Phase unwrapping (PU) is one of the key processes in reconstructing the digital elevation model of a scene from its interferometric synthetic aperture radar (InSAR) data. It is known that two-dimensional (2-D) PU problems can be formulated as maximum a posteriori estimation of Markov random fields (MRFs). However, considering that the traditional MRF algorithm is usually defined on a rectangular grid, it fails easily if large parts of the wrapped data are dominated by noise caused by large low-coherence area or rapid-topography variation. A PU solution based on sparse MRF is presented to extend the traditional MRF algorithm to deal with sparse data, which allows the unwrapping of InSAR data dominated by high phase noise. To speed up the graph cuts algorithm for sparse MRF, we designed dual elementary graphs and merged them to obtain the Delaunay triangle graph, which is used to minimize the energy function efficiently. The experiments on simulated and real data, compared with other existing algorithms, both confirm the effectiveness of the proposed MRF approach, which suffers less from decorrelation effects caused by large low-coherence area or rapid-topography variation.
This paper presents a novel method for automatic image registration. It represents image as triangular mesh and use triangle as feature primitive. First, it detects corner features and triangulates them into triangular mesh. Then, orrespondences of triangles from different images are established by evaluating the similarity of the triangular regions. Affine rectification is applied to establish pixel correspondences. Based on the triangle correspondences, the image transformation is estimated using RANSAC estimator. The proposed method is applied to various image pairs related by projective transformation, experimental results show that the method works successfully even under the case that there are large rotation or severe perspective deformation effect between the images.