Target Recognition of SAR Image Based on Improved Convolutional Auto-Encoding Network

Wu, XiangYong; Jiang, Yicheng; Zheng, Ling; Kuang, Hao

doi:10.1109/apsar46974.2019.9048531

Cited by 2 publications

(1 citation statement)

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“…Among them, L is the number of sights; I is the intensity of coherent speckle noise. 3 Wireless Communications and Mobile Computing deleted [24]. As the capability of feature extraction is enhanced with the deepening of network layers, when deep CAE network is applied to image tasks, the layers of encoder and decoder are usually deepened at the same time to extract, encode, and decode input features.…”

Section: Sar Image Target Recognition Methodsmentioning

confidence: 99%

A Novel SAR Image Target Recognition Algorithm under Big Data Analysis

Chen

Wang

Chen

et al. 2021

Wireless Communications and Mobile Computing

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Synthetic aperture radar (SAR) image target recognition technology is aimed at automatically determining the presence or absence of target information from the input SAR image and improving the efficiency and accuracy of SAR image interpretation. Based on big data analysis, dirty data is removed, clean data is returned, and standardized processing of SAR image data is realized. At the same time, by establishing a statistical model of coherent speckles, the convolutional autoencoder is used to denoise the SAR image. Finally, the network model modified by softmax cross-entropy loss and Fisher loss is used for automatic target recognition. Based on the MSTAR data set, two scene graphs containing the target synthesized by the background image and the target slice are used for experiments. Several comparative experiments have verified the effectiveness of the classification and recognition model in this paper.

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Section: Sar Image Target Recognition Methodsmentioning

confidence: 99%