Tomographic synthetic aperture radar (TomoSAR) imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction. In these years, for the sparse elevation distribution, compressive sensing (CS) is a developed favorable technique for the high-resolution elevation reconstruction in TomoSAR by solving an Li regularization problem. However, because the elevation distribution in the forested area is nonsparse, if we want to use CS in the recovery, some basis, such as wavelet, should be exploited in the sparse representation of the elevation reflectivity function. This paper presents a novel wavelet-based L 1 / 2 regularization CS-TomoSAR imaging method of the forested area. In the proposed method, we first construct a wavelet basis, which can sparsely represent the elevation reflectivity function of the forested area, and then reconstruct the elevation distribution by using the L 1 / 2 regularization technique. Compared to the wavelet-based L 1 regularization TomoSAR imaging, the proposed method can improve the elevation recovered quality efficiently.

中文翻译：

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基于小波的L½ 森林地区CS-TomoSAR成像的正则化

断层成像合成孔径雷达（TomoSAR）成像利用在不同仰角孔径处进行的天线阵列测量来恢复沿仰角方向的反射率函数。近年来，对于稀疏的高程分布，压缩感测（CS）是通过解决Li正则化问题而在TomoSAR中进行高分辨率高程重构的一项先进技术。但是，由于林区的高程分布不稀疏，因此如果要在恢复中使用CS，则在高程反射率函数的稀疏表示中应利用一些基础，例如小波。本文提出了一种新颖的基于小波的L 1个 / 2森林区域的正则化CS-TomoSAR成像方法。在提出的方法中，我们首先构造一个小波基，可以稀疏地表示林区的高程反射率函数，然后使用L重建高程分布。 1个 / 2正则化技术。与基于小波的L 1个 正则化TomoSAR成像，该方法可以有效提高高程恢复质量。