IEEE Access (Jan 2019)
Fast DDL Classification for SAR Images With <inline-formula> <tex-math notation="LaTeX">${l}_{1,\infty}$ </tex-math></inline-formula> Constraint
Abstract
Synthetic aperture radar (SAR) image classification aims at labeling pixels with different categories and this is both, a fundamental step for automatic target recognition (ATR) and a prerequisite for further interpretation. In the past decades, various methods have been proposed for the classification of SAR targets and among them are discriminative dictionary learning (DDL) methods. These DDL methods have recently gained attention from researchers' community due to the fact that they are very powerful on both, representation and discrimination during the classification process of SAR images. However, most of the existing DDL methods adopt l0-norm or l1-norm to ensure the sparsity, but in general, these DDL methods suffer from a high computational burden. Furthermore, it is important to minimize the execution time in the phase of online testing for the scenario of onboard real-time or near real-time SAR automatic target recognition such as modern unmanned aerial vehicle SAR platforms. That said, on reducing execution time, we are confronted with the problem of enhancing recognition efficiency while maintaining its accuracy. In order to solve this problem, our paper proposes a fast DDL method (named as FaDDL) based on a nonlinear analysis co-sparse model by adopting an l1,∞-norm ball as a constraint to replace l0-norm or l1-norm on the coding coefficient matrix. The experimental results show that our proposed method significantly reduces execution time, without losing the classification accuracy.
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