<i>L</i> _1/2 -Norm Regularization Outlier Removal and Moving Least Squares-Based TomoSAR Point Cloud Optimization of Mountain Areas

Tomographic synthetic aperture radar (TomoSAR) enables high-resolution 3-D reconstruction of mountainous terrain, but TomoSAR point clouds often suffer from outliers and height errors due to estimation noise and geometric distortions. In this letter, we propose a framework combining <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub>-norm outlier detection and moving least squares (MLS) interpolation. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub>-norm effectively identifies sparse anomalies, while MLS reconstructs continuous terrain with preserved features. Compared with L1-norm regularization based outlier detection technology, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub>-norm method can obtain sparser solution, achieving an order-of-magnitude improvement in height reconstruction accuracy, and hence shows superior performance in 3-D imaging of mountain areas. Experimental results based on simulated and real Fucheng-1 SAR dataset verify the proposed method.