Classification of remotely compressively sensed data

We propose a method for classifying SAR unfocussed images by utilising compressed sensing as a universal dimensionality reduction. This method benefits from the smearness of unfocussed SAR data to simplify the classification system development. We will show that 25% of the measurements were enough to achieve a classification accuracy comparable with that of the best learning based method, i.e PCA.