Recognition and identification of target images using feature based retrieval in UAV missions

With the introduction of unmanned air vehicles as force multipliers in the defense services worldwide, automatic recognition and identification of ground based targets has become an important area of research in the defense community. Due to inherent instabilities in smaller unmanned platforms, image blurredness and distortion need to be addressed for the successful recognition of the target. In this paper, an image enhancement technique that can improve images' quality acquired by an unmanned system is proposed. An image de-blurring technique based on blind de-convolution algorithm which adaptively enhances the edges of characters and wipes off blurredness effectively is proposed. A content-based image retrieval technique based on features extraction to generate an image description and a compact feature vector that represents the visual information, color, texture and shape is used with a minimum distance algorithm to effectively retrieve the plausible target images from a library of images stored in a target folder. This methodology was implemented for planning and gaming the UAV/UCAV missions in the Air Warfare Simulation System.