Airplane Recognition in Terra SAR-X Images via Scatter Cluster Extraction and Reweighted Sparse Representation

Airplane Recognition in Terra SAR-X Images via Scatter Cluster Extraction and Reweighted Sparse Representation

 Abstract:

Target recognition in synthetic aperture radar (SAR) images has become a hotspot in recent years. The backscattering characteristic of target is a significant issue taken into consideration in SAR applications. Almost all of the previous work focus on the scatter point extraction to depict the backscattering characteristic of the target; however, a point-target corresponds to a region rather than a single point due to the convolution during the imaging. Based on this fact, we first analyze the extent to how a point-target spreads, then propose a novel scatter cluster extraction (SCE) method, and utilize the scatter cluster as the feature to solve the airplane recognition problem in SAR images. In practice, there often exist interfering objects near the target to be classified. To overcome this issue, we design a reweighted sparse representation (RSR)-based automatic purifying method by assigning a weight to each element of the feature iteratively according to the representation error. Since the element with large representation error always corresponds to the interfering objects, we give it a small weight, consequently suppressing the influence of the interference. Experimental results demonstrate that the proposed SCE method outperforms the traditional scatter point extraction-based method as well as some state-of-the-art methods. The comparison result also validates the effectiveness of the proposed RSR method.

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