Application of sparse prior in aperture synthesis radiometric imaging of extended radiation source

Abstract

Aimed at the extended source of earth thermal radiation scene, the sparse prior is extracted from the transform domain, and used in the statistical inversion approach (SIA) to deal with the inverse problem in aperture synthesis radiometric imaging of the extended source. As the transform basis, Laplace basis, Fourier basis and Daubechies wavelet basis are proposed to explore the implicit sparse prior about the extended source. For the SIA, the image inversion of aperture synthesis radiometers is recast as the statistical inference about the hyperparameters based the sparse prior in the transform domain, which can be automatically derived from an expectation maximization (EM) algorithm. The simulations show that the proposed SIA can improve the radiometric accuracy of the reconstructed image by introducing the sparse prior as compared to the traditional deterministic inversion approaches.