SAR Image Classification Using CNN Embeddings and Metric Learning

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Li, Yibing, Li, Xiang, Sun, Qian, Dong, Qianhui
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Classification; Convolutional neural network (CNN); Datasets; Feature extraction; Image classification; Marine vehicles; Measurement; metric learning; Neural networks; Prototypes; Radar imaging; SAR (radar); Synthetic aperture radar; synthetic aperture radar (SAR); Task analysis; Training
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2020.3022435

The method proposed in this letter for synthetic aperture radar (SAR) image classification has two main stages. In the first stage, a convolutional neural network (CNN) is trained for normal SAR image classification task. After training, the sample features can be obtained by extracting the output of middle layer in the forward propagation process of CNN. In the second stage, an end-to-end metric network is trained to measure the relations between sample features. The method proposed in this letter is tested with some of the larger targets in OpenSARShip data set which is collected from Sentinel-1 satellite, and it is also tested with the MSTAR data set which is created by the U.S. Air Force Laboratory. The experimental results show that our method can get a higher recognition accuracy than normal CNN structure.