Automatic detection and classification of craters from image data acquired by lunar orbiting spacecraft using multiple approaches

There are large volumes of lunar images that are archived and to be archived by missions such as Clementine and SELENE (SELenological and Engineering Explorer). We have developed an algorithm to detect and classify automatically craters in lunar images. Craters are of scientific interest because the density of craters can yield the relative ages of the surface units. However, the manual extraction of craters remains difficult because it requires a great deal of man power. Several automated craters detection methods have been developed so for but none are yet practical nor have been sufficiently tested. Our algorithm locates craters using four different approaches. These are extraction of: (1) the shade and sunny pattern of crater caused by low sun elevation, (2) the circular features from the edge image, (3) the circular lines from thinned and bonded edge lines, and (4) discrete or separated circular edge lines using fuzzy Hough transform. We applied the proposed algorithm to Clementine and Apollo images acquired for both mare and highland under different solar elevation. As a result it is shown that the algorithm can detect craters at 80% detection rate with same parameter values. As for to Clementine images, we classified craters detected by our algorithm using spectral information derived from Clementine UVVIS multispectral images. Finally we propose the crater GIS including the geological and spectral attributes automatically generated by our algorithm described above.