A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF

This paper presents a novel computational model to detect airports in optical remote sensing images (RSI). It works in a hierarchical architecture with a coarse layer and a fine layer. At the coarse layer, a target-oriented saliency model is built by combing the cues of contrast and line density to rapidly localize the airport candidate areas. Furthermore, at the fine layer, a learned condition random field (CRF) model is applied to each candidate area to perform the fine detection of the airport target. The CRF model is learned based on sparse features of local patches in a multi-scale structure and it also takes the contextual information of target into consideration. Therefore, its detection is more accurate and is robust to target scale variation. Comprehensive evaluations on RSI database from the Google Earth and comparisons with state-of-the-art approaches demonstrate the effectiveness of the proposed model.