Target Oriented High Resolution SAR Image Formation via Semantic Information Guided Regularizations

PDF: Target Oriented High Resolution SAR Image Formation via Semantic Information Guided Regularizations

Biao Hou, Member, IEEE, Zaidao Wen, Licheng Jiao, Senior Member, IEEE, and Qian Wu

Abstract— 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 l1 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.

AUTOMATIC target recognition (ATR) is one of the most important decision making tasks for synthetic aperture radar (SAR), in which a high quality SAR image is required to provide some informative target features for recognition [1]. Therefore, the SAR platform operating in the spotlight mode is widely leveraged for ATR [1] since it can generate a target image with higher resolution by continuously illuminating the target scene from a series of viewing angles [2]. Convention- ally, forming a SAR image, namely SAR imaging, relies on in- verse Fourier transformation for the spotlight mode SAR, e.g., polar formatting algorithm and convolution back-projection algorithm [2], [3]. These approaches have been extensively leveraged for SAR image formation due to their simplicity and efficiency, but they still suffer from the following deficiencies

in terms of the ATR task. 1). They rely on a perfectly received and sampled data to form a high quality image, which makes them sensitive to various non ideal or noisy environments such as limitations in the sampled data as well as viewing angles. In these scenarios, the quality and resolution of the obtained image will be generally degraded. 2). The contents of the underlying scene image and the features of the target are not concerned in these imaging algorithms so that the generated image will not provide a positive contribution to improve the performance of ATR or other decision-making tasks, e.g. segmentation etc.

These deficiencies consequently raise an issue whether we could develop a SAR imaging framework that is driven by the following decision-making tasks [4]. More specifically, for example, most ATR algorithms will generally exploit some features of the target extracted from a high resolution SAR im- age, such as the scattering points configuration, target contour and shape [1], [5]. If the imaging algorithm can take these fea- tures into consideration and simultaneously provide a feature enhanced target image, the subsequent ATR will be easier. For this purpose, C ̧ etin and Karl [6] propose a promising feature enhanced SAR imaging framework which recasts the imaging procedure as solving a regularized linear inverse problem. This framework enables us to enhance some task-specific features via involving a variety of regularization functions [7]. In their framework, they adopt the lp, p ≤ 1 norm and the total variation (TV) regularizer [8] to respectively enhance the magnitude of those dominated scattering points and the boundaries or edges in the image by exploiting their sparsity priors in the underlying image. Accordingly, their framework will suppress the sidelobes and produce a point enhanced or a piecewise smooth region enhanced image. It has been further evaluated that such enhancement can improve target recognition performance as expectation [9], [10]. Additionally, they give an empirically conclusion that their regularization framework is also robust to the partially sampled data as well as observation noise [7], [9]. This conclusion can be also confirmed and coincident with the latter emerging theory called compressive sampling or compressed sensing [11]–[14], which theoretically demonstrates the overwhelming possibility of exactly recovering a sparse vector or a low rank matrix from its partially and randomly sampled entries1. Over past decade, this novel theory brings a new road to SAR or inverse

1More precisely, recovering a low rank matrix with its partially known entries is referred to as matrix completion problem.

SAR (ISAR) imaging by exploiting the priors of sparsity in an image for the sake of relieving the sampling burden and achieving an apparent improvement on image quality and resolution [15]–[22]. More details about these sparsity- driven SAR imaging algorithms can refer to the surveys and references therein [4], [23].


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