Abstract:

Inverse synthetic aperture radar (ISAR) images, different from the optical images and consisting of many sparse scatterers, usually vary with different view angles and suffer jamming and shelter, which make ISAR targets classification difficult. An available recognition method based on geometric hash is presented. The approach extracts key points which reflect the structure of ISAR targets at first, then constructs affine coordinates and obtains the affine invariance features to solve the variation problem of translation, rotation and scale. Finally, geometric hash is applied as the final classifier, which performs excellently in anti-jamming and local recognition, to solve the problem of location and intensity variation of scatterers due to pose alternation, jamming and shelter. Experimental results show that the proposed method is quite effective in distinguishing targets with different structures and performs well in local recognition.