Abstract
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.
Highlights
In recent years, Synthetic aperture radar (SAR) image automatic target recognition technology (SARATR) has been widely used [1] and has formed a fixed three-level flow process: detection, identification, and classification [2]
In order to evaluate the effectiveness of the proposed SAR image target recognition framework, the experimental part of this paper mainly uses the proposed recognition system to recognize the synthetic SAR image of the target scene to be detected and analyzes the experimental results
Due to the high cost of acquiring scene images with a large number of targets, only SAR image slices of 10 types of targets and some background images of the same area are provided in the MSTAR data set
Summary
SAR image automatic target recognition technology (SARATR) has been widely used [1] and has formed a fixed three-level flow process: detection, identification, and classification [2]. For the classification and recognition task of natural images, researchers have proposed some very typical deep learning network models, such as AlexNet [6], VGG [7], Googlenet [8], ResNet [9], and DenseNet [10]. Wang et al [12] studied the influence of coherent speckles in SAR images on CNN for SAR target recognition On this basis, they proposed a bipolar coupled CNN structure. They proposed a bipolar coupled CNN structure They firstly used the denoising subnetwork to denoise and learned the residual speckle characteristics and target information through the classification subnetwork. A novel model framework based on deep learning is proposed to study the automatic target recognition technology of SAR image. L is the number of sights; I is the intensity of coherent speckle noise
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