Morphological component analysis in SAR images to improve the generalization of ATR systems

Morphological Component Analysis is a technique to separate morphological different components from an image or signal. Morphological difference is in this case measured by the dictionary incoherence of the corresponding components. We propose to use a local discrete cosine transform to represent the periodic ground clutter and an undecimated wavelet transform to represent the piecewise smooth target. The parameters of the algorithm, like the total variation constraint, are determined automatically dependent on the contrast of the images. The decomposition is demonstrated with the spotlight SAR image chips of the MSTAR database and the found target images are used as input for a classification system to show the benefit of an increased generalization capability. As classifier we use the recently proposed combination of a convolutional neural network and support vector machines. Results are shown for forced decision classification as well as with rejection class.