CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available

Bu, Hui; Deng, Jiarui; Yang, Tianwen; Wang, Jian

doi:10.1109/jstars.2021.3093645

Cited by 24 publications

(12 citation statements)

References 43 publications

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“…The reviewed techniques used for identification of synthetic images have limitations, some of which include, 1. The current techniques used for identifying synthetic images are not always effective in distinguishing between real and synthetic images [1][2][3]. Some of the techniques are based on statistical analysis of pixel-level features, which can be easily manipulated by sophisticated generators [4].…”

Section: Issues With Existing Techniquesmentioning

confidence: 99%

“…Similar models [2][3][4] along with their functional nuances, application-specific advantages, contextual limitations, and deployment-specific future scopes are discussed in the next section of this text. Based on this discussion, it can be observed that existing models for synthetic image classification are either extremely sophisticated or incapable of scaling to multi-domain image sets.…”

Section: Introductionmentioning

confidence: 99%

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Design of a Hybrid GWO CNN Model for Identification of Synthetic Images via Transfer Learning Process

2023

IJIES

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Visual representation of synthetic images is very accurate, due to which it is difficult to differentiate them for their natural counterparts. Existing models that perform this differentiation are either very complex, or cannot be scaled for multidomain image sets. Moreover, the accuracy of these models depends directly upon type of dataset & feature sets used for training & validation purposes. To overcome these limitations, this paper proposes design of a Hybrid GWO CNN Model for identification of Synthetic Images for big data Applications. The proposed model initially extracts multidomain feature sets from input images, that includes wavelet, cosine, fourier & convolutional features. These features are processed via a grey wolf optimization (GWO) Model, that assists in improving inter-class feature variance while minimizing intra-class variance levels. The GWO Model identifies training & validation sets, thereby assisting the classification model to accurately differentiate between different image types. To perform this task, a variance-based fitness function was modelled that covers both inter-class & inter-class variance levels. This classification is performed via use of a transfer learning-based CNN Model, that extends VGG-19 for high-efficiency operations. The proposed model was tested on a large number of datasets including Synthetic Fruit, Unsplash, ESPL Synthetic Image, and Okazaki Synthetic Texture Image (OSTI) databases. Based on these datasets, accuracy, precision, computational delay, recall & AUC (Area Under the Curve) metrics were evaluated & compared with existing models. It was observed that the proposed model showcased 9.5% better accuracy, 8.3% higher precision, 6.5% better recall, and 3.9% faster performance when compared with existing models. Due to such high performance under different datasets, the proposed model is useful for large-scale synthetic-image identification use cases.

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Section: Issues With Existing Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of a Hybrid GWO CNN Model for Identification of Synthetic Images via Transfer Learning Process

2023

IJIES

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“…In other words, the CAMP algorithm could achieve CFAR detection of sparse SAR images. Recently, Bi et al [33] used sparse SAR images as input data under a CNN architecture. This approach achieved a higher target classification performance than that obtained with the same network structure using MF SAR images as input.…”

Section: Introductionmentioning

confidence: 99%

Saliency-Based SAR Target Detection via Convolutional Sparse Feature Enhancement and Bayesian Inference

Luo

Wang

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Traditional synthetic aperture radar (SAR) target detection methods use matched filtered SAR images as input, and the detection performance is restricted due to the high sidelobes and speckle noise of these images. Sparse SAR imaging methods developed in recent years provide the advantages of reducing sidelobes, noise, and clutter. The imaging results obtained with these methods could help improve the SAR target detection performance. In this article, to improve the target detection performance using sparse SAR images as input, we proposed a convolutional sparse feature enhancement method to meet the needs of Bayesian saliency detection. The proposed Bayesian saliency joint target detection method comprised the following three steps: first, to obtain sparse SAR images with continuous contours and fewer holes in the target area, we proposed a convolutional L 1 sparse regularization method. Second, a regularization parameter optimization method was derived to quickly obtain optimal regularization parameters for saliency detection. Finally, target detection results were obtained through a superpixel-based Bayesian saliency joint detector. Extensive experiments verified that the proposed method could improve the SAR target detection accuracy in complex backgrounds.

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“…In this case, high-resolution and wide-swath imaging means that the complexity of the SAR system will increase dramatically. The proposal of the sparse SAR imaging technique has provided a new solution to this problem and has gradually become the main direction of SAR imaging [23]- [26]. Compared with matched filtering (MF)-based result, a sparse SAR image shows better performance with lower sidelobes and higher signal-to-noise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

Amplitude-Phase CNN-Based SAR Target Classification via Complex-Valued Sparse Image

Deng

Zhang

et al. 2022

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

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It is known that a synthetic aperture radar (SAR) image obtained by matched filtering (MF)-based algorithms always suffers from serious noise, sidelobes, and clutters. However, the improvement of the image quality means the complexity of the SAR system will increase, which affects the application of the SAR image. The introduction of the sparse signal processing technique into SAR imaging proposes a new way to solve this problem. Sparse SAR image obtained by sparse recovery algorithms shows a better image performance than the typical complex SAR image with lower sidelobes and higher signal-to-noise ratio. As the most widely applied field of the SAR image, target classification relies on the SAR image with high quality. Therefore, a novel target classification model based on the amplitude and phase information of the sparse SAR image is introduced in this article. First, complex sparse image dataset is constructed by a novel iterative soft thresholding (BiIST) algorithm. Compared with typical regularization-based sparse recovery algorithms, BiIST not only can improve the quality of recovered image, but also can obtain a nonsparse solution with retaining phase information and background statistical distribution of the SAR image. Then, targets are classified by the proposed amplitude-phase convolutional neural network (AP-CNN). Typical SAR target classification networks imitate those on optical image, just using amplitude data. However, considering the particularity of the SAR image, the AP-CNN uses both amplitude and phase for training, which theoretically improves the classification accuracy. Experimental results show that the AP-CNN outperforms the typical amplitude-based CNN in target classification, both under standard operating conditions (SOCs) and extended operating conditions (EOCs). Results under SOC demonstrate that the AP-CNN improves the classification accuracy by 11.46% with only 1000 training samples. Even under EOC, the accuracy gap between the Manuscript

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CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available

Cited by 24 publications

References 43 publications

Design of a Hybrid GWO CNN Model for Identification of Synthetic Images via Transfer Learning Process

Design of a Hybrid GWO CNN Model for Identification of Synthetic Images via Transfer Learning Process

Saliency-Based SAR Target Detection via Convolutional Sparse Feature Enhancement and Bayesian Inference

Amplitude-Phase CNN-Based SAR Target Classification via Complex-Valued Sparse Image

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