An Orthogonal Subspace Decomposition-based Large Scale SAR Super-Resolution Process Using Relative Optimal Subspace

SAR image super resolution algorithms have been shown to significantly improve performance in many applications. For these memory-intensive algorithms, large-scale images must be divided into overlapped blocks and finally concatenated back to the final result. Orthogonal subspace decomposition method has better performance, but needs to manually choose the size of noise subspace according to the noise levels. Unsuitabe size of noise subspace results in the block effect. In this work, a relative optimal noise subspace determination algorithm is proposed. A Laplace operator is applied to get the size of the relative optimal noise subspace. Considering large-scale SAR images, a chip-merging approach is designed to eliminate the blocking effect with the relative optimal size of noise subspace. Experiment shows that this algorithm eliminates the blocking effect. This work can also be applied to other methods that need the parameter selection based on data.