Joint Supervised Dictionary and Classifier Learning for Multi-View SAR Image Classification

• Published in: IEEE access Vol. 7; pp. 165127 - 165142
• Main Authors: Ren, Haohao, Yu, Xuelian, Zou, Lin, Zhou, Yun, Wang, Xuegang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; automatic target recognition (ATR); Back propagation; Classification; Classifiers; Correlation; Data mining; Dictionaries; dictionary learning; Image classification; Machine learning; Object recognition; Radar imaging; Radar polarimetry; Representations; sparse representation based classification (SRC); Synthetic aperture radar; Synthetic aperture radar (SAR); Target acquisition; Target recognition; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2953366

A new multi-view sparse representation classification (SRC) algorithm based on joint supervised dictionary and classifier learning (MSRC-JSDC) is proposed for synthetic aperture radar (SAR) image classification. Unlike most existing sparse representation methods for SAR image classification, MSRC-JSDC learns a supervised sparse model from training samples by utilizing sample label information, rather than directly employs a predefined one. Moreover, a supervised classifier is jointly designed during dictionary learning, which can further promote the classification performance compared with unsupervised reconstruction based classifier. In the meantime, to enhance the representation capability of the sparse model, classification error is back propagated to the dictionary learning procedure to optimize dictionary atoms. In order to extract more recognition information from collected SAR images, a multi-view strategy is applied in testing stage. A new sparse constraint is introduced into multi-view sparse representation procedure so that both inner correlation and complementary information among multiple views can be extracted. This is helpful for alleviating the influence of SAR image's sensitivity on classification performance in such challenging scenarios as large depression variation and noise corruption. Extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) database demonstrate that the proposed method is more robust and performs better than some state-of-the-art approaches.