The proposed method is valid even when the signal-to-interference ratio is lower than -40dB.

Super resolution technology has played a significant role in enhancing the imaging resolution of forward-looking scanning radar. However, a large number of super-resolution methods still rely on single frame scanning echoes. This paper aims to leverage multi-frame real beam images for super-resolution imaging, utilizing the complementary information present in multiple images to construct a higher resolution image. This paper first establishes the multi-frame super-resolution imaging model. Subsequently, a feasible multi-frame super-resolution method was proposed, and motion parameter estimation was performed using the correlated phase method. Finally, the effectiveness of the proposed method was verified through simulation experiments.

The signal emitted by ground radiation sources often interferes with Synthetic Aperture Radar (SAR) satellites, with the most common interference being periodic pulses emitted by ground radars. This paper proposes a method for locating ground-based periodic pulse signal interference sources using SAR echo data. Firstly, We estimate the Time Difference of Arrival (TDOA) of each pulse emitted by the interference source to SAR from the received SAR signals, and we seek the mapping relationship between the coordinates of the interference source (latitude and longitude) and the variations in TDOA. Using this mapping relationship, we achieve the localization of the interference source through a two-dimensional search method. The proposed method in this paper is highly versatile, applicable to single-station SAR satellites, multi-station SAR, and single-station SAR with multiple passes. It is also applicable regardless of the modulation form of the interference signal. Finally, the proposed TDOA-based localization method is experimentally validated for its accuracy based on GaoFen-3 satellite-borne SAR. The results demonstrate that the positioning error using two measurements from the satellite is only 3.708 km.

In radar detection, small and slow targets are easily overwhelmed by strong clutter. Traditional methods, such as singular value decomposition (SVD) and robust principal component analysis (RPCA), can suppress clutter and recover targets by using low-rank and sparse models. However, these methods rely on fixed prior information, which lacks adaptivity and suffers from unfavorable extensive manual hyperparameter tuning. To address these issues, a data-driven deep network model combined with an iterative algorithm called unfolding joint sparse recovery network (UFJSR-Net) is proposed to achieve the improved target detection performance. First, a joint sparse recovery (JSR) model is established and the fast iterative shrinkage/thresholding algorithm (FISTA) is derived to solve this model. Then, one iteration consisting of a linear operation and a nonlinear one can be recast into a single network layer and the stacking and combination of all layers will form the UFSJR-Net. Finally, the properties of the target and clutter can be learned by paired inputs and outputs training data to optimize the hyperparameters in the iterative algorithm to obtain the JSR model. Experiments on simulation data and measured radar data demonstrate that the proposed method exhibits advantages in detection performance over traditional decomposition methods under strong clutter with different intensities.

Accurate mapping of forests is critical for forest management and carbon stocks monitoring. Deep learning (DL) is becoming more popular in Earth observation (EO), however, the availability of reference data limits its potential in wide-area forest mapping. To overcome those limitations, here we introduce contrastive regression into EO-based forest mapping and develop a novel semisupervised regression framework for wall-to-wall mapping of continuous forest variables. It combines supervised contrastive regression loss (CtRL) and semi-supervised cross-pseudo regression (CPR) loss. The framework is demonstrated over a boreal forest site using Copernicus Sentinel-1 and Sentinel-2 imagery for mapping forest tree height. Achieved prediction accuracies are strongly better compared to using vanilla UNet or traditional regression models, with relative root mean square error (rRMSE) of 15.1% on stand level. We expect that the developed framework can be used for modeling other forest variables and EO datasets.

In this study, we evaluate the potential of deep learning models in predicting forest tree height in boreal forest zone using ESA Sentinel-1 and Sentinel-2 images. The performance of studied deep learning models is compared to several popular conventional machine learning approaches. The study area is located near Hyytiala forestry station in Finland, and represents a conifer-dominated mixed boreal forestland. Improved predictions were obtained when using combined optical and SAR data for all studied models. Our results indicate that UNet based models can achieve better accuracy in predicting forest tree heights (RMSE of$1.90m,\ \mathrm{R}^{2}$of 0.69), compared to traditional parametric and machine learning models with RMSE range of$2.27-2.41m$and$\mathrm{R}^{2}$range of 0.50-0.56 when satellite optical and radar data are combined.

Since the start of the Sentinel-1 mission, numerous cases of severe image degradation caused by RFI have been reported, which puts forward an urgent need for RFI identification and mitigation. In this paper, an automatic RFI identification method is proposed based on a siamese-type deep convolutional neural network (Siam-CNN-RIM). The Siam-CNN-RIM can be served as a pre-processing step before RFI mitigation to identify whether an S-1 image is RFI-contaminated or not. Different from traditional RFI identification networks which only use a single image as input, an additional image in the repeat-pass time-series is also fed into the input of Siam-CNN-RIM as a reference. Both of the input images correspond to the same illuminated area, and pass through the same convolutional layer followed by an energy function, such that the different features caused by RFI can be extracted and the background terrain features can be ignored. This is beneficial for distinguishing the real RFI signatures and the similar terrain signatures that may cause false positives, and thus improving the RFI identification performance. Experimental results show that the proposed method is robust in different scenarios and can achieve more than 97% RFI identification accuracy, even for the open-set task where the test scenarios are not included in the training set.

Radio frequency interference (RFI) suppression is a hot topic in synthetic aperture radar (SAR) imaging. Mathematically, the RFI suppression problem can be considered as an underdetermined signal separation problem to extract the signal of interest (SOI) from the RFI contaminated raw data. The regularization-based method can exploit both the prior knowledge of RFI and SOI and, therefore, has the advantage of solving the underdetermined problem and preserving the information of SOI. Current regularization methods make use of the RFI prior well by exploiting low-rank representation (LRR) or sparse representation (SR), but the prior knowledge of SOI has not been sufficiently studied and used. In some literature, the sparsity of the raw data or range profile was exploited to formulate the regularization term, which we found to be inadequate in describing the SOI property. In this article, we explore the features of SAR images and propose an RFI suppression model with a combination of multiple image domain regularizations to preserve different types of targets. An efficient solution to the optimization problem is proposed based on the alternating direction multiplier method (ADMM). The proposed method can accurately recover both the sparse strong targets, and the nonsparse regions in the illuminated area and its performance is validated by measured data.

Deconvolution technology can be utilized to improve the angular resolution of real-aperture scanning radar (RASR) with high efficiency and low cost. However, it is an ill-posed problem and the solution is sensitive to noise. Regularization methods are considered to be efficient ways to ease the noise sensitivity by absorbing the prior information into the objective function. In this paper, we propose a new super-resolution imaging method for RASA based on the Markov random field (MRF). Compared with the published angular super-resolution methods for RASA, the proposed method takes advantage of the two-dimensional spatial prior information and can recover the shape of scene much better. Simulations are carried out to demonstrate the effectiveness of the proposed method.

The performance of synthetic aperture radar is vulnerable to radio frequency interference (RFI). In many situations, the RFI has a low-rank property, since the frequency bands occupied by RFI usually remain stable during a short slow time period. Therefore, low-rank representation (LRR)-based methods can be applied to separate RFI and signal of interest (SOI), by minimizing the rank of RFI components with a regularization constraint to protect SOI. However, traditional methods use the sparsity of the raw data or range profile to formulate the regularization term, which fails to describe the properties of SOI accurately. In addition to the sparse property of range profiles, this article explores the common patterns hidden in the range profiles and proposes two new LRR-based RFI suppression optimization models with a well-designed regularization term to describe such common sparsity to protect the SOI. Four methods are proposed to solve the optimization problems based on the alternating direction multiplier (ADM) method, which provides tradeoff between efficiency and accuracy. Compared with traditional LRR-based RFI suppression methods, the proposed methods make a more precise description of the features of SOI, therefore can better protect the information of SOI during the RFI suppression process and improves the imaging quality. The superior performance of the proposed method is validated by measured data in both sparse and nonsparse scenes.

In this study, we assess the potential of long time series of Sentinel-1 SAR data in forest growing stock volume (GSV) estimation. The study site with 17,762 forest stands is located near the Hyytiälä forestry field station in Finland and represents the boreal coniferous forest. Altogether 96 images spanning more than three years of observations have been studied using linear and random forest regression approaches. Our analysis demonstrates considerable decrease in the prediction errors of GSV as the the number of input scenes increases. The use of feature extraction and dimensionality reduction techniques allows to achieve to nearly optimal performance already with 10 scenes. While the GSV prediction errors using individual Sentinel-1 scenes varied considerably from 86 to 93 m3/ha, the prediction accuracy with combined scenes improved to 76 m3/ha (44.9%) RMSE.

Deceptive jamming can severely degrade synthetic aperture radar (SAR) image quality by introducing high-fidelity false targets. In this letter, a simultaneous deceptive jamming suppression and target reconstruction method for a single-channel SAR system is proposed. The signal model is formulated by constructing a joint dictionary based on different time-frequency distributions of the actual targets and false targets. Then, an efficient algorithm is proposed based on the alternating direction method of multipliers (ADMMs) to simultaneously recover the actual and false targets. Several strategies are also proposed to accelerate the computation. Compared with other existing single-channel SAR deceptive jamming suppression methods, the proposed method has lower reconstruction error and computational load. Simulation results demonstrate the superior performance of the proposed algorithm.

To date, the potential of multitemporal interferometric SAR (InSAR) data in land-cover mapping has not been fully explored despite suitable time series increasingly acquired from SAR sensors. Here, we suggest to use an LSTM (Long Short Term Memory) based land-cover classifier to address this problem. Spatial context is preserved by using grey-level spatial dependencies and morphological profiles. Further, a 4-LSTM-based model was trained to capture the temporal dynamics of InSAR coherence. Altogether 39 Sentinel-1 interferometric coherence pairs acquired over Donana in Spain were used to evaluate the method performance. Achieved more than 90% overall accuracy indicates the strong potential of developed InSAR recurrent approach in improving differentiation between various land cover classes.