Target Classification Method Research Articles

Efficient Target Classification Based on Vehicle Volume Estimation in High-Resolution Radar Systems

In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar sensor can estimate the distance and velocity of a target. In addition, the azimuth and elevation angle of the target can be estimated by using a multiple-input and multiple-output antenna system. Using the estimated distance, velocity, and angle, the 3D point cloud of target can be generated. From the generated point cloud, we extract the point cloud for each individual target using the density-based spatial clustering of application with noise method and a camera mounted on the radar sensor. Then, we define the convex hull boundaries that enclose these point clouds in both 3D and 2D spaces obtained by orthogonally projecting onto the xy, yz, and zx planes. Using the vertices of convex hull, we calculate the volume of the targets and the areas in 2D spaces. Several feature points, including the calculated spatial information, are numerized and configured into feature vectors. We design an uncomplicated deep neural network classifier based on minimal input information to achieve fast and efficient classification performance. As a result, the proposed method achieved an average accuracy of 97.1%, and the time required for training was reduced compared to the method using only point cloud data and the convolutional neural network-based method.

Frequency analysis method for SAR target classification using convolutional neural networks

Frequency analysis method for SAR target classification using convolutional neural networks

A polarization HRRP target classification method based on one-dimensional convolutional attention neural network

A polarization HRRP target classification method based on one-dimensional convolutional attention neural network

Aerial target classification method based on clutter suppression and fully connected neural network

Aerial target classification method based on clutter suppression and fully connected neural network

ASC-SMCNN: SAR target classification method guided by attribute scattering center and separability measures

ASC-SMCNN: SAR target classification method guided by attribute scattering center and separability measures

A novel classification method for GPR B-scan images based on weak-shot learning

Ground Penetrating Radar (GPR) is extensively employed for underground target detection, and the advancements in this domain have been hastened by the utilization of deep learning methodologies. Despite advancements in deep learning, practical engineering applications often face challenges in meeting the demanding requirements of large-scale data and high data quality. In this study, based on cross-domain transfer learning, we proposed a method for underground target classification of weak labeled GPR data. It combined strongly annotated source domain data with weakly annotated target domain data through adversarial learning to leverage the prior knowledge from existing large datasets and addressed the classification problem of small-sample GPR data. By employing adversarial learning to integrate strongly labeled source domain data with weakly labeled target domain data, we leverage prior knowledge from existing large datasets, thereby enhancing classification accuracy on a small GPR dataset. This approach reduces the requirements for annotation quality and sample quantity in the GPR B-scan dataset of underground targets. Additionally, it exhibits robustness to label noise, making it applicable to real-world GPR data classification tasks. Compared to some well-known networks, the proposed model achieved the best classification performance in multiclass classification problems, with an accuracy of 87.62% and an F1macro score of 87.60% on the test set. These results represent an improvement of 17.14 and 17.20 percentage points, respectively, over the best-performing classical classification networks. Additionally, the proposed model's reliability in cross-domain transfer learning, even with limited data, was substantiated through a comparison with other few-shot learning networks.

An improved target classification method for 4D radar sensor using RAM

An improved target classification method for 4D radar sensor using RAM

A Target Recognition Method of Small Sample Based on RCS Data

Abstract During the training of target recognition models based on Radar Cross Section (RCS) data, a persistent challenge arises in sampling due to the inherent difficulty in acquiring a sufficient number of samples. This scarcity of data poses a significant impediment to the effective training of models, resulting in diminished accuracy in target recognition. To address this issue, this article proposes a target classification method based on RCS data under small sample conditions. The approach adopts the fundamental concept of Model-Agnostic Meta-Learning (MAML) to train the target recognition model, enhancing the structure of MAML model. An hourglass-shaped convolution layer is introduced to the input layer, with an additional convolution layer preceding the output layer, and a switch to a central loss function. To substantiate the efficacy of the improved MAML model, comprehensive comparative analyses are conducted with benchmark models, including MAML, ResNet 18-layers, Long Short-Term Memory (LSTM), among others. Experimental results conclusively demonstrate the superior performance of the refined MAML model in target recognition under conditions of limited samples, attaining an average prediction accuracy of 85.62%. This signifies a noteworthy 5-percentage-point improvement compared to the baseline model prior to the introduced enhancements.

Unsupervised domain adaptation for SAR target classification based on domain- and class-level alignment: From simulated to real data

Synthetic Aperture Radar (SAR) target classification methods based on convolutional neural networks have attracted wide attention recently. Typically, these methods necessitate a substantial quantity of labeled data to train the deep model, which is an obstacle for SAR target classification since annotating SAR images is costly and time-consuming. Generating large-scale labeled data in simulation to train the deep model is a feasible alternative. However, cross-domain recognition is challenging due to significant differences in distribution between simulated and real domains. In this paper, we propose an unsupervised domain adaptation method for SAR target classification by aligning the distribution between the simulated and unlabeled real domains. Specifically, we encode the dependencies across different granularity perspectives including domain-, and class-levels simultaneously to align two domains finely. On the one hand, a gradient-weighted adversarial alignment method is proposed to align the global distributions at the domain-level. On the other hand, a novel class-level alignment method based on the prototypical network is proposed to align the finer class structure. Technically, we respectively model the cross-domain prototype-prototype relations and cross-domain prototype-instance relations with contrastive learning to achieve intra-class prototype compact as well as inter-class prototype separation among cross-domain categories, where the prototypes of the real domain are learned with pseudo labels. To improve the quality of pseudo-labels, a pseudo-label filtering strategy combined with the attribute scattering center is further designed. The experiments on two simulation-to-reality datasets indicate that the proposed method outperforms state-of-the-art unsupervised domain adaptive methods, such as pixelfeat (+4.45% and +7.6% in accuracy on SAMPLE and S2M datasets respectively). Code and data will be available in https://github.com/YuShi1213/Sim2Real-Unsupervised-SAR-Target-Classification.

A Grid-Based Gradient Descent Extended Target Clustering Method and Ship Target Inverse Synthetic Aperture Radar Imaging for UHF Radar

Inland shipping is of great significance in economic development, and ship surveillance and classification are of great importance for ship management and dispatch. For river ship detection, ultrahigh-frequency (UHF) radar is an effective equipment owing to its wide coverage and easy deployment. The extension in range, Doppler, and azimuth and target recognition are two main problems in UHF ship detection. Clustering is a necessary step to get the center of an extended target. However, it is difficult to distinguish between different target echoes when they overlap each other in range, Doppler, and azimuth and so far practical methods for extended target recognition with UHF radar have been rarely discussed. In this study, a two-stage target classification method is proposed for UHF radar ship detection. In the first stage, grid-based gradient descent (GBGD) clustering is proposed to distinguish targets with three-dimensional (3D) information. Then in the second stage, the inverse synthetic aperture radar (ISAR) imaging algorithm is employed to differentiate ships of different types. The simulation results show that the proposed method achieves a 20% higher clustering accuracy than other methods when the targets have close 3D information. The feasibility of ISAR imaging for target classification using UHF radar is also validated via simulation. Some experimental results are also given to show the effectiveness of the proposed method.

A novel noise-robustness and rotation-invariant LADAR point cloud target classification method

Identifying targets of LAser Detection And Ranging (LADAR) point clouds that are affected by noise and different poses throws a significant challenge. This paper proposes a novel LADAR point cloud target classification method that is both noise-robust and rotation-invariant. Specifically, the proposed method transforms the point cloud target into a slice image that is resilient to noise and holds rotation-invariance. Next, a designed 2D Convolutional Neural Network (CNN) is utilized to classify the corresponding point cloud target based on the slice image. To overcome the challenge of lacking prior knowledge about the discriminative features of the slice image, the 2D CNN is designed with the Local Importance-based Pooling (LIP) layer. This layer extracts the discriminative feature in a data-driven manner, thereby improving the accuracy of the classification process. The proposed method is evaluated through multiple experiments on the public ModelNet40 dataset. The experimental results demonstrate that the designed LIP-CNN can better learn the discriminative features of the slice image, achieving high classification accuracy. Moreover, the proposed slice-image-based method is capable of accurately classifying the target, even in the presence of noise and different poses.

Ship Classification Based on Density Features in SAR Images

Ship classification in SAR images has attracted much attention by researchers. In this paper, a SAR target classification method for three commercial ships (container ships, bulk carriers and oil tanker) is proposed by analyzing their scattering features. Firstly, the ship slice is preprocessed to obtain the binary image, from which the density features can be extracted, which describing the ship scattering point distribution. Finally, the support vector machine (SVM) classifier is applied to classify these three types of commercial ships. The experimental results show that the classification accuracy of structure feature and strength feature is low, while the proposed density feature can reach 80% for three types of ships. The combination of structure features and strength features with density features can improve the classification accuracy. Combining the three features has the best classification performance.

Research on Metal Target Classification Method Based on Multi-Frequency Electromagnetic Induction

The traditional single-frequency metal mine detector is limited by the single-frequency component, which is difficult to meet the requirements of high-accuracy detection of multiple types of mine targets. The modulation square wave is used to transmit the multi-frequency signal based on the multi-frequency electromagnetic induction technology. The phase-locked amplifier is used to extract the signal feature of each frequency component. The naive bayesian algorithm is used to classify metal targets according to the extracted feature vectors. This method can effectively improve target classification accuracy compared with single-frequency metal mine detectors.

A multi-order Fractional Fourier domain feature union method for active sonar target classification

Different order domains in Fractional Fourier Transform (FrFT) reflect the features of different time–frequency planes. They contain multi-domain information about the target. The optimal order domain feature is used widely because of its anti-reverberation interference performance, but other order domains also have anti-reverberation ability, and even have more separable performance. It is of great significance to use effectively the multi-order domain feature for target classification and recognition. Therefore, a multi-order Fractional Fourier domain feature union method based on the information entropy is proposed in this paper. This method aims to find more distinguishable and separable target features than that of the optimal order domain by calculating the information entropies of the amplitude features and union them by determining the weights according to the value of the information entropies. The performance of the presented method is verified using the field data of four types of similar targets’ echo signals when the signal-to-reverberation ratios (SRRs) are respectively 4 dB, 0 dB, −3 dB, and −6 dB. Experimental results show that the method proposed in this paper can reflect the overall characteristics of the signals and has a more distinguishable ability and a good anti-reverberation ability.

Contrastive Domain Adaptation-Based Sparse SAR Target Classification under Few-Shot Cases

Due to the imaging mechanism of synthetic aperture radar (SAR), it is difficult and costly to acquire abundant labeled SAR images. Moreover, a typical matched filtering (MF) based image faces the problems of serious noise, sidelobes, and clutters, which will bring down the accuracy of SAR target classification. Different from the MF-based result, a sparse image shows better quality with less noise and higher image signal-to-noise ratio (SNR). Therefore, theoretically using it for target classification will achieve better performance. In this paper, a novel contrastive domain adaptation (CDA) based sparse SAR target classification method is proposed to solve the problem of insufficient samples. In the proposed method, we firstly construct a sparse SAR image dataset by using the complex image based iterative soft thresholding (BiIST) algorithm. Then, the simulated and real SAR datasets are simultaneously sent into an unsupervised domain adaptation framework to reduce the distribution difference and obtain the reconstructed simulated SAR images for subsequent target classification. Finally, the reconstructed simulated images are manually labeled and fed into a shallow convolutional neural network (CNN) for target classification along with a small number of real sparse SAR images. Since the current definition of the number of small samples is still vague and inconsistent, this paper defines few-shot as less than 20 per class. Experimental results based on MSTAR under standard operating conditions (SOC) and extended operating conditions (EOC) show that the reconstructed simulated SAR dataset makes up for the insufficient information from limited real data. Compared with other typical deep learning methods based on limited samples, our method is able to achieve higher accuracy especially under the conditions of few shots.

Open Set Recognition With Incremental Learning for SAR Target Classification

SAR target classification is an important application in SAR image interpretation. In practical applications, the battlefield is open and dynamic, and the SAR target classification model often encounters the targets of unknown classes. However, most of the existing SAR target classification methods follow the close-set assumption. It makes them only classify several fixed classes of targets and can’t deal with the targets from unknown classes. To this end, this paper proposes a novel SAR target classification method. This method can not only classify the targets from known classes and search targets from unknown classes but also incrementally update the classification model with these unknown class targets. Specifically, an autoencoder improved by MS-SSIM (multi-scale structural similarity) loss is utilized to extract targets’ features, and it can better utilize the structural information in SAR images. Next, the classifier based on EVT (Extreme Value Theorem) is established, which can classify the known class targets and search the unknown class targets. Then, we perform improved model reduction on the established classifier. This operation could speed up the model and prepare for incremental learning. Finally, after manually labeling those unknown class targets, the classifier is updated with these data in incremental form. Experimental results on the MSTAR (Moving and Stationary Target Automatic Recognition) dataset indicate that, compared with the state-of-the-art methods, our proposed method has better performance in open set recognition and incremental learning.

HENC: Hierarchical Embedding Network With Center Calibration for Few-Shot Fine-Grained SAR Target Classification

Restricted by observation conditions, some scarce targets in the synthetic aperture radar (SAR) image only have a few samples, making effective classification a challenging task. Although few-shot SAR target classification methods originated from meta-learning have made great breakthroughs recently, they only focus on object-level (global) feature extraction while ignoring part-level (local) features, resulting in degraded performance in fine-grained classification. To tackle this issue, a novel few-shot fine-grained classification framework, dubbed as HENC, is proposed in this article. In HENC, the hierarchical embedding network (HEN) is designed for the extraction of multi-scale features from both object-level and part-level. In addition, scale-channels are constructed to realize joint inference of multi-scale features. Moreover, it is observed that the existing meta-learning-based method only implicitly utilize the information of multiple base categories to construct the feature space of novel categories, resulting in scattered feature distribution and large deviation during novel center estimation. In view of this, the center calibration algorithm is proposed to explore the center information of base categories and explicitly calibrate the novel centers by dragging them closer to the real ones. Experimental results on two open benchmark datasets demonstrate that the HENC significantly improves the classification accuracy for SAR targets.

Space Target Classification With Corrupted HRRP Sequences Based on Temporal–Spatial Feature Aggregation Network

High-resolution range profile (HRRP) sequences have great potential for space target classification because they can provide both scattering information and micromotion information. However, many factors cause an obtained HRRP sequence for a space target to be corrupted in real cases due to noise interference, limited radar resources, and the requirement of multitarget observations. Many space target classification methods cease to be effective when HRRP sequences are corrupted, so classifying space targets with corrupted HRRP sequences is still a challenging problem. To solve this problem, a novel space target classification method based on a temporal–spatial feature aggregation network (TSFA-Net) is proposed by using the corrupted HRRP sequences directly. First, a sequence-to-token module (S2T-module) is designed to extract low-level and fine-grained features from the raw inputs. Second, to effectively model the long-range dependencies among corrupted HRRP sequences and capture global representations without losing target local features, we propose a parallel and dual-branch block, i.e., a temporal–spatial feature aggregation block (TSFA-block), by combining a Transformer network and a convolutional neural network (CNN). Then, via progressively hierarchically stacking TSFA-blocks, a hierarchical temporal–spatial feature aggregation subnetwork (H-TSFA-subnetwork) is constructed to obtain the final temporal–spatial features. Finally, a token-to-label module (T2L-module) is adopted to obtain the classification results. Extensive experiments demonstrate that the proposed method achieves state-of-the-art classification accuracy for space target classification with HRRP sequences, especially under the conditions of a low signal-to-noise ratio and a high missing rate.

Exploration of Vehicle Target Detection and Classification Method Based on Sea Lion Optimization with Deep Convolutional Neural Network

Abstract Presently, urban environments over the globe are highly employed to obtain solutions for enhancing the quality of the livers and enhance the usage of city infrastructure and resources with minimal operational cost. Urban remote sensing acts as a significant part in the ability of mapping, monitoring, and controlling infrastructure. High-resolution remote sensing data renders worldwide images faster than traditional data collection strategies. Hence, small objects such as cars are easily detected. Vehicle recognition on aerial remote sensing images (RSIs) in the complicated background of urban zones has always gained a lot of interest in the remote sensing field. The automatic vehicles enumeration research domain had a significant contribution in several applications, including traffic management and monitoring. Target detection technology will be a crucial part of computer vision (CV) technology, and target detection techniques were enforced in several domains. Therefore, this study develops a new Vehicle Recognition and Classification using Sea Lion Optimization with Deep Learning (VRC-SLODL) model on RSI. In the presented VRC-SLODL technique, the major intention lies in recognising and classifying vehicles present in the images. The bilateral filtering (BF) technique can initially improve the RSI quality. The VRC-SLODL technique employs a modified residual network (ResNet) model to produce a collection of feature vectors. Eventually, the SLO approach with long short-term memory (LSTM) technique was exploited for vehicle classification, where the SLO algorithm acts as a hyperparameter optimizer. The experiments were performed on a benchmark dataset to examine the better performance of the VRC-SLODL method. The obtained values reported the improved classification performance of the VRC-SLODL technique over other models.

Classification of Radar Targets with Micro-Motion Based on RCS Sequences Encoding and Convolutional Neural Network

Radar cross section (RCS) sequences, an easy-to-obtain target feature with small data volume, play a significant role in radar target classification. However, radar target classification based on RCS sequences has the shortcomings of limited information and low recognition accuracy. In order to overcome the shortcomings of RCS-based methods, this paper proposes a spatial micro-motion target classification method based on RCS sequences encoding and convolutional neural network (CNN). First, we establish the micro-motion models of spatial targets, including precession, swing and rolling. Second, we introduce three approaches for encoding RCS sequences as images. These three types of images are Gramian angular field (GAF), Markov transition field (MTF) and recurrence plot (RP). Third, a multi-scale CNN is developed to classify those RCS feature maps. Finally, the experimental results demonstrate that RP is best at reflecting the characteristics of the target among those three encoding methods. Moreover, the proposed network outperforms other existing networks with the highest classification accuracy.