Scene Segmentation of Multi-Band ISAR Fusion Imaging Based on MB-PCSBL

Abstract

We consider the problem of achieving multi-band inverse synthetic aperture radar (ISAR) fusion imaging of block structure targets with unknown block partition and develop a block-sparse recovering method based on matrix block pattern-coupled sparse Bayesian learning algorithm. Based on the sparse representation of multi-band ISAR fusion imaging model, a pattern-coupled hierarchical Gaussian prior is proposed to characterize the pattern relevance of scattering coefficients. The sparsity of each coefficient is controlled not only by its own hyperparameter, but also by the hyperparameters corresponding to its eight neighboring coefficients in the data matrix. The correlations between the coefficients in rows and columns are determined by different parameters, respectively. The proposed prior model can increase the model flexibility and promote the generation of block structures. Moreover, the whole observation scene is segmented into multiple sub-scenes to reduce the memory storage space and the computational complexity. Parameters and the fusion image result of each sub-scene are derived by the expectation-maximization method. The multi-band ISAR fusion image result of the whole scene is obtained through the stitching of the sub-scenes imaging results. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm.