ABSTRACT

Synthetic Aperture Radar systems are capable of acquiring high-resolution images by modulating and transmitting waves in high frequency levels towards the ground. Synthetic Aperture Radar (SAR) images are generated by demodulating and processing the backscattered waves by the targets. New SAR systems produce high-resolution images by raising the modulation frequency. The sampling time becomes shorter and a large number of samples are generated, which costs for processing and storage. Compressed Sensing (CS) is a compression technique capable of reconstructing a sparse signal from a small number of samples inferior to the conventional sampling. The representation basis, that projects a signal to a sparse form is the key of the application of CS. CS becomes unusable in the acquisition of heterogeneous scenes that present many scatterings types because the representation matrices are unknown and require intensive computing to generate them.

In the literature, most proposed researches in the CS field apply CS on simulated sparse scenes, where only a few number of strong scatters are present to avoid the representation matrix computing. The purpose of this paper is to apply CS on non-sparse images and to avoid heavy computation to generate a representation matrix at the same time by combining the CS with the Gaussian Process regression (GPR). A small number of backscattered signals are acquired and sampled with respect to the Nyquist theory, sparsified and used as a feature vector for the training of the GPR model. The remaining signals are processed by the CS theory. After reconstruction, the zero values of the image are predicted using the model generated by the GPR algorithm. Five strategies are proposed in this paper and several evaluations are performed. These methods reconstruct an exploitable image from $40\mathrm{\%}$ of samples.

摘要

合成孔径雷达系统能够通过调制和向地面发射高频波来获取高分辨率图像。合成孔径雷达 (SAR) 图像是通过对目标的反向散射波进行解调和处理来生成的。新的 SAR 系统通过提高调制频率来产生高分辨率图像。采样时间变短，产生大量样本，处理和存储成本高。压缩感知 (CS) 是一种压缩技术，能够从劣于传统采样的少量样本中重建稀疏信号。将信号投影为稀疏形式的表示基础是CS应用的关键。

在文献中，大多数 CS 领域提出的研究将 CS 应用于模拟的稀疏场景，其中仅存在少量强散射以避免表示矩阵计算。本文的目的是将 CS 应用到非稀疏图像上，并通过将 CS 与高斯过程回归 (GPR) 相结合来避免同时生成表示矩阵的繁重计算。根据奈奎斯特理论获取和采样少量反向散射信号，将其稀疏化并用作 GPR 模型训练的特征向量。其余信号由 CS 理论处理。重建后，使用 GPR 算法生成的模型预测图像的零值。本文提出了五种策略，并进行了多次评估。$40\mathrm{\%}$ 的样本。