Automatic target recognition based on simultaneous sparse representation

In this paper, an automatic target recognition algorithm is presented based on a framework for learning dictionaries for simultaneous sparse signal representation and feature extraction. The dictionary learning algorithm is based on class supervised simultaneous orthogonal matching pursuit while a matching pursuit-based similarity measure is used for classification. We show how the proposed framework can be helpful for efficient utilization of data, with the possibility of developing real-time, robust target classification. We verify the efficacy of the proposed algorithm using confusion matrices on the well known Comanche forward-looking infrared data set consisting of ten different military targets at different orientations.

• Type: Journal
• Year: 2010
• Where: ICIP
• Authors: Vishal M. Patel, Nasser M. Nasrabadi, Rama Chellappa
• Keywords: Automatic Target Recognition, Dictionary Learning Algorithm, ICIP 2010, Image Processing, Simultaneous Sparse Signal