Deep learning models have shown excellent performance in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. However, the opacity of the decision-making mechanisms within these models hamper their credibility in practical applications. Therefore, numerous explainable artificial intelligence (XAI) methods have been developed to interpret the models. Among them, the randomized input sampling for explanation (RISE) method introduces input random pixel perturbations to observe resulting changes in the output by generating saliency heatmaps. However, unlike optical images, SAR images have their unique physical properties. This paper introduces the attributed scattering center (ASC) into the RISE method, known as the ASC-RISE method guided by physical information, to explain the model. Experimental results demonstrate that the heatmaps generated by ASC-RISE effectively locate the model’s decision features and provide corresponding physical information.

To mitigate the overfitting in the few-shot high-resolution range profile (HRRP) recognition, we introduce the Mahalanobis based statistical ProtoNet (MSP) with regularization, inspired by the prototypical network (ProtoNet). MSP leverages regularized feature covariance matrix to enhance the ProtoNet’s Euclidean distance metric based on the isotropic Gaussian distribution. Additionally, we propose a simplified MSP, the normalized statistical ProtoNet (NSP) for the faster inference of the statistical ProtoNet. Experiments demonstrate that statistical distance metrics enhance the few-shot recognition performance in scenarios with varying signal-to-noise ratios (SNR) and domain bias.

Deep learning has significantly enhanced the performance of automatic target recognition (ATR) in synthetic aperture radar (SAR). However, the concept of model overinterpretation, characterized by classifiers discerning strong class evidence within image regions that lack semantically salient features related to target (e.g., background clutter in SAR images), has undermined confidence in the reliability of deep learning models. Previous studies predominantly relied solely on one interpretability method to qualitatively identify the key input pixels, without assessing the efficiency of these features in decision-making process, posing a significant hurdle in evaluating the model overinterpretation. In this paper, we propose necessity-sufficiency index (NSI) to select models’ decision-making basis among all the key regions identified by multiple interpretability methods and segmentation algorithm. Furthermore, we propose weighted composition ratio statistic (WCRS) method to quantitatively analyze the model overinterpretation by incorporating the NSI as weighted average weights. The experimental results indicate that our methods are capable of accurately identifying the decision-making features and quantitatively analyzing the models’ tendency towards overinterpretation.

Most of the conventional ship detection methods based on synthetic aperture radar (SAR) intends to process the focused images, which does not take full advantages of the intermediate data in the SAR imaging process. In this paper, we introduce a new framework that treats a two-dimensional point target as multiple one-dimensional sequences in the range-compressed domain, and then employs a Long Short-Term Memory (LSTM)-based network to perform the ship detection, thus reducing the computational burden and improving efficiency significantly. To validate the effectiveness of our proposed method, we conduct experiments on real SAR data. The results demonstrate the superiority of our framework in ship detection tasks.

The high-resolution range profile (HRRP), with its compact vector expression and rich structural information, has become an essential part of radar automatic target recognition (RATR) systems. However, The limited training data strictly limits the recognition performance. In this paper, we proposed an HRRP recognition method for few-shot non-cooperative targets. The proposed method leverages the HRRP of simulated aerial targets as prior information and exploits the HRRP normalization and alignment to improve the generalization. In addition, we introduce an efficient feature extractor with squeeze-and-excitation attention to refine the feature map. The experiments based on simulated HRRP show that the proposed method achieves the best recognition performance under extremely few-shot conditions among conventional few-shot learning methods such as model-agnostic meta-learning (MAML) and prototypical networks (ProtoNet).

Deep learning techniques have shown promise in the field of remote sensing; however, they face challenges related to the availability of accurately labeled data and their performance in open-set cases. To overcome these limitations, this artic le presents a self-supervised learning approach for ship target classification in remote sensing images. The proposed method leverages a combination of self-supervised learning techniques applied to high-resolution optical images. Moreover, the article investigates the impact of unknown categories on classification performance and introduces a statistical extremum-based anomaly identification method to address the open-set problem. Experimental evaluations demonstrate that the proposed approach achieves state-of-the-art classification performance.

The cross correlation power spectrum of multiple signal sequences in the singularity domain is studied in this paper. With theoretical derivation and quantitative analysis, the cross correlation singularity power spectrum (CSPS) distribution theory is proposed. Developed from correlation function (CF), spectrum CF (SCF), singularity power spectrum (SPS), and multifractal cross correlation analysis, the CSPS can be applied for the correlation analysis of multiple fractal time series. In this paper, the CSPS is rigorously derived based on SPS and SCF, and is verified with classical multifractal time series. Furthermore, a target detection method based on the proposed CSPS method is also proposed. The proposed methodology is tested on sea clutters, both with and without target, from the Ice Multiparameter Imaging X-Band radar data set. The simulation results indicate that the target detection based on CSPS performs better than conventional multifractal spectrum methods, and can achieve almost 100% detection probability of detecting low-observable targets within sea clutters.