Deep feature extraction and combination for synthetic aperture radar target classification

Amrani, Moussa; Jiang, Feng

doi:10.1117/1.jrs.11.042616

Cited by 68 publications

(21 citation statements)

References 14 publications

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“…e second one used visual saliency model for feature extraction, denoted as VSM method. e third and fourth methods are CNN-based ones, using the residual networks (Res-Net) [42] and deep feature [46], respectively. e last two are developed based on the multitask sparse representations to classify the multiple features (extracted by PCA, kernel PCA, and NMF) [55] and multiresolution representations [56].…”

Section: Preparationmentioning

confidence: 99%

“…A large number of classifiers have been used and verified in SAR target recognition, including support vector machines (SVM) [32,33] and sparse representation-based classification (SRC) [34][35][36]. In recent years, with the maturity of deep learning theory and algorithms [37][38][39], a large number of SAR target recognition methods were developed based on deep leaning models, among which the most representative one was the convolutional neural network (CNN) [40][41][42][43][44][45][46]. e results of feature extraction, as the input of the classifier, largely determine the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Target Recognition of SAR Images Based on Complex Bidimensional Empirical Mode Decomposition

Miao

Liu

2021

Scientific Programming

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A target recognition method for synthetic aperture radar (SAR) image based on complex bidimensional empirical mode decomposition (C-BEMD) is proposed. C-BEMD is used to decompose the original SAR image to obtain multilevel complex bidimensional intrinsic mode functions (BIMF), which reflect the two-dimensional time-frequency characteristics of the target. In the classification stage, the decomposed multilevel BIMFs are represented using the multitask sparse representation. Finally, the target category of the test sample is determined according to the reconstruction errors related to different training classes. In the experiment, the standard operating condition (SOC) and extended operating conditions (EOC) are designed based on the MSTAR dataset to test and verify the proposed method. The results confirm the effectiveness and robustness of the method.

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Section: Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Target Recognition of SAR Images Based on Complex Bidimensional Empirical Mode Decomposition

Miao

Liu

2021

Scientific Programming

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“…In recent years, the research on target recognition algorithms for remote sensing images has mainly been aimed at roads [10], building clusters [11], aircraft [12], large bridges [13], highways [14], oil tanks [15] and other targets that are closely related to human or military activities. Synthesizing various processing methods developed for the recognition of targets of interest in traditional remote sensing images in recent years [32][33][34], we find that they can be roughly divided into two cases: feature-based and model-based methods.…”

Section: Introductionmentioning

confidence: 99%

The Recognition Framework of Deep Kernel Learning for Enclosed Remote Sensing Objects

et al. 2021

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Remote sensing image target recognition is used in various fields, such as ships, tanks, airplanes, and vehicles, which are closed targets. The features of these targets include target outlines that are obvious and target discriminant features that are significantly different from the surrounding environment, and the targets are characterized as small and dense. Therefore, the recognition of these types of targets is a popular topic. We proposed a recognition framework consisting of a remote sensing image target recognition method based on deep saliency kernel learning analysis, which uses a target region extraction method based on the visual saliency mechanism and implements a nonlinear deep kernel learning saliency feature analysis method to realize target extraction and recognition. Experimental results show that a 95.9% recognition rate is achieved for SAR remote sensing target recognition on the public MSTAR data set, a 96% recognition rate on the UC Merced Land Use data set, and an 85% recognition rate on a selfbuilt visible light remote sensing image data set. The recognition framework can be used for video recognition.INDEX TERMS Saliency analysis, deep kernel learning, remote sensing target recognition.

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“…CNN simulates the visual cortex using convolution layers and each convolution layer contains several convolution kernels for extracting abstract features of the image data. Compared with the other neural network models, CNN has been successfully applied to SAR-ATR due to its powerful feature extraction capability [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic Aperture Radar Image Recognition

et al. 2019

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Background / introduction: SAR image automatic target recognition technology (SAR-ATR) is one of the research hotspots in the field of image cognitive learning. Inspired by the human cognitive process, experts have designed convolutional neural networks (CNN) based methods and successfully applied the methods to SAR-ATR. However, the performance of CNNs significantly deteriorates when the labelled samples are insufficient. Methods: To effectively utilize the unlabelled samples, a semi-supervised CNN method is proposed in this paper. First, CNN is used to extract the features of the samples, and subsequently the class probabilities of the unlabelled samples are computed using the softmax function. To improve the effectiveness of the unlabelled samples, we remove possible noise performing thresholding on the class probabilities. Afterwards, based on the remaining class probabilities, the information contained in the unlabelled samples is integrated with the scatter matrices of the standard linear discriminant analysis (LDA) method. The loss function of CNN consists of a supervised component and an unsupervised component, where the supervised component is created using the cross-entropy function and the unsupervised component is created using the scatter matrices. The class probabilities are utilized to control the impact of the unlabelled samples in the training process, and the reliability of the unlabelled samples is further improved. Results: We choose ten types of targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The experimental results show that the recognition accuracy of 2 our method is significantly higher than that of the supervised CNN method. Conclusions: It proves that our method can effectively improve the SAR-ATR accuracy despite the deficiency of the labelled samples.

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Deep feature extraction and combination for synthetic aperture radar target classification

Cited by 68 publications

References 14 publications

Target Recognition of SAR Images Based on Complex Bidimensional Empirical Mode Decomposition

Target Recognition of SAR Images Based on Complex Bidimensional Empirical Mode Decomposition

The Recognition Framework of Deep Kernel Learning for Enclosed Remote Sensing Objects

A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic Aperture Radar Image Recognition

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