FGM-MLSD: A Fuzzy Region Competition and Gaussian Mixture Segment Model via Modified Line Segment Detector Model for Airport Object Saliency Detection in Remote Sensing Images

Nowadays, object saliency detection has attracted considerable attention due to the vivid description of images. The traditional saliency detection methods are faced with the challenges of sparse boundary, fractured contour and internal non-uniform density. That will result in losing texture and detailed information of images, which makes a bad contribution to the subsequent object detection. Therefore, we propose a new approach based on fuzzy region competition and a Gaussian mixture segment model via a modified line segment detector (FGM-MLSD) for airport saliency detection in remote sensing images. First, we adopt fuzzy region competition, combining a Gaussian mixture model to segment the input images and obtain the airport candidate regions. After segmentation, a modified line segment detector (LSD) is used for extracting line features, which enhances the connection between broken lines and greatly improves the detected line quality. Then we can acquire the saliency map of the airport region. At last, we fuse the above saliency maps with the binarization map obtained by the Otsu method, aiming to eliminate the false alarm. Finally, abundant experiments are conducted, and the testing results reveal that the neoteric method can clearly and accurately extract the airport region in the remote sensing images and effectively improve the accuracy of saliency detection.