  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.
