On-board selection of relevant images: An application to linear feature recognition

In this paper, we propose an on-board selection scheme for aerial and space images, based on linear feature detection in a feature hyperspace. The det ection task is performed by means of the Radon transform (RT) and the wavel et transform; a fast algorithm for the RT computation is described, and cou nteractions against the discretization errors are proposed. A new, wavelet- based algorithm is introduced, which performs a fine analysis of the wavefo rms of the RT peaks, yielding a possibly error-free detection in images cor rupted by a high level of noise. A technique, based on the feature hyperspa ce, is proposed, able to significantly exploit all the available pieces of information on these peaks. Results of the tests on synthetic and real imag es are reported, which show that this method achieves satisfactory results, making the detection task highly reliable in the presence of both noise an d clutter.