基于先验稀疏模型的光谱图像压缩采样检测

Spectral Images Compressive Sampling Detection Based on Prior Sparse Model

压缩信号处理为更加高效地实现光谱图像数据采集和处理提供了有效途径,针对传统压缩采样检测算法未针对待测信号专门设计采样矩阵,检测性能低于传统采样方式,且鲁棒性较弱的问题,提出了一种待测信号稀疏模型先验条件下的光谱图像压缩采样目标检测方法。该方法利用待测信号的稀疏表示子空间构造压缩采样矩阵,增强采样矩阵的信息获取能力;采用正交子空间投影法将压缩采样信号投影到干扰信号的局部正交子空间,抑制背景光谱的影响。实验和分析结果表明:与传统压缩采样检测算法相比,该方法能够有效提升压缩采样检测算法的性能,削弱采样矩阵随机性对于检测性能的影响,增强压缩采样检测算法的鲁棒性。

The compressive signal processing provides an effective approach for efficient spectral image data collecting and processing.An improved compressive sampling detection method based on prior sparse model of signal is proposed to solve the problems of low detection rate and weak robustness in traditional compressive sampling detection algorithm.To enhance the capability of information collection,the sparse representation subspace of signal to be measured is used to design compressive sampling matrix.The orthogonal subspace projection method is used to project the compressive sampling signal into the local orthogonal subspace of the interfering signal for background suppression.The experimental and analytical results show that the method can effectively improve the performance of the compressive sampling detection compared with the traditional algorithm,and enhance the robustness for different sample matrix.