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The standard architecture of synthetic aperture radar (SAR) automatic targetrecognition (ATR) consists of three stages: detection, discrimination, andclassification. In recent years, convolutional neural networks (CNNs) for SARATR have been proposed, but most of them classify target classes from a targetchip extracted from SAR imagery, as a classification for the third stage of SARATR. In this report, we propose a novel CNN for end-to-end ATR from SARimagery. The CNN named verification support network (VersNet) performs allthree stages of SAR ATR end-to-end. VersNet inputs a SAR image of arbitrarysizes with multiple classes and multiple targets, and outputs a SAR ATR imagerepresenting the position, class, and pose of each detected target. This reportdescribes the evaluation results of VersNet which trained to output scores ofall 12 classes: 10 target classes, a target front class, and a backgroundclass, for each pixel using the moving and stationary target acquisition andrecognition (MSTAR) public dataset.

Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery