Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery
IEICE Technical Report, vol.117, no.403, SANE2017-92, pp.35-40, Jan. 2018 The standard architecture of synthetic aperture radar (SAR) automatic target recognition (ATR) consists of three stages: detection, discrimination, and classification. In recent years, convolutional neural networks (CNNs) for...
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Zusammenfassung: | IEICE Technical Report, vol.117, no.403, SANE2017-92, pp.35-40,
Jan. 2018 The standard architecture of synthetic aperture radar (SAR) automatic target
recognition (ATR) consists of three stages: detection, discrimination, and
classification. In recent years, convolutional neural networks (CNNs) for SAR
ATR have been proposed, but most of them classify target classes from a target
chip extracted from SAR imagery, as a classification for the third stage of SAR
ATR. In this report, we propose a novel CNN for end-to-end ATR from SAR
imagery. The CNN named verification support network (VersNet) performs all
three stages of SAR ATR end-to-end. VersNet inputs a SAR image of arbitrary
sizes with multiple classes and multiple targets, and outputs a SAR ATR image
representing the position, class, and pose of each detected target. This report
describes the evaluation results of VersNet which trained to output scores of
all 12 classes: 10 target classes, a target front class, and a background
class, for each pixel using the moving and stationary target acquisition and
recognition (MSTAR) public dataset. |
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DOI: | 10.48550/arxiv.1801.08558 |