Fusing local texture description of saliency map and enhanced global statistics for ship scene detection

In this paper, we introduce a new feature representation based on fusing local texture description of saliency map and enhanced global statistics for ship scene detection in very high-resolution remote sensing images in inland, coastal, and oceanic regions. First, two low computational complexity methods are adopted. Specifically, the Itti attention model is used to extract saliency map, from which local texture histograms are extracted by LBP with uniform pattern. Meanwhile, Gabor filters with multi-scale and multi-orientation are convolved with the input image to extract Gist, means and variances which are used to form the enhanced global statistics. Second, sliding window-based detection is applied to obtain local image patches and extract the fusion of local and global features. SVM with RBF kernel is then used for training and classification. Such detection manner could remove coastal and oceanic regions effectively. Moreover, the ship scene region of interest can be detected accurately. Experiments on 20 very high-resolution remote sensing images collected by Google Earth shows that the fusion feature has advantages than LBP, Saliency map-based LBP and Gist, respectively. Furthermore, desirable results can be obtained in the ship scene detection.