  Sparsity-regularized synthetic aperture radar (SAR) imaging framework has
shown its remarkable performance to generate a feature enhanced high resolution
image, in which a sparsity-inducing regularizer is involved by exploiting the
sparsity priors of some visual features in the underlying image. However, since
the simple prior of low level features are insufficient to describe different
semantic contents in the image, this type of regularizer will be incapable of
distinguishing between the target of interest and unconcerned background
clutters. As a consequence, the features belonging to the target and clutters
are simultaneously affected in the generated image without concerning their
underlying semantic labels. To address this problem, we propose a novel
semantic information guided framework for target oriented SAR image formation,
which aims at enhancing the interested target scatters while suppressing the
background clutters. Firstly, we develop a new semantics-specific regularizer
for image formation by exploiting the statistical properties of different
semantic categories in a target scene SAR image. In order to infer the semantic
label for each pixel in an unsupervised way, we moreover induce a novel
high-level prior-driven regularizer and some semantic causal rules from the
prior knowledge. Finally, our regularized framework for image formation is
further derived as a simple iteratively reweighted $\ell_1$ minimization
problem which can be conveniently solved by many off-the-shelf solvers.
Experimental results demonstrate the effectiveness and superiority of our
framework for SAR image formation in terms of target enhancement and clutters
suppression, compared with the state of the arts. Additionally, the proposed
framework opens a new direction of devoting some machine learning strategies to
image formation, which can benefit the subsequent decision making tasks.
