When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation

TL;DR

This paper introduces a multi-task deep learning framework for SAR ATR that simultaneously achieves accurate target recognition and precise segmentation, leveraging shared features for improved performance.

Contribution

The paper proposes a novel multi-task deep learning architecture with encoder-decoder structures for joint recognition and segmentation in SAR ATR, integrating deep learning theory into multi-task learning.

Findings

Outperforms existing methods in recognition accuracy

Achieves superior segmentation quality

Validated on MSTAR dataset

Abstract

With the recent advances of deep learning, automatic target recognition (ATR) of synthetic aperture radar (SAR) has achieved superior performance. By not being limited to the target category, the SAR ATR system could benefit from the simultaneous extraction of multifarious target attributes. In this paper, we propose a new multi-task learning approach for SAR ATR, which could obtain the accurate category and precise shape of the targets simultaneously. By introducing deep learning theory into multi-task learning, we first propose a novel multi-task deep learning framework with two main structures: encoder and decoder. The encoder is constructed to extract sufficient image features in different scales for the decoder, while the decoder is a tasks-specific structure which employs these extracted features adaptively and optimally to meet the different feature demands of the recognition and segmentation. Therefore, the proposed framework has the ability to achieve superior recognition and segmentation performance. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, experimental results show the superiority of the proposed framework in terms of recognition and segmentation.