SAR Target Classification Using the Multikernel-Size Feature Fusion-Based Convolutional Neural Network

Ai, Jiaqiu; Mao, Yuxiang; Luo, Qiwu; Jia, Lu; Xing, Mengdao

doi:10.1109/tgrs.2021.3106915

Cited by 59 publications

(32 citation statements)

References 49 publications

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“…Table V and Fig. 9 also reveals that in terms of fusion featurebased methods, compared to [47] and [55], [60] and the proposed framework have higher accuracy and AUC values, which can be attributed to the latter's utilization of complementary advantages of handcrafted features and deep features [60], while the former only fuses handcrafted features or deep features. The proposed feature fusion framework is slightly better than [60], which is due to the more effective use of the phase information of the SAR data through the monogenic signal and CVNLNet.…”

Section: ) Recognition Based On the Feature Fusion Frameworkmentioning

confidence: 90%

“…They include handcrafted featurebased methods, such as moment method [21], attributed scattering center (ASC) model [22] and Joint Sparse Representation (JSR) of monogenic components [30], as well as end-to-end neural networks, such as A-ConvNet [31], CV-CNN [39], CV-FCNN [43] and RVNLNet with the same architecture as CVNLNet. Furthermore, a fusion framework based on multiple handcrafted features [47], MKSFF-CNN based on fusion of multi-scale deep features [55] and FEC based on fusion of handcrafted features and deep features [60], are also used for comparison. Table V lists the classification accuracy of different methods.…”

Section: ) Recognition Based On the Feature Fusion Frameworkmentioning

confidence: 99%

“…ROC curve is an important evaluation metric in machine learning, which describes the relationship between true positive rate (TPR) and false positive rate (FPR). AUC is usually used to assist ROC to further evaluate the performance (generally say, the larger the AUC, the better the performance of the classification method) [55]. Similar to (28), the specific definitions of TPR and FPR are as follows:…”

Section: ) Recognition Based On the Feature Fusion Frameworkmentioning

confidence: 99%

“…In order to make up for the deficiencies of a single feature under diverse and complex conditions, researchers have found that a reasonable combination of different types of features can greatly enhance the performance of image processing [45]. The fusion strategies based on multiple handcrafted features [46]- [51] or multi-scale deep features [52]- [55] have been confirmed to be effective. Furthermore, some fusion strategies based on the handcrafted features and deep features, which have been indicated to have certain complementary properties, have also emerged [56][57][58].…”

mentioning

confidence: 99%

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POLSAR Target Recognition Using a Feature Fusion Framework Based on Monogenic Signal and Complex-Valued Nonlocal Network

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the continuous development of synthetic aperture radar (SAR) systems, multi-polarization information has been increasingly applied to numerous fields, and automatic target recognition (ATR) in polarimetric SAR (POLSAR) has been recognized as vital problem. The SAR recognition methods can primarily fall into handcrafted feature-based algorithms and deep learning algorithms. The former exhibits excellent interpretability but insufficient generalization; the latter achieves stronger representational ability but relies on a considerable number of samples. To solve above problems, a feature fusion framework is proposed in this paper based on monogenic signal and complex-valued non-local network (CVNLNet) for POLSAR target recognition. The proposed feature fusion framework effectively uses the complementarity of handcrafted features and deep features, while making up for the disadvantages of single feature-based methods. First, a Mono-BOVW model is proposed based on monogenic signal and bag-of-visual-words (BOVW) model to extract handcrafted features, which can more fully mine the information covered in POL-SAR data in multi-scale space. Moreover, CVNLNet is built for deep feature extraction to use both the amplitude and phase covered in POLSAR data. Next, a kernel discrimination correlation analysis algorithm (KDCA) is proposed to jointly analyze and transform the two features, so as to remove redundant information while retaining effective and discriminative information. Experiments on the MSTAR dataset and the GOTCHA dataset show that the proposed framework has superior performance on single polarimetric and fully polarimetric datasets.

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Section: ) Recognition Based On the Feature Fusion Frameworkmentioning

confidence: 90%

Section: ) Recognition Based On the Feature Fusion Frameworkmentioning

confidence: 99%

Section: ) Recognition Based On the Feature Fusion Frameworkmentioning

confidence: 99%

mentioning

confidence: 99%

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POLSAR Target Recognition Using a Feature Fusion Framework Based on Monogenic Signal and Complex-Valued Nonlocal Network

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In addition, we also compared the Receiver Operation Characteristics (ROC) of different models. ROC is an important metric that can be used to evaluate the detection effect under the same false positive [59,60]. ROC The higher the true positive rate (TPR), the better the detection effect.…”

Section: 60mentioning

confidence: 99%

A High-Precision Recognition Method of Circular Marks Based on CMNet Within Complex Scenes

Hong

Tong

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate recognition of circular marks is crucial for calibration, object tracking, and threedimensional reconstruction in videogrammetry. However, most existing studies were designed under single or relatively simple scenes. When the existing algorithms are applied to more complex scenarios, it will result in higher false detection and miss-detection rate. In this article, we present a high-precision recognition method based on a novel deep learning model, Circular-MarkNet (CMNet) to solve this problem. The proposed network consists of three main steps: first, circular marks are detected using the improved YOLOv4 model to narrow the search region of the circular contour; the contour of the circular marks is then extracted based on the saliency object detection model BASNet; and finally, least square fitting (LSF) is used to calculate the central pixel coordinate of the identified contour on the saliency map. The proposed method was tested under three complex scenarios with different characteristics and disturbances. The experimental results demonstrated that: (1) the proposed CMNet can effectively recognize of circular marks within complex scenes, which reveals the superiority and generalization ability of the proposed method; (2) the improved YOLOv4 can significantly enhance the detection accuracy of circular marks, which is crucial to the subsequent saliency courter detection and circle center identification; (3) Circular-MarkNet achieved the best performance, with an RMSE of 0.0713 pixel, compared to the state-of-the-art methods.

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ECG_SegNet: An ECG delineation model based on the encoder-decoder structure

Liang

Liu

et al. 2022

Computers in Biology and Medicine

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SAR Target Classification Using the Multikernel-Size Feature Fusion-Based Convolutional Neural Network

Cited by 59 publications

References 49 publications

POLSAR Target Recognition Using a Feature Fusion Framework Based on Monogenic Signal and Complex-Valued Nonlocal Network

POLSAR Target Recognition Using a Feature Fusion Framework Based on Monogenic Signal and Complex-Valued Nonlocal Network

A High-Precision Recognition Method of Circular Marks Based on CMNet Within Complex Scenes

ECG_SegNet: An ECG delineation model based on the encoder-decoder structure

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