In the scene of remote sensing image (RSI) recognition, it is difficult to obtain a sufficient number of samples for training all categories at once. A more realistic situation is that the recognition task occurs in an open environment, with categories gradually increasing. Additionally, due to the difficulty of collecting certain data, there are only a few samples for each new category. This leads to the problem of few-shot class-incremental learning (FSCIL), where the model learns incrementally and the number of samples for incremental classes is very small, generally only a few, while the number of samples for base classes is relatively large. To address this, this paper proposes a model framework for FSCIL of RSIs, called HDCPAA. The model is mainly divided into three parts. The first part is the feature extraction network, which is pre-trained on the base classes and then its parameters are frozen in subsequent incremental learning to alleviate catastrophic forgetting of the base classes. The second part is a fully connected layer, which transforms the prototypes of each category into quasi-orthogonal prototypes to increase the distance between the prototypes. The third part is the prototype adaptation attention module, which adaptively updates prototypes and query vectors using attention mechanisms. The training process of this module is based on the meta-learning of pseudo-incremental classes. Experiments on two popular benchmark RSI datasets, MSTAR and NWPU-RESISC45, show that our model significantly outperforms the baseline models and sets new state-of-the-art results with remarkable advantages. Our code will be uploaded at: https://github.com/lipeng144/HDCPAA .

Although deep neural networks (DNNs) have achieved good results on some common datasets of synthetic aperture radar (SAR) automatic target recognition (ATR), DNNs are opaque and difficult to interpret, which limits its practical application. In view of this, many interpretable models have been proposed in recent years. However, the previous interpretable models only represent transparent network structures and cannot be called real “interpretable” models. We think that the interpretability of a model contains two meanings: on the one hand, the decision process of the model is transparent, and on the other hand, the decision-making basis of the model should be reasonable and conform to human cognition. For this reason, we propose a new SAR image recognition model based on adversarial defense, namely SAR-AD-BagNet. Not only does it have a transparent decision-making process, but it also has a more reasonable basis for decision-making. In addition, the model also has high SAR image recognition accuracy and strong adversarial robustness.

Micro-Doppler (MD) extraction is the precondition for target classification based on micro-motion. This letter proposes a method for MD extraction based on constrained nonnegative matrix factorization (NMF). The proposed algorithm is applied to the time-frequency spectrogram of the cone-shaped precession target. Taking the sparsity constraint, temporal continuity constraint, and approximate orthogonality constraint into consideration, the basic NMF is improved to match the MD separation problem, and thus the time-frequency spectrogram of each scattering center is reconstructed by deriving the basis matrix and coefficient matrix. Morphological processing, valid frequency extraction, and MD curve fitting are conducted on the reconstructed spectrograms to extract the MD accurately. Experiment results show that the proposed approach outperforms other algorithms with better accuracy and robustness.

Correct and robust translational motion compensation is necessary but challenging for inverse synthetic aperture radar (ISAR) imaging and target recognition. On the one hand, parametric translational motion compensation methods only effective for polynomial translational models, on the other hand, the noise robustness of range alignment-autofocus methods is not satisfactory. Therefore, this work proposes a joint range alignment and autofocus method based on Combined Broyden-Fletcher-Goldfarb-Shanno algorithm and whale optimization algorithm (BFGS-WOA). The method aims to achieve accurate and robust compensation of profile shift and the phase error simultaneously without relying on a specific translational model. Specifically, we use Laplacian entropy and squared envelope entropy to construct a dynamic objective function. It can improve the robustness of translational error estimation. The translational error of each pulse can be estimated by minimizing the objective function. We adopt BFGS to solve the optimization to ensure the global convergence. WOA is introduced to determine the optimal step size of the iteration. Finally, the estimated translational error is used to perform range alignment and autofocus simultaneously. Experimental results of measured datasets demonstrate that the proposed method outperforms existing methods and has strong robustness for low signal to noise ratio (SNR) and sparse aperture.

There are a number of targets in space that are similar in shape, but they pose different threat levels. It is necessary to discriminate these targets for the sake of space security. The unique characteristics of detailed micro-Doppler (MD) signatures can be used to classify micromotion forms, such as spinning, precession, nutation, wobbling, and tumbling. In this letter, we design a novel processing flow for classifying five types of micromotion under low signal-to-noise ratio (SNR) condition. We start from the range map and successively perform noise-level estimation, adaptive filtering, and multithreshold segmentation to obtain an ideal binary mask. The clean range maps are used as the final input to an eight-layer convolutional neural network (CNN) for training, validation, and testing. The experiment results show that our method achieves more than 80% classification accuracy for micromotion in the case of −10 dB, which is higher than the existing popular methods.

The micro-Doppler (MD) signature, an effect that is induced by micro-motion, characterizes the rich motion information of targets, making it a valuable tool for target classification and recognition. However, the MD structure is always destroyed for radar targets with translational motion. In this letter, we propose an end–to–end translational motion compensation network based on a spatial transformer network (STN). First, the effect of the translational motion on the radar echo and its time-frequency graph (TFG) is modeled, so that translational motion compensation is regarded as an image spatial transformation task. Then, a two-branch convolutional network based on residual modules is designed to locate the transformation parameters, and the TFG containing only MD is obtained using a grid generator and sampler. Finally, the simulation results demonstrate that the proposed algorithm has high accuracy and stability.

Translational motion compensation for maneuvering targets is a challenging step in inverse synthetic aperture radar (ISAR) imaging. On one hand, nonuniform translational motion and rotational motion of maneuvering targets result in nonlinear bending of the range profile and create high-order slow time phases. On the other hand, radar cross section (RCS) fluctuation of targets may cause a sparse aperture and, therefore, sharply weakens the correlation between the pulses. Under this condition, traditional methods of translational motion compensation are sometimes difficult to get ideal results. Therefore, we propose an effective and novel method to achieve translational motion compensation for maneuvering targets with sparse aperture. In this method, the translational and rotational motions are modeled as cubic polynomial and quadratic polynomial, respectively. Based on this model, we use the echo to construct a 1-D optimization to estimate all translational parameters simultaneously and use the root-mean-square prop-momentum gradient descent (RMSprop-MGD) algorithm to solve the optimization to maintain the accuracy of parameter estimation. Finally, we use those translational parameters to compensate for the translational motion. Since this method converts the multiparameter optimization into a 1-D optimization, we named it dimension compressed optimization (DCO). Experimental results of the measured dataset prove that the proposed method is effective for full-aperture echoes, random sparse echoes, and block sparse echoes. Moreover, this method is robust under a low pulse sampling rate and low signal-to-noise ratio (SNR) environment.

Accurate translational motion compensation is critical for ISAR imaging, especially for datasets with low signal-to-noise ratio (SNR) or sparse aperture. Among the existing translational motion compensation methods, the methods that based on the polynomial model have profound noise robustness but are easily affected by the variation of the reference distance. The methods that based on the range profile alignment have good versatility but are far from ideal under low SNR and sparse aperture environment. Therefore, we propose a novel and robust translational motion compensation method to overcome the shortcoming of the existing methods. Specifically, we estimate the translational phase error sequence by maximizing Laplacian average gray level and peak value. It can improve the robustness of translational motion compensation for noise and sparse aperture. Since such principle creates a large-scale multi-objective optimization problem, we design weighted optimization framework - second-order drift particle swarm optimization (WOF - SDPSO) to solve it accurately. WOF-SDPSO achieves full exploration of high-dimensional variable space by variable dimension reducing, random drift update, and second-order oscillation convergence. These ensure the global optimal solution easier to be found. Experimental results of the measured dataset prove that the proposed method is effective and accurate under the conditions of low SNR and sparse aperture. Moreover, the proposed method is noise robust for polynomial translational motion model with sparse aperture.

Multiple-input multiple-output (MIMO) radar imaging is a new technique to obtain the radar image of aerospace targets. Orthogonal waveform design is one of the important issues for MIMO radar imaging. However, the fully orthogonal waveforms in the same frequency and with the arbitrary time delay do not exist in practice. Thus, the imaging result using nonorthogonal waveforms based on matched filtering (MF) method is usually unsatisfactory if further processing like digital beam forming (DBF) is not used. Sparse recovery (SR) method is possible to restrain the mutual interference of nonorthogonal waveforms by exploiting the sparsity of targets and improve the imaging quality. In this article, waveform design issue in SR-based MIMO imaging method is studied. The difference in the designs of waveforms in MF method and SR method is discussed. Based on requirements analysis, a comprehensive optimization model is built for waveform design and the existing cycle algorithm (CA) is modified to solve the model. Considering the fact that the target scene is always changing, waveforms should be adjusted along with the dynamic scene. Therefore, an adaptive waveform optimization method is further proposed based on the cognition of target scene. The dimension of SR model is reduced and the waveforms are optimized according to the cognitive target length. Moreover, based on the reconstructed target range profiles, transmitting waveforms together with recovery algorithm are further optimized to match the target better. Simulation results show that the waveforms after optimization are better than the nonoptimized waveforms and the proposed adaptive optimization method is valid and robust for the dynamic target scene.