A new unsupervised classification method is proposed for polarimetric SAR images to keep the spatial coherence of pixels and edges of different kinds of targets simultaneously. We consider the label scale variability of images by combining Inhomogeneous Markov Random Field (MRF) and Bayes' theorem. After minimizing an energy function using an expansion algorithm based on Graph Cuts, we can obtain classification results that are discontinuity preserving. Using a NASA/JPL AIRSAR image, we demonstrate the effectiveness of the proposed method.

Near range microwave imaging systems have broad application prospects in the field of concealed weapon detection, biomedical imaging, nondestructive testing, etc. In this paper, the technique of optimized sparse antenna array is applied to near range microwave imaging, which can greatly reduce the complexity of imaging systems. In detail, the paper establishes three-dimensional sparse array imaging geometry and corresponding echo model, where the imaging geometry is formed by arranging optimized sparse antenna array in elevation, scanning in azimuth and transmitting broadband signals in range direction; and by analyzing the characteristics of near range imaging, that is, the maximum interval of transmitting and receiving elements is limited by the range from imaging system to targets, we propose the idea of piecewise sparse line array; secondly, by analyzing the convolution principle, we develop a method of arranging piecewise sparse array which can generate the same distribution of equivalent phase centers as filled antenna array; then, the paper deduces corresponding imaging algorithm; finally, the imaging geometry and corresponding algorithm proposed in this paper are investigated and verified via numerical simulations and near range imaging experiments.