InSAR-based scene matching navigation is a cutting-edge autonomous navigation scheme for GNSS-denied environments. However, due to the inherent speckle noise of SAR systems and texture differences between real-time and reference interferograms, the extracted matching feature points inevitably contain observation noise and outliers. Meanwhile, current mainstream platform localization inversion primarily relies on simplified airborne SAR imaging geometric models. Lacking error suppression mechanisms, these models cause the matching observation errors to directly propagate and amplify into platform positioning errors. To address these issues, this paper proposes a robust localization estimation model named Inv-RD. By incorporating time-dependent payload orbital equations, the inherently underdetermined Range-Doppler (RD) inversion problem is reframed into an overdetermined nonlinear optimization problem based on redundant observations. Subsequently, the Levenberg-Marquardt (L-M) algorithm is employed for global optimization, which significantly mitigates the impact of matching noise on localization accuracy. Experiments using measured flight data demonstrate that, even in the presence of matching errors, the proposed Inv-RD model significantly outperforms traditional geometric models in terms of positioning precision.

Generalized category discovery (GCD) aims to group unlabeled samples from known and unknown classes when only part of the labeled data in the known classes is given. It allows the model to adapt to dynamic environments by discovering novel categories. However, when we applied the GCD approach to the decentralized open world, we still encountered the following challenges: (1) none of labeled data easily obtained in the open world, (2) heterogeneous label spaces across different environments, (3)representation degradation caused by fine-tuning models with limited data in specific environments. To address the above challenges, we introduce a new and practical task, namely Cloud-edge GCD (CE-GCD). Different from semi-supervised GCD, CE-GCD assumes that we only have a base model trained on common public categories, and aims to perform personalized unsupervised novel category discovery in multiple environments with heterogeneous label spaces. Data from different environments or clients cannot be shared, only model parameters can be transferred. To tackle this problem, we propose a novel GCD framework based on energy-guided known class discrimination and multi-level contrastive learning. In each client, we first use the classifier of the base model to distinguish between known and unknown classes, and then perform unsupervised learning on the unknown classes. Each client transfers category information through prototypes to assist learning. Extensive experiments on multiple datasets demonstrate the effectiveness of our approach.

Most existing adversarial attack methods for remote sensing images merely add adversarial perturbations or patches, resulting in visually unnatural modifications. Clouds are common atmospheric effects in remote sensing images. Generating clouds on these images can produce adversarial examples better aligning with human perception. In this paper, we propose an adversarial attack framework that leverages natural cloud patterns as perturbations. Common Perlin noise-based cloud generation is a random, non-optimizable process, which cannot be directly used to attack the target models. We design a Perlin Gradient Generator Network (PGGN), which takes a compact gradient parameter vector (gradient vectors, coefficients, and scaling factors) as input and generates multiscale Perlin noise gradient grids. Through hierarchical computations, these grids produce scale-specific cloud masks, which are adaptively fused via learnable mixing coefficients and scaling factors. Crucially, the entire cloud generation process is formulated as a black-box optimization problem, where the cloud parameter vector is iteratively refined using the Differential Evolution (DE) algorithm. This approach enables query-efficient black-box attacks by directly aligning cloud shapes with adversarial objectives, while preserving natural cloud textures. Comprehensive experiments demonstrate the strong attack capabilities of this method, along with its high query efficiency. Furthermore, we conduct an in-depth analysis of the transferability of the generated adversarial examples and their robustness in adversarial defense scenarios.

Classification of hypersaline water bodies, e.g., salt fields and salt lakes, presents unique challenges due to the similar spectral and scattering characteristics of various saline water bodies. To address this issue, we propose an innovative classification method tailored for such environments, integrating Sentinel-1 multitemporal polarimetric synthetic aperture radar (PolSAR) data with Landsat multispectral imagery. The method introduces a novel PolSAR-based feature, termed scattering mechanism entropy, to quantify variations in salt crystal precipitation processes. Additionally, the blue band from multispectral imagery is leveraged to represent ion concentrations in hypersaline water bodies. By deriving the statistical relationship between scattering mechanism entropy and blue band data for each pixel, we amplify the separability of salt features and mitigate the influence of spectral similarity. These derived features are then concatenated into a Chernoff distance-based classifier for improved classification performance. To validate the robustness and effectiveness of this method, we apply it to the classification of salt fields and salt lakes on four major salt lake sites in China: Qarhan salt lake (2018–2020), Yiliping salt lake (2021–2023), Taijnar salt lake (2019–2023), and Gasikule salt lake (2019–2023). The proposed approach achieves classification accuracies of 90.91%, 92.73%, 97.40%, and 97.65%, respectively, significantly outperforming existing water body classification methods.

Deploying deep learning-based remote sensing image interpretation models in orbit can alleviate data downlink pressure and enhance processing efficiency. To adapt to the arithmetic conditions of on-orbit platforms, deep learning models must undergo compilation before deployment, where the search and measurement of operator scheduling are critical aspects. The existing compilation methods related to the Tensor Virtual Machine (TVM) compiler suffer from inefficiencies caused by suboptimal starting point selection and extensive search spaces during the search process. Additionally, the clustering algorithm employed during the measurement of candidate scheduling relies on fixed dimensions and lacks flexible application management, resulting in slow convergence. This paper proposes corresponding optimization methods built upon the TVM framework to address these two challenges. In the search aspect, techniques such as starting point planning, salient reduction, and model sharing considerably reduce search time. In the measurement aspect, the clustering process is optimized through adaptive scheduling management and dimension adaptation, enhancing measurement efficiency. Experimental results indicate that, compared to current state-of-the-art deep learning compilers, the proposed method increases compilation speed by an average of 2.6 times while maintaining consistent inference speed.

Qarhan Salt Lake is located in the Qaidam Basin in northwest China, containing abundant salt mineral resources such as sodium, potassium, and magnesium. It is the largest salt lake and one of the most important salt lake industry bases in China. The changes and development of the salt crust are of great significance for understanding the ecological environmental change of the Qarhan Salt Lake and promoting sustainable production of salt lakes. However, the polarimetric scattering features are extremely similar between different types of salt crusts basically composed of dominant surface scattering with some volume and surface scattering, so the redundancy problem between features is particularly serious. The purpose of this paper is to select 7 polarimetric features with a classification accuracy of more than 95% from statistical and textural similarity, respectively, using the similarity metrics SSFSM for different salt shell polarized features, achieving a classification accuracy similar to that of all features.

Research about SAR target recognition has received a lot of attention in recent years. The relevant research results encountered some obstacles in their practical application. This is partly due to the long-tailed distribution of data in real-life scenarios. Specifically, a majority of data samples are concentrated in a few categories. The skewed distribution can cause learning bias toward the majority class. Although there have been some studies on long-tailed recognition for natural images, we found that these methods still need improvement when applied to SAR scenes. On the one hand, these studies use balanced datasets for performance testing, which is incompatible with imbalanced SAR target recognition. On the other hand, the predictions of these methods are unreliable for identifying high-value tail class SAR targets that are sensitive to faults. To address these issues, We propose a SAR long-tailed target recognition method based on evidence learning. The evidence learning head can output uncertainty as a measure of predictive confidence. On this basis, we demonstrate the use of uncertainty to further optimize the long-tailed SAR recognition performance and achieve optimal performance when the predicted distribution is unknown.

Compact Polarimetric (CP) SAR has a unique advantage in marine target observation, which can obtain rich polarization information and maintain a large observation width. However, due to the Doppler effect, moving ship targets are prone to ghost during the imaging process. Since CP data lose some scattering information, it is more difficult to distinguish the target from interference. This paper proposes a multi-feature collaboration and enhancement method for removing ghosts from the CP SAR ship target. The ship and ghost interference features are enhanced by multi-feature collaborations, then the contrast between ship and ghost interference is enhanced using an extremum separation feature descriptor, and finally, the ghost interference is removed using a signal-to-clutter ratio based Interference Feature Filter (IFF). Given limited CP data for ships with ghost phenomenon, the experiments simulate Circular Transmit and Linear Receive (CTLR) mode data using GF-3 fully polarimetric data, and the results show that this method can remove the ghost around the ship in the CP image better.

A ground-based simulation system is necessary to verify the feasibility of real spaceborne Synthetic Aperture Radar (SAR) systems. Since echo simulation and image generation are computationally complex, parallel acceleration for SAR systems has been an active research area. However, most of the acceleration algorithms utilize compute unified device architecture (CUDA) as the programming platform, that works only on NVIDIA’s GPUs. This paper proposes a parallel optimization algorithm for spaceborne SAR echo simulation and imaging in Open Computing Language (OpenCL). In the echo simulation module, we optimize the Fast Fourier Transform (FFT), the calculation of range echoes, and the synchronization operation. In the imaging module, the data is decomposed to reduce the computational burden, and matrix transpose and phase factor multiplication are optimized using OpenCL. The architecture and steps needed to extract parallelism in the implementation of the algorithm for accelerated SAR echo simulation and imaging are described in detail. Benefiting from the better generality of OpenCL, this algorithm can be used to program General-Purpose Graphics Processing Units (GPGPU), i.e., CPUs, GPUs, and other types of processors. The performance is promoted by orders of magnitude compared with CPU-based implementation.

Compact Polarimetric (CP) SAR has a larger imaging bandwidth compared to full-polarimetric SAR and more polarization information than dual-polarimetric SAR, which has significant potential in maritime ship detection. The non-uniform scale of ship targets and complex backgrounds present detection challenges. This paper proposes an improvement to YOLOv8 by employing deformable convolutions in the backbone network and adding attention mechanisms in the network neck. Deformable convolutions excel at extracting multi-scale features of ships with strong expressive capability, while attention mechanisms suppress the learning of background features. The paper utilizes a CP SAR dataset constructed by using high-information-content SPAN images and augments the dataset. In comparison with the experimental results of the standard YOLOv8 model, our method demonstrates an improvement of 3.2% in recall, 3.6% in precision, and 1.5% in mAP. The results indicate the effectiveness of our approach in the task of ship detection using CP SAR.

While deep learning-based methods have made significant strides in remote sensing applications, the scarcity and inadequate quality of remote sensing images tend to curtail the improvement of follow-up research such as remote sensing object detection. However, the human visual system is able to quickly grasp the features of an unseen object given only a few examples, which is considered to be related to a strong shape bias. Inspired by how human toddlers learn shapes and the process of recognizing objects by shape, this paper proposes a novel method known as Shape-Prior based Generated Content (SPGC) data augmentation to overcome these challenges. Specifically, our method includes two main steps: shape data generation and stylization. Initially, the method begins with generating shape data regardless of training data availability. Next, we enhance the robustness of the generated shape data through stylization, forming a robust shape dataset. Stylization is further bifurcated into two scenarios: when training data is unseen, self-stylization is employed where the shape data simultaneously serves as content and style data, resulting in significant performance improvements. When training data is accessible, data-specific stylization is applied, with the shape data as content and training data as style, leading to more substantial enhancements than self-stylization. Experimental results on mainstream remote sensing object detection datasets including NWPU VHR-10, DIOR, and FAIR1M demonstrate that our method significantly improves performance and underscores its effectiveness.

SAR ship detection is an important technology supporting water traffic monitoring and marine safety maintenance. In recent years, many methods based on deep neural networks have been used to improve the performance of SAR ship detection. These methods mainly focus on two issues: one is the false alarm of ship detection in complex inshore environments, and the other is the effective extraction and utilization of SAR ship features. The topic discussed in this paper is one of the culprits that has caused the aforementioned two problems, but has long been overlooked. Specifically, it pertains to the issue of intra-class imbalance in SAR ship detection. There are imbalances in the size distribution, azimuth distribution, and background distribution under the real data collection environment. However, since SAR ship detection is a single-class detection task, the aforementioned imbalances lack reliable descriptors during training. This paper proposes using evidence learning to obtain the epistemic uncertainty as a descriptor of biased learning on samples. Contrastive learning is used to further utilize the uncertainty label of samples to correct biased learning under intra-class imbalance. The proposed method is proven to be effective on multiple network models. AP50 reaches 94.8% on the HRSID dataset, 98.4% on SSDD dataset and 80.9% on the LS-SSDD dataset, both achieving SOTA performance.

Multi-temporal PolSAR data captures the temporal variations in polarization parameters, enabling more accurate land cover classification. Most existing multi-temporal PolSAR features rely on comparing only two-time points. These approaches can be limited in capturing cumulative changes over a longer period. To better represent the cumulative changes of land cover in the entire time-series, this paper proposes a multi-temporal PolSAR feature, namely time correlation entropy. We first extract the dominant scattering mechanism of targets from the polarization covariance matrices using matrix decomposition. Then the time correlation matrix is constructed by comparing all dominant scattering mechanism pairs in the time series. From the Shannon entropy, the entropy of the time correlation matrix, i.e., time correlation entropy, is derived to indicate the degree of changes in the land cover during the observation period. Finally, the maximum entropy principle is further applied to prove that this entropy conforms to a normal distribution. Following this corollary, a classification method based on the interval estimation of distribution parameters is proposed. We evaluate the proposed feature and classification on the salt lake classification application in Qarhan Salt Lake and Gasikule Salt Lake using Sentinel-1 images. Compared to common PolSAR features and classification methods, our method gains the best results. Besides, its results also have better regional consistency and noise resistance.

Traditional absolute radiometric calibration methods for synthetic aperture radar (SAR) face challenges in terms of flexibility, maintenance, and calibration frequency. In contrast, radiometric cross calibration can achieve rapid and timely calibration by utilizing the calibrated SAR satellites to illuminate the same ground targets. However, there are still two factors limiting the accuracy of cross calibration. First, two satellites used for cross calibration often have different incidence angles, whereas the existing methods for correcting incidence angle differences have poor performance in scene adaptation and overcorrection. Second, the stability of ground targets plays a critical role in effective cross calibration, but in practice, not all targets possess the same stability. To address the first issue, this article proposes a novel scene-driven incidence angle difference correction method. It leverages the historical information about the target scenes to determine the evaluation threshold for data blocks. Moreover, it incorporates the adaptive exponential cosine model to correct the scattering variations caused by the difference in incidence angle. To address the second issue, an uncertainty analysis method is employed to calculate the uncertainty of each data block. Then, these uncertainties are utilized to calculate weight coefficients, and the calibration constant is determined using a weighted least squares (WLS) model. Cross-calibration experimental results on the Sentinel-1A/B demonstrate an average reduction of 21.6% in the relative calibration error and 18.6% in the root-mean-square error (RMSE) compared with the traditional method, validating the effectiveness of the proposed method.

For the Synthetic Aperture Radar (SAR) change detection, the traditional pixel-wise methods have poor performance in terms of regional consistence and noise resistance, especially for the greatly heterogeneous aeras (e.g., built-up areas). In this article, a change detection method based on superpixel-wise likelihood-ratio tests are presented. Firstly, assuming that SAR pixel values follow a Gamma distribution, we derive the superpixel-wise probability density function of SAR images. Secondly, we conduct the likelihood-ratio test (LRT) to test the equity of two superpixels’ parameters. Thirdly, by observing the statistical properties of superpixel aeras, we simplify the calculation of LRT. Finally, we perform binarization on the LRT results according to Wilk’s Theorem to acquire the change maps. In the experiment, this method is successfully applied to flood extraction on Sentinel-1 data, showing its superiority over those traditional pixel-wise methods.

In recent years, it has been a research hotspot to apply big data-driven deep learning methods to Synthetic Aperture Radar (SAR) target recognition with limited data. However, the problem caused by the long-tailed characteristics of SAR data has long been ignored. Specifically, a majority of data samples are concentrated in a few categories, leading to a skewed distribution of data. This skewed distribution can cause learning bias towards the majority class, which can subsequently degrade the recognition performance of the minority class. This issue is further exacerbated in limited sample conditions for SAR target recognition. After conducting research on target recognition for long-tailed natural images, this study has found that the existing methods used in this field cannot be easily applied to SAR target recognition. The primary reason is that SAR image data exhibit simultaneous and complex inter-class and intra-class long-tailed distributions. In response to this issue, we proposes the use of a multi-branch expert network and dual-environment sampling to address the long-tail problems in both inter-class and intra-class scenarios. The proposed method outperforms popular long-tailed target recognition methods on the long-tailed versions of the MSTAR and FUSAR datasets.

In existing superpixel-wise saliency detection algorithms, superpixel generation often is an isolated preprocessing step. The performance of saliency maps is determined by the accuracy of superpixels to a certain extent. However, it is still a challenge to develop a stable superpixel generation method. In this article, we attempt to incorporate the superpixel generation and saliency calculation steps into an end-to-end trainable deep network. First, we employ a recently proposed differentiable superpixel generation method to over-segment the synthetic aperture radar (SAR) images, which outputs the possibility that the pixels assigned to neighbor superpixels (soft superpixel). In saliency calculation part, as one of our main contributions, we propose a differentiable and computationally simple saliency model, i.e., lacunarity cue. It is inspired by the fact that generally the backscattering intensity of regions of interest (ROIs) in SAR images irregularly fluctuates, while the areas with consistent pixels are often ignored as the clusters. We improve the pixelwise box differential dimension algorithm to measure the irregularity of scattering points in a superpixel. The superpixel generation and saliency calculation can be implemented under a unified deep network. Hence, the shapes of the superpixels can be iteratively adjusted according to the saliency maps until the ROIs are correctly detected. Experiments on real SAR images with different sizes and scenes show that the saliency maps can effectively highlight the target areas, thus outperforming the state-of-the-art saliency detection models.

Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods for ship detection have been developed; however, the detection of small ships presents unique challenges. SAR image characteristics, such as the sidelobe effect and blurred outline induced by the special imaging mechanism, as well as the small ship size, are the primary factors that lower the detection accuracy. This paper provides a sidelobe-aware small ship detection network for synthetic aperture radar imagery. First, considering the sidelobe effect and blurred outline, dual-pooling, i.e., average pooling and max pooling, was utilized to build a feature extraction module that lowered the effects of strong scattering points outside of the ship body and enhanced the ship body information. Second, as the bipartition process of the average pooling and maximum pooling caused some loss of original data information, different feature maps in the network were concatenated to construct a new network structure to compensate for the information lost and enrich the small ship features. Third, because the traditional loss function based on centroid distance and aspect ratio may result in the same loss function value for different prediction box sizes, a novel loss function based on the dual Euclidean distances of the corner point coordinates between the prediction box and the real box was proposed, which could accurately describe various overlapping box situations. Experiments using the Large-Scale SAR Ship Detection Dataset (LS-SSDD), SAR Ship Detection Dataset (SSDD), and AIR-SARShip dataset validated the efficacy and state-of-the-art performance.

Crop classification is one of the most important applications of polarimetric SAR images. Time-series polarimetric SAR images have the characteristics of reflecting the changes of various scattering characteristics of crops in different growth periods. However, since time-series polarimetric SAR needs to combine multiple single polarimetric SAR images, the redundancy between features is multiplied. In this paper, aiming at the problem of feature redundancy, the method of similarity measurement is used to select features from two dimensions of space and time respectively to reduce feature redundancy. Since the sample size of SAR feature images applied in supervised classification is small, it's not suitable for multiple downsampling in CNN, and a suitable classifier based on Transformer is designed. Preliminary experiments on the full polarimetric data verified the effectiveness of the proposed method.

Rapid and accurate extraction of water body information is fundamental to disaster assessment. However, in SAR images, shadows often appear similar to the water regions, so the commonly threshold-based water detection methods easily confuse them. The purpose of this paper is to use Sentinel-1 polarimetric SAR data to find a simpler and more effective method to identify shadow areas out of water bodies. Considering a strong dependence on intensity of Wishart classifier, the weak backscattered regions on the SAR images will be categorized into the same class. We firstly use$H/\overline{\alpha}$Wishart to roughly classify the PolSAR data to obtain a relatively complete experimental study area. Then, we further discriminate the shadow regions from the study area based on the difference of the entropy between the water body regions and the surrounding environment. The experimental results based on Sentinel-1 measured data prove the effectiveness of this method.

As flood occurs unpredictably, there is not enough time to label the data in practice. The use of clustering inside flood detection deep networks can reduce their demand for labeled data. However, existing clustering algorithms aim at assigning a unique cluster for each pixel. This leads to the fact that clustering process is non-differentiable to the inputs, hindering their incorporation into deep networks. In this study, we introduce a new assignment strategy for single-polarization SAR images to make the clustering differentiable, named “soft association.” Here, each pixel is assigned to various clusters with different probabilities. The greater the probability value, the more likely the pixel will be finally assigned to the cluster. Based on this, an end-to-end trainable semi-supervised clustering network for SAR flood detection is established. Compared with the existing state-of-the-art semi-supervised methods, it can achieve similar performance with fewer labeled samples.

In existing superpixel-wise segmentation algorithms, superpixel generation most often is an isolated preprocessing step. The segmentation performance is determined to a certain extent by the accuracy of superpixels. However, it is still a challenge to develop a stable superpixel generation method. In this article, we attempt to incorporate the superpixel generation and merging steps into an end-to-end trainable deep network. First, we employ a recently proposed differentiable superpixel generation method to over-segment the single-polarization synthetic aperture radar (SAR) image. It outputs the statistical likelihood that each pixel belongs to different superpixels. In superpixel merging part, as one of our main contributions, we propose a superpixel-wise statistical dissimilarity measure method for converting the soft superpixels set into a self-connected weighted graph. More importantly, inspired by the concept of the number of walks in graph theory, we define the$k$-order connectivity of each vertex. This definition can intelligently indicate the potential soft cluster centers and class assignments in graph. This merging method is differentiable, computationally simple, and free of empirical parameters. The superpixel generation and merging phases can be implemented under a unified deep network. The benefit is that our method can iteratively adjust the shapes of the superpixels according to the boundaries and segmentation results during training, until the satisfactory segmentation results are captured. Experimental results on real SAR images demonstrate that the segmentation precision of our proposed method is superior to other state-of-the-art methods in terms of precision and computational efficiency.

Since the number of superpixels is lower than that of pixels, superpixels can substantially speed up subsequent processing steps and have been widely used in synthetic aperture radar (SAR) image segmentation. However, in most of the existing superpixel-wise segmentation algorithms, superpixel prediction is an isolated preprocessing step and is independent of the segmentation task. The performance of the segmentation results is determined by the accuracy of superpixels. Once superpixels are generated, their shape cannot be changed in the following segmentation stage, even if the same superpixels contain pixels of different landcovers. To address this, we propose an end-to-end trainable superpixel-wise segmentation method for single-polarization SAR images. First, we design a differentiable boundary-ware clustering method for estimating task-specific superpixels. Instead of the hard association between pixels and superpixels in the existing superpixel algorithms, this method introduces the soft association map to make the clustering differentiable. Hence, it can be implemented using a simple deep fully convolutional network. In the segmentation part, we propose a novel soft graph convolution network (Soft-GCN), which takes the association map as input and performs superpixel-wise segmentation. The advantage of our method is that superpixel generation and graph convolution parts can be trained under a unified framework, until two parts obtain the optimum parameters. In the training process, it can adaptively adjust the shape of the superpixels according to the segmentation results, ensuring the superpixels correctly adhere the boundaries. Experimental results with simulated and real SAR images demonstrate that our method outperforms other state-of-the-art segmentation algorithms, while also being faster.

Currently, deep neural networks have been widely used in the field of SAR target recognition. Many researchers found that deep neural networks have an ability of denoising. In many cases, there is no need to denoise in pre-process. But the denoising ability of deep neural networks can take place of conventional denoising algorithm or not is doubtful. In this article, we explore the effect of image denoising algorithms to SAR target recognition methods based on deep neural networks. Firstly, seven traditional denoising algorithms are selected to process two SAR datasets. And these data are utilized to train two kinds of deep neural networks. After comparing and analyzing the training processes and results, we find that 1) The effect of denoising algorithms is influenced by architectures of neural networks and quality of datasets. It is difficult to find a SAR image denoising algorithm, which can improve the accuracy of any recognition network. Sometimes they even drag down the performance of recognition networks. 2) The deep networks with more layers will have better denoising ability, so the effect of denoising algorithms will decrease. For ResNet, there is no need to add the denoising processing.

Small vessels in synthetic aperture radar (SAR) images usually have weak scattering intensity and occupy only a few numbers of image pixels, resulting in a high miss detection rate during the detection process. Regarding the problem, two solutions were presented in this paper. Firstly, dual-polarimetric SAR data were used and dual-polarimetric features were adaptively fused. Comparing to single-polarization and conventional non-adaptive fusion method, it optimally enhanced the characteristics of small vessels. Secondly, the conventional feature pyramid network (FPN) was enhanced by reducing the downsampling factor, adding spatial attention, and channel attention. The added spatial attention enhanced the significant features of small vessels on the large-scale feature map; the added channel attention filtered out the spliced features maps that were benefiting small vessel detection and reduced feature redundancy. Experimental results on the small vessel data set of Sentinel-1 verified that it not only reduced the miss detection rate but also improved calculation efficiency.

Many deep learning algorithms have been successful applied for synthetic aperture radar automatic target recognition (SAR-ATR), but high recognition accuracy usually relies on large amount of labeled training data. In addition, SAR is active imaging sensor and target characteristics are quite different with varying look angles, which also reduces recognition accuracy. Multi-task learning can improve the performance of main task by learning and sharing useful information from auxiliary tasks. Based on multi-task learning, this paper fully exploits the potential of available SAR data for target classification. Two auxiliary tasks, separating target from shadow and estimating target aspect angle, are designed to obtain auxiliary information and improve the classification accuracy. The MSTAR data set proves the effectiveness of the method, and the results show that the method has good recognition accuracy.

Simultaneous multitask processing is a common requirement in synthetic aperture radar (SAR) automatic target recognition (ATR), e.g., not only the category of the target but also the aspect angle of the target need to be identified at the same time. Moreover, the target recognition network is always expected to have the capability of incremental learning, i.e., acquire the processing capabilities for new tasks while maintaining the processing capabilities for old tasks. In this paper, an incremental multitask learning method based on structured pruning is proposed. The structured pruning, originally proposed for network compression, is used to learn with dominant neuron and release parameter space of convolutional neural network for new tasks. Through iterative pruning and training of new tasks, multitask target recognition is realized in a single convolutional neural network and could simultaneously output recognition results of multiple tasks. The experiments on the MSTAR dataset show that our method can simultaneously recognize the category and aspect angle of target, while does not decrease the corresponding accuracy compared to single-task processing.

It is very difficult for the millimeter wave (MMW) seeker to detect and capture the target. Tracking the target unstably, even losing the target happens frequently. Focusing on the problem, a simple but effective target recapture method is presented for narrow-beam MMW seeker in this paper. The parameters outputted by the inertial navigation system (INS) and the seeker are utilized to deduce the coordinates of the target. And then, target searching is implemented again on the basis of the deduced coordinates. The target recapture time can be dramatically reduced by using the proposed method, thus guaranteeing enough terminal guidance time. The effectiveness of the proposed method is verified by the mooring test-fly experiments.