Joint Embedding and Classification for SAR Target Recognition

by   Jiayun Wang, et al.

Deep learning can be an effective and efficient means to automatically detect and classify targets in synthetic aperture radar (SAR) images, but it is critical for trained neural networks to be robust to variations that exist between training and test environments. The layers in a neural network can be understood to be successive transformations of an input image into embedded feature representations and ultimately into a semantic class label. To address the overfitting problem in SAR target classification, we train neural networks to optimize the spatial clustering of points in the embedded space in addition to optimizing the final classification score. We demonstrate that networks trained with this dual embedding and classification loss outperform networks with only a classification loss. We study placing the embedding loss after different network layers and and found that applying the embedding loss on the classification space results in the best the SAR classification performance. Finally, our visualization of the network's ten-dimensional classification space supports our claim that the embedding loss encourages a better embedding, namely greater separation between target class clusters for both training and testing partitions of the MSTAR dataset.


page 1

page 6

page 8


Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

Along with the improvement of radar technologies, Automatic Target Recog...

A Dual-Polarization Information Guided Network for SAR Ship Classification

How to fully utilize polarization to enhance synthetic aperture radar (S...

New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis

Synthetic Aperture Radar (SAR) images are prone to be contaminated by no...

Graph-based Active Learning for Semi-supervised Classification of SAR Data

We present a novel method for classification of Synthetic Aperture Radar...

Large-scale detection and categorization of oil spills from SAR images with deep learning

We propose a deep learning framework to detect and categorize oil spills...

Rollable Latent Space for SAR Target Recognition of Un-seen Views

This paper proposes rollable latent space (RLS) for synthetic aperture r...