SAR Target Recognition via Meta-Learning and Amortized Variational Inference.

Ke Wang,Gong Zhang

doi:10.3390/s20205966

Abstract

The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data.

Highlights

Synthetic aperture radar (SAR) is an active remote sensor with all day and night, high-resolution, and wide-area imaging capabilities
We focus on the third stage, that is, high-level classification
The contributions of this paper are summarized as follows: (1) We propose a novel recognition model integrating meta-learning and amortized variational inference (AVI)

Summary

Introduction

Synthetic aperture radar (SAR) is an active remote sensor with all day and night, high-resolution, and wide-area imaging capabilities. Because of these unique capabilities, SAR is widely used in geoscience and remote sensing. Numerous SAR sensors are operating on spaceborne and airborne platforms and are imaging ground targets for surveillance and reconnaissance. For efficient interpretation of SAR image data, SAR automatic target recognition (SAR-ATR) system are being developed. SAR-ATR aims to detect and recognize targets, such as trucks and armored personnel carriers, in SAR images. The workflow of an end-to-end SAR-ATR system includes three stages: detection, low-level classification, and high-level classification [1]. The clutter is analyzed and filtered out in the low-level classification

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