粒子群优化结构测量矩阵的遥感压缩成像

针对块循环测量矩阵应用于遥感压缩成像存在图像重构性能不理想的问题,本文把粒子群智能优化算法引入到块循环矩阵优化中,实现了在保持矩阵结构不变的同时对块循环矩阵的优化。首先以相关系数的Welch界为阈值约束Gram矩阵非对角元素构造目标矩阵;然后以Gram矩阵逼近目标矩阵的方式建立目标函数,将优化对象改为构造块循环矩阵的自由元向量。为提高优化效率,文中采用权重自适应更新的方式提高粒子搜索能力。开展了相关重构对比实验,结果表明,优化后的块循环测量矩阵在保持矩阵结构的同时,降低了与稀疏变换矩阵的相关性,其与稀疏变换矩阵的最大相关系数、平均相关系数和阈值平均相关系数分别降低了0.027 3、0.017 5和0.004 6,得到的结果显示优化的块循环矩阵提高了图像的重构性能。

Structured measurement matrix by particle swarm optimization for remote sensing compressive imaging

For non-ideal image construction performance of a block circulant matrix in remote sensing compressive imaging ,this paper introduces the particle swarm optimization intelligent algorithm into optimizing the block circulant matrix ,meanwhile maintaining the matrix structure .Firstly ,the Welch bound of a correlation coefficient is taken as a threshold value to restrain the off -diagonal entries of the Gram matrix and to build a target matrix .Then ,the objective function is established by making the Gram matrix approach the target matrix ,and the optimized variable is replaced as the free entries to compose the block circulant matrix .To improve the optimized efficiency ,the weight adaptive update is used to improve the partical search capacity .A construction comparison experiment is carried out , the results show that the correlation properties of the block circulant matrix with the sparse transform matrix has been reduced while maintaining the matrix structure , and the coefficients for maximum correlation ,average correction and threshold average correction have been reduced by 0 .027 3 ,0 .017 5 and 0 .004 6 , respectively . These results show the image construction performance is improved by optimized block circulant matrix .