Liu B, Yu X, Zhang P, Tan X, Yu A, Xue Z. A new algorithm of SAR image target recognition based on improved deep convolutional neural network. Gao F, Huang T, Sun J, Wang J, Hussain A, Yang E. International Conference on Data Science and Advanced Analytics IEEE 2015 pp 541–7 SAR target recognition based on deep learning. Geoscience and Remote Sensing Symposium IEEE. Convolutional neural network for SAR image classification at patch level. Deep feature extraction and combination for synthetic aperture radar target classification. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. Clustering-oriented multiple convolutional neural networks for single image super-resolution. A hierarchical predictive coding model of object recognition in natural images. Biologically inspired progressive enhancement target detection from heavy cluttered SAR images. Gao F, Ma F, Zhang Y, Wang J, Sun J, Yang E, et al. Visual saliency modeling for river detection in high-resolution SAR imagery. Gao F, Ma F, Wang J, Sun J, Yang E, Zhou H. Fast image recognition based on independent component analysis and extreme learning machine. Zhang S, He B, Rui N, Wang J, Han B, Lendasse A. Recruitment and consolidation of cell assemblies for words by way of hebbian learning and competition in a multi-layer neural network. Garagnani M, Wennekers T, Pulvermüller F. Multiple Mode SAR Raw Data Simulation and Parallel Acceleration for Gaofen-3 Mission. Zhang F, Yao X, Tang H,Yin Q, Hu Y, Lei B. Slim and efficient neural network design for resource-constrained SAR target recognition. Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF. Multiple model particle filter track-before-detect for range ambiguous radar. A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images. Gao F,Yang Y, Wang J, Sun J, Yang E, Zhou H. It has been proved that our method can effectively improve the SAR ATR accuracy when labeled samples are insufficient. The experimental results show that the recognition accuracy of our method is significantly higher than other semi-supervised methods. We choose ten types of targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Finally, the loss function of CNN is modified by the scatter matrices. Afterward, the optimized class probabilities are used to calculate the scatter matrices of the linear discriminant analysis (LDA) method. Thresholding processing is performed to optimize the class probabilities so that the reliability of the unlabeled samples is improved. Specifically, we first utilize CNN to obtain the class probabilities of the unlabeled samples. In the training process of our method, the information contained in the unlabeled samples is integrated into the loss function of CNN. To effectively utilize the unlabeled samples, we present a novel semi-supervised CNN method. However, the performance of CNN significantly deteriorates when the labeled samples are insufficient. Inspired by the human cognitive process, experts have designed convolutional neural network (CNN)-based SAR ATR methods. Synthetic aperture radar (SAR) automatic target recognition (ATR) technology is one of the research hotspots in the field of image cognitive learning.
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