Object localization based on sparse representation for remote sensing imagery

In this paper, we propose a new object localization method named sparse representation based object localization (SROL), which is based on the generalized Hough-transform-based approach using sparse representations for parts detection. The proposed method was applied to car and ship detection in remote sensing images and its performance was compared to those of state-of-the-art methods. Experimental results showed that the SROL algorithm can accurately localize categorical objects or a specific object using a small size of training data.